Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues 9781584889748, 1584889748

Table of contents :
1584889748......Page 1
c9748_c000......Page 2
Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues......Page 4
Contents......Page 6
Preface......Page 17
References......Page 23
List of Contributors......Page 27
Chapter 1: Glucose: Physiological Norm and Pathology......Page 33
Table of Contents......Page 0
1.1.2 Terms and definitions......Page 34
1.2.1 Glucose transporters......Page 36
1.2.2 Pathways of glucose concentration change: glucose distribution and concentrations in human organism......Page 37
1.2.3 Regulation of glucose metabolism: main pathways and processes......Page 40
1.2.3.1 Free fatty acids role in glucose metabolism regulation......Page 41
1.2.4 Insulin: the key hormone of glucose metabolism......Page 42
1.2.4.1 Nonglycemical insulin activities......Page 44
1.2.5 Endothelium and glucose metabolism......Page 45
1.3.1 Diabetes mellitus: glucose — victim or culprit?......Page 46
1.3.1.2 Free fatty acids......Page 48
1.3.1.3 Diabetes mellitus complications and glucose......Page 49
1.3.2.1 Hyperglycemia and atherogenesis......Page 50
1.3.2.2 Haemostasis and rheology modifications and glucose......Page 52
1.4.1 Glucose level regulation system tests: clinical and experimental use......Page 53
1.4.3 Current state of the problem: unsolved questions......Page 54
1.5 Conclusion......Page 56
1.6 Glossary......Page 57
References......Page 58
Chapter 2: Commercial Biosensors for Diabetes......Page 73
2.1 Introduction......Page 74
2.2.3 Gestational diabetes......Page 75
2.2.4 Incidence - A major world problem......Page 76
2.2.5 Treatments......Page 77
2.3.2 Blood glucose monitoring......Page 78
2.5.1 The Clark enzyme electrode......Page 79
2.5.3 Mediated biosensors......Page 81
2.6.1 General principles......Page 82
2.6.2.1 Roche Diagnostics......Page 83
2.6.2.2 LifeScan......Page 84
2.6.2.4 Bayer HealthCare......Page 85
2.7 Integrated Devices......Page 86
2.8.1 Minimally invasive testing......Page 88
2.8.2.1 Continuous glucose monitoring system – Electrochemical detection of glucose oxidase......Page 89
2.8.2.2 Continuous glucose monitoring system – Optical detection of glucose oxidase......Page 90
2.9 Challenges and Hurdles Facing Glucose Biosensors......Page 91
2.10 Future Perspectives & Conclusions......Page 93
References......Page 94
Chapter 3: Monte Carlo Simulation of Light Propagation in Human Tissues and Noninvasive Glucose Sensing......Page 97
3.1 Introduction......Page 98
3.2 Effect of Glucose on Optical Parameters of Particulate Media......Page 99
3.3.1 Basics of the Monte Carlo method......Page 100
3.3.2 Monte Carlo algorithm......Page 101
3.4.1 Principles of OCT......Page 104
3.4.2 Simulation of the OCT A-scan......Page 105
3.4.3 Comparison of simulated and experimental results......Page 107
3.5.1 Multilayer biotissue phantom and its optical properties for Monte Carlo simulation......Page 109
3.5.2 SRR-signal......Page 110
3.5.3 Relative sensitivity of SRR......Page 112
3.5.5 Dependence of SRR-signal on glucose concentration......Page 113
3.6 Modeling of Glucose Sensing with Time Domain Technique......Page 115
3.6.1 Output time-of-flight signal......Page 116
3.6.2 Relative sensitivity of the TOF signals to glucose concentration......Page 117
3.7.1 Principles of frequency domain technique......Page 118
3.7.2 Simulation of frequency domain signals......Page 120
3.7.3 Analysis of glucose sensing potentialities of the frequency domain technique......Page 121
3.8 Conclusion......Page 122
References......Page 123
Chapter 4: Statistical Analysis for Glucose Prediction in Blood Samples by Infrared Spectroscopy......Page 128
4.1 Introduction......Page 129
4.2.1 Optimal wavelength region in the mid infrared......Page 131
4.2.2 Optimal wavelength region in the near infrared......Page 133
4.3 Minimization of Hemoglobin Interference......Page 136
4.3.2 Hemoglobin influence in the near infrared region......Page 137
4.4 Independent Component Analysis without Calibration Process......Page 139
4.5 Conclusion......Page 142
References......Page 144
Chapter 5: Near-Infrared Reflection Spectroscopy for Noninvasive Monitoring of Glucose — Established and Novel Strategies for Multivariate Calibration......Page 146
5.1 Introduction......Page 147
5.2.1 Patients and calibration design......Page 149
5.2.2 Reference measurements and calibration method......Page 150
5.2.3 Experiments and spectroscopic data......Page 151
5.3 Results Obtained by Conventional Calibration and Discussion......Page 155
5.4 Advantages of the “Science-Based” Calibration Method......Page 163
5.5 Theory and Background......Page 164
5.6 Specificity of Response......Page 167
5.7 Illustration of the Science-Based Calibration Method......Page 172
5.7.1 Outlook for the novel calibration method......Page 181
5.8 Conclusions......Page 182
Acknowledgments......Page 183
References......Page 184
6.1 Introduction......Page 188
6.2.1.1 Laser speckle phenomenon......Page 190
6.2.1.2 Laser speckle contrast analysis......Page 191
6.2.1.4 Measuring CBF through LSI......Page 192
6.2.2.1 Principles on intrinsic optical signal imaging......Page 193
6.2.2.3 Monitoring IOS changes during SD by IOSI......Page 194
6.3.1 Long-term monitoring the influence of glucose upon CBF in rat cortex......Page 195
6.3.2 Optical imaging of hemodynamic response during cortical spreading depression in the normal or acute hyperglycemic rat cortex......Page 198
6.4 Conclusion......Page 200
Acknowledgment......Page 201
References......Page 205
Chapter 7: Near-Infrared Thermo-Optical Response of the Localized Reflectance of Diabetic and Non-Diabetic Human Skin......Page 212
7.1 Introduction......Page 213
7.2 Experimental Setup......Page 215
7.3 Temperature Dependence of µa and µ's of Individual’s Skin......Page 216
7.4 Temperature Modulation of µa and µ's of Skin Over Prolonged Interaction Between the Optical Probe and Skin......Page 221
7.5 Dependence of Thermo-Optical Response of Localized Reflectance of Human Skin on Diabetic State......Page 223
7.6 Test for Diabetic State......Page 224
7.7 Biological Noise and Glucose Determinations......Page 225
7.8 Conclusions......Page 228
Acknowledgments......Page 229
References......Page 230
Chapter 8: In Vivo Nondestructive Measurement of Blood Glucose by Near-Infrared Diffuse-Reflectance Spectroscopy......Page 235
8.1 Introduction......Page 236
8.2 Importance of NIR In Vivo Monitoring of Blood Glucose......Page 237
8.3 The NIR System for Noninvasive Blood Glucose Assay......Page 238
8.3.1 Outline of the NIR instrument......Page 239
8.4.1 NIR spectra of human skin......Page 240
8.4.3 Blood glucose assay......Page 241
8.4.5 The prediction of blood glucose content......Page 244
8.5 New Chemometrics Algorithms for Wavelength Interval Selection and Sample Selection and Their Applications to In Vivo Near-Infrared Spectroscopic Determination of Blood Glucose......Page 247
8.5.1 Moving window partial least squares regression (MWPLSR)......Page 249
8.5.2 Changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS)......Page 251
8.5.3 Application of MWPLSR and SCMWPLS to noninvasive blood glucose assay with NIR spectroscopy......Page 252
8.6.1 Multi-objective genetic algorithm......Page 256
8.6.2 Sample selection by multi-objective GA in PLS......Page 257
8.6.3 Applications of multi-objective GA to NIR spectra of human skin......Page 258
8.7 Region Orthogonal Signal Correction (ROSC) and Its Application to In Vivo NIR Spectra of Human Skin......Page 261
References......Page 263
Chapter 9: Glucose Correlation with Light Scattering Patterns......Page 267
9.1.2 Current art of noninvasive blood measurements......Page 268
9.1.3 Red blood cells aggregation phenomena......Page 270
9.1.5 Clinical relevance of RBC aggregation......Page 272
9.1.6 Measurement of RBC aggregation......Page 273
9.2.1 Aggregation assisted optical signal in vivo......Page 274
9.2.2 The occlusion spectroscopy system......Page 275
9.3.1 The parametric slope......Page 277
9.3.2 Structure of parametric slope in vivo......Page 278
9.3.3 In vitro measurement of POS signal......Page 280
9.4.1 Mismatch of refractive index......Page 283
9.4.2 Mismatch of refractive index as a function of glucose......Page 284
9.5.1 Time dependent optical parameters......Page 286
9.5.2 General expression for the PS......Page 288
9.6.2 PS for Mie scattering approximation......Page 292
9.6.3 PSV as a function of glucose......Page 294
9.6.4 PSS as function of blood plasma glucose for small aggregates......Page 296
9.7.1 WKB approximation......Page 297
9.7.2 Expression for the K-function......Page 299
9.7.3 Critical wavelength......Page 300
9.8 Conclusions......Page 303
Acknowledgments......Page 305
References......Page 306
Chapter 10: Challenges and Countermeasures in NIR Noninvasive Blood Glucose Monitoring......Page 311
10.1.1 The principle of blood glucose measurement using near infrared spectroscopy......Page 312
10.1.2 Noninvasive glucose measurement by diffuse reflectance spectroscopy......Page 313
10.1.3 The main questions of noninvasive glucose measurement by NIR spectroscopy......Page 316
10.2 Factors of Influencing the Measuring Precision of Glucose Monitor......Page 317
10.2.1 The relationship between measuring precision and instrumental precision......Page 318
10.2.2 An effective calibration method to improve the measuring precision of glucose concentration......Page 319
10.2.3 The influence of sample complexity on measuring precision......Page 320
10.2.4 The optimal pathlength method to improve the measuring precision of glucose concentration......Page 323
10.2.5 Precision analysis of the glucose concentration measurement by diffuse reflectance spectroscopy from dermis layer......Page 325
10.3.1 The influence of measurement site and position......Page 327
10.3.2 The influence of contact pressure......Page 329
10.3.2.1 Experiment on the influence of contact pressure......Page 330
10.3.2.2 The optimal contact state......Page 331
10.3.2.3 The optimal measurement time......Page 333
10.3.3 The measuring conditions reproducible system (MCRS) and human glucose sensing experiments......Page 334
10.4.1 The influence of the time dependent variations from physiological background on the glucose measurement......Page 337
10.4.2 The floating-reference method solution......Page 339
10.4.3 The preliminary experimental validation of the floating-reference method......Page 342
10.4.4 Summary......Page 345
References......Page 346
11.1 Introduction......Page 348
11.3 Issues Involved with In Vivo Glucose Monitoring Using Fluorescent Sensors......Page 350
11.4 Fluorescence-Based Glucose-Binding Protein Assays......Page 353
11.4.1 Concanavalin A-based approaches......Page 354
11.4.2 Engineered glucose-binding proteins......Page 360
11.5 Fluorescence Resonance Energy Transfer Systems for Glucose Monitoring......Page 361
11.5.1 Single-molecule RET systems using dual-labeled engineered proteins......Page 364
11.6 Enzyme-Based Glucose Sensors......Page 365
11.6.1 Apo-glucose oxidase......Page 366
11.7 Boronic Acid Derivatives......Page 367
11.8 Summary and Concluding Remarks......Page 370
References......Page 371
Chapter 12: Quantitative Biological Raman Spectroscopy......Page 382
12.1.1 Introduction to Raman spectroscopy......Page 383
12.2 Review......Page 385
12.2.3 Multivariate implementation......Page 386
12.3 Quantitative Considerations for Raman Spectroscopy......Page 387
12.3.3 Chance or spurious correlation......Page 388
12.3.4 Spectral evidence of the analyte of interest......Page 389
12.4.1 Using near infrared radiation......Page 390
12.4.2 Background signal in biological Raman spectra......Page 391
12.5 Instrumentation......Page 393
12.5.2 Light delivery, collection, and transport......Page 394
12.5.3 Spectrograph and detector......Page 395
12.6.1 Image curvature correction......Page 396
12.6.2 Spectral range selection......Page 398
12.6.5 Random noise rejection and suppression......Page 399
12.7.1 Model validation protocol and summary statistics......Page 400
12.7.2 Blood serum......Page 401
12.7.4 Human study......Page 402
12.8.1 Analyte-specific information extraction using hybrid calibration methods......Page 403
12.8.3 Constrained regularization (CR)......Page 404
12.8.5 Corrections based on photon migration theory......Page 406
12.8.6 Intrinsic Raman spectroscopy (IRS)......Page 407
12.8.7.2 Tissue morphology and skin heterogeneity......Page 408
Acknowledgments......Page 409
References......Page 410
Chapter 13: Tear Fluid Photonic Crystal Contact Lens Noninvasive Glucose Sensors......Page 415
13.1 Importance of Glucose Monitoring in Diabetes Management......Page 416
13.2 Eye Tear Film......Page 417
13.3.1 Tear fluid glucose transport......Page 418
13.3.2 Tear glucose in diabetic subjects......Page 419
13.4.1 Previous measurements of tears in extracted tear fluid......Page 420
13.4.2 Mechanical tear fluid stimulation......Page 421
13.4.3 Chemical and non-contact tear fluid stimulation......Page 422
13.4.4 Non-stimulated tear fluid......Page 423
13.5 Recent Tear Fluid Glucose Determinations......Page 424
13.6 In Situ Tear Glucose Measurements......Page 428
13.7 Photonic Crystal Glucose Sensors......Page 429
13.8 Summary......Page 437
Financial Disclosures......Page 438
References......Page 439
14.1 Introduction......Page 446
14.2 Theoretical Aspects of PA Techniques Used in Glucose Measurements......Page 449
14.2.1 Cylindrical PA source in a weakly absorbing liquid......Page 450
14.2.2 Plane PA source in strongly absorbing and scattering tissues......Page 452
14.2.3 Spherical PA source......Page 455
14.3.1 Optical sources......Page 457
14.3.2.1 Piezoelectric detection......Page 459
14.3.2.2 Optical detection......Page 462
14.4 PA Glucose Determination......Page 464
14.4.1.1 Water solutions......Page 465
14.4.1.2 Tissue phantoms......Page 466
14.4.1.3 Tissue......Page 468
14.4.2 In vivo noninvasive glucose determination......Page 470
14.5 Problems and Future Perspectives......Page 472
References......Page 475
Chapter 15: A Noninvasive Glucose Sensor Based on Polarimetric Measurements Through the Aqueous Humor of the Eye......Page 483
15.2 Theory of Polarized Light for Detecting Chemical Compounds......Page 484
15.3.1 Why use the eye?......Page 488
15.3.2 The anatomy and physiology of the eye toward glucose monitoring......Page 489
15.3.3 Corneal curvature and birefringence......Page 491
15.4 Polarimetric Glucose Monitoring Using a Single Wavelength......Page 495
15.5 Measurement of Optical Rotatory Dispersion of Aqueous Humor Analytes......Page 496
15.6 Corneal Birefringence Simulation and Experimental Measurement......Page 501
15.7 Dual Wavelength (Multi-Spectral) Polarimetric Glucose Monitoring......Page 506
15.8 Concluding Remarks Regarding the Use of Polarization for Glucose Monitoring......Page 507
References......Page 509
Chapter 16: Noninvasive Measurements of Glucose in the Human Body Using Polarimetry and Brewster-Reflection Off of the Eye Lens......Page 512
16.1 Introduction......Page 513
16.2 Basic Theory......Page 514
16.3.1 Polarization effects in the eye’s anterior chamber......Page 515
16.3.2 The Navarro eye model......Page 516
16.4.2 Brewster scheme......Page 518
16.5.1 Working principle......Page 523
16.5.2 Angle detection unit......Page 524
16.5.3 Experimental set-up......Page 525
16.6 Performance of the Glucose Sensor Based on the Brewster Scheme......Page 526
16.6.1.1 Error due to optical path length variations......Page 527
16.6.1.2 Error due to reflection off the ocular lens......Page 528
16.6.1.3 Error due to refraction at the cornea......Page 530
16.6.1.4 Error due to dispersion of the eye media......Page 531
16.6.1.5.1 Influence of wavelength variations......Page 534
16.6.1.5.2 Influence of entrance condition variations......Page 536
16.6.1.5.3 Influence of anterior lens shape variations......Page 537
16.6.1.5.4 Influence of anterior cornea shape variations......Page 538
16.6.1.5.5 Influence of involuntary eye movements......Page 539
16.6.2.1 Rotating linear polarizer......Page 541
16.6.2.2 Rotating lambda/4 plate......Page 543
16.7 Conclusion......Page 548
References......Page 549
Chapter 17: Toward Noninvasive Glucose Sensing Using Polarization Analysis of Multiply Scattered Light......Page 552
17.1 Introduction......Page 553
17.2 Polarimetry in Turbid Media: Experimental Platform for Sensitive Polarization Measurements in the Presence of Large Depolarized Noise......Page 555
17.3 Polarimetry in Turbid Media: Accurate Forward Modeling Using the Monte Carlo Approach......Page 561
17.4 Tackling the Inverse Problem: Polar Decomposition of the Lumped Mueller Matrix to Extract Individual Polarization Contributions......Page 565
17.5 Monte Carlo Modeling Results for Measurement Geometry, Optical Pathlength, Detection Depth, and Sampling Volume Quantification......Page 572
17.6 Combining Intensity and Polarization Information via Spectroscopic Turbid Polarimetry with Chemometric Analysis......Page 578
Acknowledgments......Page 583
References......Page 584
Chapter 18: Noninvasive Monitoring of Glucose Concentration with Optical Coherence Tomography......Page 588
18.1 Introduction......Page 589
18.2 Noninvasive Optical Techniques for Glucose Monitoring......Page 591
18.3 Optical Coherence Tomography......Page 592
18.4 Experimental Setup......Page 594
18.5 Studies in Tissue Phantoms......Page 595
18.6 Animal Studies......Page 596
18.7 Specificity Studies......Page 597
18.8 Clinical Studies......Page 599
18.9 Mechanisms of Glucose-Induced Changes in Optical Properties of Tissue......Page 601
Acknowledgments......Page 603
References......Page 604
Chapter 19: Measurement of Glucose Diffusion Coefficients in Human Tissues......Page 612
19.1 Introduction......Page 613
19.2 Spectroscopic Methods......Page 614
19.3 Photoacoustic Technique......Page 621
19.4 Use of Radioactive Labels for Detecting Matter Flux......Page 623
19.5.1 Spectrophotometry......Page 625
19.5.2 OCT and interferometry......Page 635
19.6 Conclusion......Page 637
Acknowledgments......Page 638
References......Page 639
Chapter 20: Monitoring of Glucose Diffusion in Epithelial Tissues with Optical Coherence Tomography......Page 647
20.1 Introduction......Page 648
20.2 Basic Theories of Glucose-Induced Changes of Tissue Optical Properties......Page 651
20.3.1 Materials and methods......Page 654
20.3.2 Quantification of molecular diffusion in ocular tissues (cornea and sclera) in vitro......Page 656
20.3.3 Quantification of glucose diffusion in skin in vitro......Page 660
20.3.5 Quantification of glucose diffusion in healthy and diseased aortas in vitro......Page 661
20.3.6 Comparative studies for assessment of molecular diffusion with OCT and histology......Page 664
20.3.7 Assessment of optical clearing of ocular tissues with OCT......Page 666
20.3.8 Depth-resolved assessment of glucose diffusion in tissues......Page 667
Acknowledgments......Page 669
References......Page 670
Chapter 21: Glucose-Induced Optical Clearing Effects in Tissues and Blood......Page 681
21.1 Introduction......Page 682
21.2.1 Structure, physical and optical properties of fibrous tissues......Page 683
21.2.2 Structure, physical and optical properties of skin......Page 684
21.2.3 Optical model of fibrous tissue......Page 685
21.2.4 Structure, physical and optical properties of blood......Page 687
21.2.5 Optical model of blood......Page 688
21.3.1 Mechanisms of optical immersion clearing......Page 690
21.3.2.1 In vitro spectral measurements......Page 691
21.3.2.2 In vivo spectral measurements......Page 693
21.3.2.3 Polarization measurements......Page 695
21.3.3.2 Two-photon microscopy......Page 696
21.3.3.3 OCT imaging......Page 698
21.3.3.5 In vivo spectral and fluorescence measurements......Page 699
21.4.1 Optical clearing of blood......Page 703
21.4.3 Experimental results......Page 706
Acknowledgment......Page 707
References......Page 708

Citation preview

Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues

© 2009 by Taylor & Francis Group, LLC

Series in Medical Physics and Biomedical Engineering Series Editors: John  G Webster, E Russell Ritenour, Slavik Tabakov, and Kwan-Hoong Ng Other recent books in the series: A Introduction to Radiation Protection in Medicine Jamie V. Trapp and Tomas Kron (Eds) A Practical Approach to Medical Image Processing Elizabeth Berry Biomolecular Action of Ionizing Radiation Shirley Lehnert An Introduction to Rehabilitation Engineering R A Cooper, H Ohnabe, and D A Hobson The Physics of Modern Brachytherapy for Oncology D Baltas, N Zamboglou, and L Sakelliou Electrical Impedance Tomography D Holder (Ed) Contemporary IMRT S Webb The Physical Measurement of Bone C M Langton and C F Njeh (Eds) Therapeutic Applications of Monte Carlo Calculations in Nuclear Medicine H Zaidi and G Sgouros (Eds) Minimally Invasive Medical Technology J G Webster (Ed) Intensity-Modulated Radiation Therapy S Webb Physics for Diagnostic Radiology, Second Edition P Dendy and B Heaton Achieving Quality in Brachytherapy B R Thomadsen Medical Physics and Biomedical Engineering B H Brown, R H Smallwood, D C Barber, P V Lawford, and D R Hose Monte Carlo Calculations in Nuclear Medicine M Ljungberg, S-E Strand, and M A King (Eds) © 2009 by Taylor & Francis Group, LLC

Series in Medical Physics and Biomedical Engineering

Handbook of Optical Sensing of Glucose in Biological Fluids and Tissues

Edited by

Valery V. Tuchin

Saratov State University and Institute of Precise Mechanics and Control of RAS Russia

Boca Raton London New York

CRC Press is an imprint of the Taylor & Francis Group, an informa business

A TA Y L O R & F R A N C I S B O O K

© 2009 by Taylor & Francis Group, LLC

CRC Press Taylor & Francis Group 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487‑2742 © 2009 by Taylor & Francis Group, LLC CRC Press is an imprint of Taylor & Francis Group, an Informa business No claim to original U.S. Government works Printed in the United States of America on acid‑free paper 10 9 8 7 6 5 4 3 2 1 International Standard Book Number‑13: 978‑1‑58488‑974‑8 (Hardcover) This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, trans‑ mitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, please access www.copyright. com (http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978‑750‑8400. CCC is a not‑for‑profit organization that provides licenses and registration for a variety of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment has been arranged. Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Handbook of optical sensing of glucose in biological fluids and tissues / editor, Valery V. Tuchin. p. ; cm. ‑‑ (Series in medical physics and biomedical engineering) Includes bibliographical references and index. ISBN 978‑1‑58488‑974‑8 (hardback : alk. paper) 1. Blood sugar monitoring‑‑Handbooks, manuals, etc. 2. Near infrared spectroscopy‑‑Handbooks, manuals, etc. 3. Diagnosis, Noninvasive‑‑Handbooks, manuals, etc. I. Tuchin, V. V. (Valerii Viktorovich) II. Series. [DNLM: 1. Blood Glucose‑‑analysis. 2. Biosensing Techniques‑‑instrumentation. 3. Blood Glucose Self‑Monitoring‑‑instrumentation. 4. Blood Glucose Self‑Monitoring‑‑methods. 5. Optics. 6. Spectrum Analysis‑‑methods. 7. Tomography, Optical‑‑methods. QY 470 H236 2008] RC660.H363 2008 572’.565‑‑dc22 Visit the Taylor & Francis Web site at http://www.taylorandfrancis.com and the CRC Press Web site at http://www.crcpress.com

© 2009 by Taylor & Francis Group, LLC

2008014799

Contents

Preface List of Contributors 1

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Glucose: Physiological Norm and Pathology Lidia I. Malinova and Tatyana P. Denisova 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1 Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.2 Terms and definitions . . . . . . . . . . . . . . . . . . . . . 1.2 System of Blood Glucose Level Regulation and Carbohydrate Metabolism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Glucose transporters . . . . . . . . . . . . . . . . . . . . . 1.2.2 Pathways of glucose concentration change: glucose distribution and concentrations in human organism . . . . . . . . . 1.2.3 Regulation of glucose metabolism: main pathways and processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.4 Insulin: the key hormone of glucose metabolism . . . . . . 1.2.5 Endothelium and glucose metabolism . . . . . . . . . . . . 1.3 Glucose and Carbohydrate Metabolism Violations . . . . . . . . . 1.3.1 Diabetes mellitus: glucose — victim or culprit? . . . . . . . 1.3.2 Atherosclerosis and coronary artery disease: glucose’s place in pathogenesis . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Blood Glucose Level Monitoring in Clinical Practice . . . . . . . . 1.4.1 Glucose level regulation system tests: clinical and experimental use . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Clinical value of blood glucose level measurements . . . . . 1.4.3 Current state of the problem: unsolved questions . . . . . . 1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Commercial Biosensors for Diabetes Vasiliki Fragkou and Anthony P.F. Turner 2.1 Introduction . . . . . . . . . . . . . . . . . 2.2 Diabetes Mellitus . . . . . . . . . . . . . . 2.2.1 Type I diabetes . . . . . . . . . . . 2.2.2 Type II diabetes . . . . . . . . . . 2.2.3 Gestational diabetes . . . . . . . . . 2.2.4 Incidence - A major world problem

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Handbook of Optical Sensing of Glucose 2.2.5 Treatments . . . . . . . . . . . . . . . . . . . . Home Urine/Blood Glucose Monitoring . . . . . . . . . 2.3.1 Urine glucose monitoring . . . . . . . . . . . . . 2.3.2 Blood glucose monitoring . . . . . . . . . . . . 2.4 Glucose Meters . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Colorimetric strips . . . . . . . . . . . . . . . . 2.4.2 Ames Reflectance Meter . . . . . . . . . . . . . 2.5 Glucose Biosensors . . . . . . . . . . . . . . . . . . . . 2.5.1 The Clark enzyme electrode . . . . . . . . . . . 2.5.2 Yellow Springs Instrument . . . . . . . . . . . . 2.5.3 Mediated biosensors . . . . . . . . . . . . . . . 2.6 Current Commercial Home Blood Glucose Monitoring . 2.6.1 General principles . . . . . . . . . . . . . . . . 2.6.2 Commercial aspects . . . . . . . . . . . . . . . . 2.7 Integrated Devices . . . . . . . . . . . . . . . . . . . . 2.8 Alternative Glucose Monitors . . . . . . . . . . . . . . 2.8.1 Minimally invasive testing . . . . . . . . . . . . 2.8.2 Continuous Glucose Monitoring System [36, 37] 2.9 Challenges and Hurdles Facing Glucose Biosensors . . . 2.10 Future Perspectives & Conclusions . . . . . . . . . . . 2.3

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Monte Carlo Simulation of Light Propagation in Human Tissues and Noninvasive Glucose Sensing Alexander V. Bykov, Mikhail Yu. Kirillin and Alexander V. Priezzhev 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Effect of Glucose on Optical Parameters of Particulate Media . . . 3.3 Principles of the Monte Carlo Technique . . . . . . . . . . . . . . 3.3.1 Basics of the Monte Carlo method . . . . . . . . . . . . . . 3.3.2 Monte Carlo algorithm . . . . . . . . . . . . . . . . . . . . 3.4 Modeling of Glucose Sensing with OCT . . . . . . . . . . . . . . . 3.4.1 Principles of OCT . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Simulation of the OCT A-scan . . . . . . . . . . . . . . . . 3.4.3 Comparison of simulated and experimental results . . . . . 3.5 Modeling of Glucose Sensing with Spatial Resolved Reflectometry 3.5.1 Multilayer biotissue phantom and its optical properties for Monte Carlo simulation . . . . . . . . . . . . . . . . . . . . 3.5.2 SRR-signal . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Relative sensitivity of SRR . . . . . . . . . . . . . . . . . . 3.5.4 Scattering maps . . . . . . . . . . . . . . . . . . . . . . . 3.5.5 Dependence of SRR-signal on glucose concentration . . . . 3.6 Modeling of Glucose Sensing with Time Domain Technique . . . . 3.6.1 Output time-of-flight signal . . . . . . . . . . . . . . . . . . 3.6.2 Relative sensitivity of the TOF signals to glucose concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3.8 4

Modeling of Glucose Sensing with Frequency Domain Technique . 3.7.1 Principles of frequency domain technique . . . . . . . . . . 3.7.2 Simulation of frequency domain signals . . . . . . . . . . . 3.7.3 Analysis of glucose sensing potentialities of the frequency domain technique . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

vii 86 86 88 89 90

Statistical Analysis for Glucose Prediction in Blood Samples by Infrared Spectroscopy 97 Gilwon Yoon 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Selection of Optimal Wavelength Region Based on the First Loading Vector Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Optimal wavelength region in the mid infrared . . . . . . . 4.2.2 Optimal wavelength region in the near infrared . . . . . . . 4.3 Minimization of Hemoglobin Interference . . . . . . . . . . . . . . 4.3.1 Hemoglobin influence in the mid infrared region . . . . . . 4.3.2 Hemoglobin influence in the near infrared region . . . . . . 4.4 Independent Component Analysis without Calibration Process . . . 4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

98 100 100 102 105 106 106 108 111

5

Near-Infrared Reflection Spectroscopy for Noninvasive Monitoring of Glucose — Established and Novel Strategies for Multivariate Calibration 115 H.Michael Heise, Peter Lampen and Ralf Marbach 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 5.2 Experimental Design and Methods . . . . . . . . . . . . . . . . . . 118 5.2.1 Patients and calibration design . . . . . . . . . . . . . . . . 118 5.2.2 Reference measurements and calibration method . . . . . . 119 5.2.3 Experiments and spectroscopic data . . . . . . . . . . . . . 120 5.3 Results Obtained by Conventional Calibration and Discussion . . . 124 5.4 Advantages of the “Science-Based” Calibration Method . . . . . . 132 5.5 Theory and Background . . . . . . . . . . . . . . . . . . . . . . . 133 5.6 Specificity of Response . . . . . . . . . . . . . . . . . . . . . . . . 136 5.7 Illustration of the Science-Based Calibration Method . . . . . . . . 141 5.7.1 Outlook for the novel calibration method . . . . . . . . . . 150 5.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151

6

Characterizing the Influence of Acute Hyperglycaemia on Cerebral Hemodynamics by Optical Imaging 157 Qingming Luo, Zhen Wang, Weihua Luo and Pengcheng Li 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 6.2 Optical Imaging Techniques of Functional Brain . . . . . . . . . . 159 6.2.1 Laser speckle imaging . . . . . . . . . . . . . . . . . . . . 159

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6.3

6.4

6.2.2 Intrinsic optical signal imaging . . . . . . . . . . . . . . . . Influence of Acute Hyperglycaemia on CBF and SD in Rat Cortex . 6.3.1 Long-term monitoring the influence of glucose upon CBF in rat cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Optical imaging of hemodynamic response during cortical spreading depression in the normal or acute hyperglycemic rat cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

162 164 164

167 169

7

Near-Infrared Thermo-Optical Response of the Localized Reflectance of Diabetic and Non-Diabetic Human Skin 181 Omar S. Khalil 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 7.2 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 184 7.3 Temperature Dependence of µa and µs′ of Individual’s Skin . . . . . 185 7.4 Temperature Modulation of µa and µs′ of Skin Over Prolonged Interaction Between the Optical Probe and Skin . . . . . . . . . . . . . 190 7.5 Dependence of Thermo-Optical Response of Localized Reflectance of Human Skin on Diabetic State . . . . . . . . . . . . . . . . . . 192 7.6 Test for Diabetic State . . . . . . . . . . . . . . . . . . . . . . . . 193 7.7 Biological Noise and Glucose Determinations . . . . . . . . . . . . 194 7.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197

8

In Vivo Nondestructive Measurement of Blood Glucose by Near-Infrared Diffuse-Reflectance Spectroscopy 205 Yukihiro Ozaki, Hideyuki Shinzawa, Katsuhiko Maruo, Yi Ping Du and Sumaporn Kasemsumran 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 8.2 Importance of NIR In Vivo Monitoring of Blood Glucose . . . . . . 207 8.3 The NIR System for Noninvasive Blood Glucose Assay . . . . . . 208 8.3.1 Outline of the NIR instrument . . . . . . . . . . . . . . . . 209 8.3.2 Spectral measurements . . . . . . . . . . . . . . . . . . . . 210 8.4 NIR Spectra of Human Skin and Built of Calibration Models . . . . 210 8.4.1 NIR spectra of human skin . . . . . . . . . . . . . . . . . . 210 8.4.2 Calibration models . . . . . . . . . . . . . . . . . . . . . . 211 8.4.3 Blood glucose assay . . . . . . . . . . . . . . . . . . . . . 211 8.4.4 The regression coefficient characteristics . . . . . . . . . . . 214 8.4.5 The prediction of blood glucose content . . . . . . . . . . . 214 8.5 New Chemometrics Algorithms for Wavelength Interval Selection and Sample Selection and Their Applications to In Vivo Near-Infrared Spectroscopic Determination of Blood Glucose . . . . . . . . . . . 217 8.5.1 Moving window partial least squares regression (MWPLSR) 219 8.5.2 Changeable size moving window partial least squares (CSMWPLS) and searching combination moving window partial least squares (SCMWPLS) . . . . . . . . . . . . . . . . . . . . . 221

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Application of MWPLSR and SCMWPLS to noninvasive blood glucose assay with NIR spectroscopy . . . . . . . . . . . . 222 Multi-Objective Genetic Algorithm-Based Sample Selection for Partial Least Squares Model Building . . . . . . . . . . . . . . . . . . 226 8.6.1 Multi-objective genetic algorithm . . . . . . . . . . . . . . 226 8.6.2 Sample selection by multi-objective GA in PLS . . . . . . . 227 8.6.3 Applications of multi-objective GA to NIR spectra of human skin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 Region Orthogonal Signal Correction (ROSC) and Its Application to In Vivo NIR Spectra of Human Skin . . . . . . . . . . . . . . . . . 231

Glucose Correlation with Light Scattering Patterns Ilya Fine 9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1.1 Clinical need for blood glucose measurement . . . . . . . . 9.1.2 Current art of noninvasive blood measurements . . . . . . . 9.1.3 Red blood cells aggregation phenomena . . . . . . . . . . . 9.1.4 Shear forces and blood viscosity . . . . . . . . . . . . . . . 9.1.5 Clinical relevance of RBC aggregation . . . . . . . . . . . 9.1.6 Measurement of RBC aggregation . . . . . . . . . . . . . . 9.2 Principles of Occlusion Spectroscopy . . . . . . . . . . . . . . . . 9.2.1 Aggregation assisted optical signal in vivo . . . . . . . . . . 9.2.2 The occlusion spectroscopy system . . . . . . . . . . . . . 9.3 Spectro-Kinetic Features of Aggregation Assisted Signal . . . . . . 9.3.1 The parametric slope . . . . . . . . . . . . . . . . . . . . . 9.3.2 Structure of parametric slope in vivo . . . . . . . . . . . . . 9.3.3 In vitro measurement of POS signal . . . . . . . . . . . . . 9.4 Refractive Index of RBC as a Function of Blood Glucose . . . . . . 9.4.1 Mismatch of refractive index . . . . . . . . . . . . . . . . . 9.4.2 Mismatch of refractive index as a function of glucose . . . . 9.5 Parametric Slope as a Function of BG . . . . . . . . . . . . . . . . 9.5.1 Time dependent optical parameters . . . . . . . . . . . . . . 9.5.2 General expression for the PS . . . . . . . . . . . . . . . . 9.6 PS Glucose Dependence for Single RBCs and Small Aggregates . . 9.6.1 RBC scattering pattern . . . . . . . . . . . . . . . . . . . . 9.6.2 PS for Mie scattering approximation . . . . . . . . . . . . . 9.6.3 PSV as a function of glucose . . . . . . . . . . . . . . . . . 9.6.4 PSS as function of blood plasma glucose for small aggregates 9.7 PSS in the Framework of WKB Model . . . . . . . . . . . . . . . 9.7.1 WKB approximation . . . . . . . . . . . . . . . . . . . . . 9.7.2 Expression for the K-function . . . . . . . . . . . . . . . . 9.7.3 Critical wavelength . . . . . . . . . . . . . . . . . . . . . . 9.7.4 Effect of glucose on the light transmission for very long aggregates . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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237 238 238 238 240 242 242 243 244 244 245 247 247 248 250 253 253 254 256 256 258 262 262 262 264 266 267 267 269 270 273

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Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

10 Challenges and Countermeasures in NIR Noninvasive Blood Glucose Monitoring 281 Kexin Xu and Ruikang K. Wang 10.1 The Principles and Issues on the Measurement of Blood Glucose Using Near-Infrared Spectroscopy . . . . . . . . . . . . . . . . . . 10.1.1 The principle of blood glucose measurement using near infrared spectroscopy . . . . . . . . . . . . . . . . . . . . . . 10.1.2 Noninvasive glucose measurement by diffuse reflectance spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.1.3 The main questions of noninvasive glucose measurement by NIR spectroscopy . . . . . . . . . . . . . . . . . . . . . . 10.2 Factors of Influencing the Measuring Precision of Glucose Monitor 10.2.1 The relationship between measuring precision and instrumental precision . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.2 An effective calibration method to improve the measuring precision of glucose concentration . . . . . . . . . . . . . . 10.2.3 The influence of sample complexity on measuring precision 10.2.4 The optimal pathlength method to improve the measuring precision of glucose concentration . . . . . . . . . . . . . . 10.2.5 Precision analysis of the glucose concentration measurement by diffuse reflectance spectroscopy from dermis layer . . . . 10.3 Noninvasive Glucose Measurement and Human-Spectrometer Interface Technique . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.1 The influence of measurement site and position . . . . . . . 10.3.2 The influence of contact pressure . . . . . . . . . . . . . . . 10.3.3 The measuring conditions reproducible system (MCRS) and human glucose sensing experiments . . . . . . . . . . . . . 10.4 Challenges and Solutions in In Vivo Noninvasive Blood Glucose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4.1 The influence of the time dependent variations from physiological background on the glucose measurement . . . . . . . 10.4.2 The floating-reference method solution . . . . . . . . . . . 10.4.3 The preliminary experimental validation of the floating-reference method . . . . . . . . . . . . . . . . . . . . . . . . 10.4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Fluorescence-Based Glucose Biosensors

282 282 283 286 287 288 289 290 293 295 297 297 299 304 307 307 309 312 315 319

Gerard L. Cot`e, M. McShane and M.V. Pishko 11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 11.2 Historical Review of Fluorescence-Based Glucose Assays . . . . . 321 11.3 Issues Involved with In Vivo Glucose Monitoring Using Fluorescent Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

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Table of Contents 11.4 Fluorescence-Based Glucose-Binding Protein Assays . . . . . . . . 11.4.1 Concanavalin A-based approaches . . . . . . . . . . . . . . 11.4.2 Engineered glucose-binding proteins . . . . . . . . . . . . . 11.5 Fluorescence Resonance Energy Transfer Systems for Glucose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.5.1 Single-molecule RET systems using dual-labeled engineered proteins . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Enzyme-Based Glucose Sensors . . . . . . . . . . . . . . . . . . . 11.6.1 Apo-glucose oxidase . . . . . . . . . . . . . . . . . . . . . 11.7 Boronic Acid Derivatives . . . . . . . . . . . . . . . . . . . . . . 11.8 Summary and Concluding Remarks . . . . . . . . . . . . . . . . . 12 Quantitative Biological Raman Spectroscopy Wei-Chuan Shih, Kate L. Bechtel and Michael S. Feld 12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.1 Introduction to Raman spectroscopy . . . . . . . . 12.2 Review . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.1 Semi-quantitative implementation . . . . . . . . . 12.2.2 Univariate implementation . . . . . . . . . . . . . 12.2.3 Multivariate implementation . . . . . . . . . . . . 12.3 Quantitative Considerations for Raman Spectroscopy . . . 12.3.1 Considerations for multivariate calibration models 12.3.2 Fundamental and practical limits . . . . . . . . . . 12.3.3 Chance or spurious correlation . . . . . . . . . . . 12.3.4 Spectral evidence of the analyte of interest . . . . . 12.3.5 Minimum detection limit . . . . . . . . . . . . . . 12.4 Biological Considerations for Raman Spectroscopy . . . . 12.4.1 Using near infrared radiation . . . . . . . . . . . . 12.4.2 Background signal in biological Raman spectra . . 12.4.3 Heterogeneities in human skin . . . . . . . . . . . 12.5 Instrumentation . . . . . . . . . . . . . . . . . . . . . . . 12.5.1 Excitation light source . . . . . . . . . . . . . . . 12.5.2 Light delivery, collection, and transport . . . . . . 12.5.3 Spectrograph and detector . . . . . . . . . . . . . 12.6 Data Pre-Processing . . . . . . . . . . . . . . . . . . . . 12.6.1 Image curvature correction . . . . . . . . . . . . . 12.6.2 Spectral range selection . . . . . . . . . . . . . . . 12.6.3 Cosmic ray removal . . . . . . . . . . . . . . . . . 12.6.4 Background subtraction . . . . . . . . . . . . . . . 12.6.5 Random noise rejection and suppression . . . . . . 12.6.6 White light correction and wavelength calibration . 12.6.7 Wavelength selection . . . . . . . . . . . . . . . . 12.7 In Vitro and In Vivo Studies . . . . . . . . . . . . . . . . 12.7.1 Model validation protocol and summary statistics .

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Handbook of Optical Sensing of Glucose 12.7.2 Blood serum . . . . . . . . . . . . . . . . . . . . . . . . . 12.7.3 Whole blood . . . . . . . . . . . . . . . . . . . . . . . . . 12.7.4 Human study . . . . . . . . . . . . . . . . . . . . . . . . . 12.8 Toward Prospective Application . . . . . . . . . . . . . . . . . . . 12.8.1 Analyte-specific information extraction using hybrid calibration methods . . . . . . . . . . . . . . . . . . . . . . . . . 12.8.2 Hybrid linear analysis (HLA) . . . . . . . . . . . . . . . . . 12.8.3 Constrained regularization (CR) . . . . . . . . . . . . . . . 12.8.4 Sampling volume correction using intrinsic Raman spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.8.5 Corrections based on photon migration theory . . . . . . . . 12.8.6 Intrinsic Raman spectroscopy (IRS) . . . . . . . . . . . . . 12.8.7 Other considerations and future directions . . . . . . . . . . 12.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

372 373 373 374 374 375 375 377 377 378 379 380

13 Tear Fluid Photonic Crystal Contact Lens Noninvasive Glucose Sensors 387 Sanford A. Asher and Justin T. Baca 13.1 Importance of Glucose Monitoring in Diabetes Management 13.2 Eye Tear Film . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Glucose in Tear Fluid . . . . . . . . . . . . . . . . . . . . . 13.3.1 Tear fluid glucose transport . . . . . . . . . . . . . 13.3.2 Tear glucose in diabetic subjects . . . . . . . . . . . 13.4 Previously Reported Tear Fluid Glucose Concentrations . . 13.4.1 Previous measurements of tears in extracted tear fluid 13.4.2 Mechanical tear fluid stimulation . . . . . . . . . . . 13.4.3 Chemical and non-contact tear fluid stimulation . . . 13.4.4 Non-stimulated tear fluid . . . . . . . . . . . . . . . 13.5 Recent Tear Fluid Glucose Determinations . . . . . . . . . 13.6 In Situ Tear Glucose Measurements . . . . . . . . . . . . . 13.7 Photonic Crystal Glucose Sensors . . . . . . . . . . . . . . 13.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . .

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388 389 390 390 391 392 392 393 394 395 396 400 401 409

14 Pulsed Photoacoustic Techniques and Glucose Determination in Human Blood and Tissue 419 Risto Myllyl¨a, Zuomin Zhao and Matti Kinnunen 14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Theoretical Aspects of PA Techniques Used in Glucose Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Cylindrical PA source in a weakly absorbing liquid . . . . . 14.2.2 Plane PA source in strongly absorbing and scattering tissues 14.2.3 Spherical PA source . . . . . . . . . . . . . . . . . . . . . . 14.3 Optical Sources and Detectors . . . . . . . . . . . . . . . . . . . . 14.3.1 Optical sources . . . . . . . . . . . . . . . . . . . . . . . . 14.3.2 PA detectors . . . . . . . . . . . . . . . . . . . . . . . . . .

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Table of Contents 14.4 PA Glucose Determination . . . . . . . . . . . 14.4.1 In vitro glucose studies . . . . . . . . . . 14.4.2 In vivo noninvasive glucose determination 14.5 Problems and Future Perspectives . . . . . . . .

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437 438 443 445

15 A Noninvasive Glucose Sensor Based on Polarimetric Measurements Through the Aqueous Humor of the Eye 457 Gerard L. Cot`e and Brent D. Cameron 15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 Theory of Polarized Light for Detecting Chemical Compounds . . . 15.3 The Anterior Chamber of the Eye as a Site for Polarimetric Glucose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.1 Why use the eye? . . . . . . . . . . . . . . . . . . . . . . . 15.3.2 The anatomy and physiology of the eye toward glucose monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.3 Corneal curvature and birefringence . . . . . . . . . . . . . 15.4 Polarimetric Glucose Monitoring Using a Single Wavelength . . . . 15.5 Measurement of Optical Rotatory Dispersion of Aqueous Humor Analytes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.6 Corneal Birefringence Simulation and Experimental Measurement . 15.7 Dual Wavelength (Multi-Spectral) Polarimetric Glucose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.8 Concluding Remarks Regarding the Use of Polarization for Glucose Monitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

458 458 462 462 463 465 469 470 475 480 481

16 Noninvasive Measurements of Glucose in the Human Body Using Polarimetry and Brewster-Reflection Off of the Eye Lens 487 Luigi Rovati and Rafat R. Ansari 16.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 488 16.2 Basic Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489 16.3 Anatomy and Properties of the Human Eye of Interest for Polarimetric Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 16.3.1 Polarization effects in the eye’s anterior chamber . . . . . . 490 16.3.2 The Navarro eye model . . . . . . . . . . . . . . . . . . . 491 16.4 Optical Access to the Aqueous: Tangential Path and Brewster Scheme Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493 16.4.1 Tangential path approach . . . . . . . . . . . . . . . . . . . 493 16.4.2 Brewster scheme . . . . . . . . . . . . . . . . . . . . . . . 493 16.5 Glucose Sensor Based on the Brewster Scheme . . . . . . . . . . . 498 16.5.1 Working principle . . . . . . . . . . . . . . . . . . . . . . . 498 16.5.2 Angle detection unit . . . . . . . . . . . . . . . . . . . . . 499 16.5.3 Experimental set-up . . . . . . . . . . . . . . . . . . . . . . 500 16.6 Performance of the Glucose Sensor Based on the Brewster Scheme 501 16.6.1 Theoretical analysis . . . . . . . . . . . . . . . . . . . . . 502

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17 Toward Noninvasive Glucose Sensing Using Polarization Analysis of Multiply Scattered Light 527 Michael F. G. Wood, Nirmalya Ghosh, Xinxin Guo and I. Alex Vitkin 17.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528 17.2 Polarimetry in Turbid Media: Experimental Platform for Sensitive Polarization Measurements in the Presence of Large Depolarized Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530 17.3 Polarimetry in Turbid Media: Accurate Forward Modeling Using the Monte Carlo Approach . . . . . . . . . . . . . . . . . . . . . . . . 536 17.4 Tackling the Inverse Problem: Polar Decomposition of the Lumped Mueller Matrix to Extract Individual Polarization Contributions . . 540 17.5 Monte Carlo Modeling Results for Measurement Geometry, Optical Pathlength, Detection Depth, and Sampling Volume Quantification . 547 17.6 Combining Intensity and Polarization Information via Spectroscopic Turbid Polarimetry with Chemometric Analysis . . . . . . . . . . . 553 17.7 Concluding Remarks on the Prospect of Glucose Detection in Optically Thick Scattering Tissues with Polarized Light . . . . . . . . . 558 18 Noninvasive Monitoring of Glucose Concentration with Optical Coherence Tomography 563 Rinat O. Esenaliev and Donald S. Prough 18.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 564 18.2 Noninvasive Optical Techniques for Glucose Monitoring . . . . . . 566 18.3 Optical Coherence Tomography . . . . . . . . . . . . . . . . . . . 567 18.4 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . 569 18.5 Studies in Tissue Phantoms . . . . . . . . . . . . . . . . . . . . . 570 18.6 Animal Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 571 18.7 Specificity Studies . . . . . . . . . . . . . . . . . . . . . . . . . . 572 18.8 Clinical Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 574 18.9 Mechanisms of Glucose-Induced Changes in Optical Properties of Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 576 18.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 578 19 Measurement of Glucose Diffusion Coefficients in Human Tissues Alexey N. Bashkatov, Elina A. Genina and Valery V. Tuchin 19.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19.2 Spectroscopic Methods . . . . . . . . . . . . . . . . . . . . . . 19.3 Photoacoustic Technique . . . . . . . . . . . . . . . . . . . . . 19.4 Use of Radioactive Labels for Detecting Matter Flux . . . . . . 19.5 Light Scattering Measurements . . . . . . . . . . . . . . . . .

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19.5.1 Spectrophotometry . . . . . . . . . . . . . . . . . . . . . . 600 19.5.2 OCT and interferometry . . . . . . . . . . . . . . . . . . . 610 19.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612 20 Monitoring of Glucose Diffusion in Epithelial Tissues with Optical Coherence Tomography 623 Kirill V. Larin and Valery V. Tuchin 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 624 20.2 Basic Theories of Glucose-Induced Changes of Tissue Optical Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627 20.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . 630 20.3.1 Materials and methods . . . . . . . . . . . . . . . . . . . . 630 20.3.2 Quantification of molecular diffusion in ocular tissues (cornea and sclera) in vitro . . . . . . . . . . . . . . . . . . . . . . 632 20.3.3 Quantification of glucose diffusion in skin in vitro . . . . . . 636 20.3.4 Quantification of glucose diffusion in skin in vivo . . . . . . 637 20.3.5 Quantification of glucose diffusion in healthy and diseased aortas in vitro . . . . . . . . . . . . . . . . . . . . . . . . . 637 20.3.6 Comparative studies for assessment of molecular diffusion with OCT and histology . . . . . . . . . . . . . . . . . . . 640 20.3.7 Assessment of optical clearing of ocular tissues with OCT . 642 20.3.8 Depth-resolved assessment of glucose diffusion in tissues . . 643 21 Glucose-Induced Optical Clearing Effects in Tissues and Blood Elina A. Genina, Alexey N. Bashkatov and Valery V. Tuchin 21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Structure and Optical Properties of Fibrous Tissues and Blood . . . 21.2.1 Structure, physical and optical properties of fibrous tissues . 21.2.2 Structure, physical and optical properties of skin . . . . . . 21.2.3 Optical model of fibrous tissue . . . . . . . . . . . . . . . . 21.2.4 Structure, physical and optical properties of blood . . . . . . 21.2.5 Optical model of blood . . . . . . . . . . . . . . . . . . . . 21.3 Glucose-Induced Optical Clearing Effects in Tissues . . . . . . . . 21.3.1 Mechanisms of optical immersion clearing . . . . . . . . . 21.3.2 Optical clearing of fibrous tissues . . . . . . . . . . . . . . 21.3.3 Optical clearing of skin . . . . . . . . . . . . . . . . . . . . 21.4 Glucose-Induced Optical Clearing Effects in Blood and Cellular Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.4.1 Optical clearing of blood . . . . . . . . . . . . . . . . . . . 21.4.2 Time-domain and frequency-domain measurements . . . . . 21.4.3 Experimental results . . . . . . . . . . . . . . . . . . . . . 21.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Preface

Approximately 17 million people in the USA (6% of the population) and 140 million people worldwide (this number is expected to rise to almost 300 million by the year 2025) suffer from diabetes mellitus. Currently, there are a few dozen commercialized devices for detecting blood glucose levels [1]. However, most of them are invasive. The development of a noninvasive method would considerably improve the quality of life for diabetic patients, facilitate their compliance for glucose monitoring, and reduce complications and mortality associated with this disease. Noninvasive and continuous monitoring of glucose concentration in blood and tissues is one of the most challenging and exciting applications of optics in medicine. The major difficulty in the development of a clinical application of optical noninvasive blood glucose sensors is associated with the very low signal produced by glucose molecules. This results in low sensitivity and specificity of glucose monitoring by optical methods and needs a lot of effort to overcome this difficulty. A wide range of optical technologies have been designed in attempts to develop robust noninvasive methods for glucose sensing. The methods include infrared absorption; near-infrared scattering; Raman, fluorescent, and thermal gradient spectroscopies; as well as polarimetric, polarization heterodyning, photonic crystal, optoacoustic, optothermal, and optical coherence tomography (OCT) techniques [1-31]. For example, the polarimetric quantification of glucose is based on the phenomenon of optical rotatory dispersion, whereby a chiral molecule in an aqueous solution rotates the plane of linearly polarized light passing through the solution. The angle of rotation depends linearly on the concentration of the chiral species, the path length through the sample, and the molecule specific rotation. However, a polarization sensitive optical technique makes it difficult to measure in vivo glucose concentration in blood through the skin because of the strong light scattering that causes light depolarization. For this reason, the anterior chamber of the eye has been suggested as a site well suited for polarimetric measurements, since scattering in the eye is generally very low compared to that in other tissues, and a high correlation exists between the glucose in the blood and in the aqueous humor. The high accuracy of anterior eye chamber measurements is also due to the low concentration of optically active aqueous proteins within the aqueous humor. On the other hand, the concept of noninvasive blood glucose sensing using the scattering properties of blood and tissues as an alternative to spectral absorption and polarization methods for monitoring of physiological glucose concentrations in diabetic patients has been under intensive discussion for the last decade. Many of the considered effects, such as changing of the size, refractive index, packing, and ag-

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gregation of RBC under glucose variation, are important for glucose monitoring in diabetic patients. Indeed, at physiological concentrations of glucose, ranging from 40 to 400 mg/dl, the role of some of the effects may be modified, and some other effects, such as glucose penetration inside the RBC and the followed hemoglobin glycation, may be important [30-32]. Noninvasive determination of glucose was attempted using light scattering of skin tissue components measured by a spatially-resolved diffuse reflectance or NIR frequency-domain reflectance techniques. Both approaches are based on a change in glucose concentration, which affects the refractive index mismatch between the interstitial fluid and tissue fibers, and hence reduces scattering coefficient. A glucose clamp experiment showed that reduced scattering coefficient measured in the visible range qualitatively tracked changes in blood glucose concentration in the volunteer with diabetes who was studied. The so-called occlusion spectroscopy is an approach that is based on light scattering from RBCs. This method suggests a controlled occlusion of finger blood vessels to slow blood flow in order to provide the shear forces of blood flow to be minimal and, thus, to allow RBCs to aggregate. Change in light scattering upon occlusion should be measured. Occlusion does not affect the rest of the tissue components, while scattering properties of aggregated RBCs differ from those of the nonaggregated ones and from the rest of the tissue. Change in glucose concentration affects refractive index of blood plasma, and hence affects blood light scattering at occlusion due to the refractive index match/mismatch between aggregates and plasma. Occlusion spectroscopy differs from that of spatially-resolved reflectance and frequencydomain measurements in that it proposes measurements of glucose in blood rather than in the interstitial fluid. Recently, the OCT technique has been proposed for noninvasive assessment of glucose concentration in tissues.High resolution of the OCT technique may allow high sensitivity, accuracy, and specificity of glucose concentration monitoring due to precise measurements of glucose-induced changes in the tissue optical properties from the layer of interest (dermis). Unlike diffuse reflectance method, OCT allows provision of depth-resolved qualitative and quantitative information about tissue optical properties of the three major layers of human skin: dead keratinized layer of squames (stratum corneum of epidermis); prickle cells layer (epidermis); and connective tissue of dermis. Dermis is the only layer of the skin containing a developed blood microvessel network. Since glucose concentration in the interstitial fluid is closely related to the blood glucose concentration, one can expect glucose-induced changes in the OCT signal detected from the dermis area of the skin. Two methods of OCT-based measurement and monitoring of tissue glucose concentration were proposed: 1) monitoring of the tissue scattering coefficient as a function of blood glucose concentration using conventional OCT; and 2) measurement of glucose-induced changes in the refractive index using novel polarization maintaining fiber-based dual channel phase-sensitive optical low-coherence reflectometer (PS-OLCR). In monitoring and determining chemical traces, the time-resolved OA technique and other optothermal techniques may be used in noninvasive monitoring of glucose. In the low scattering mode when aqueous glucose solutions were irradiated by NIR

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laser pulses at wavelengths that corresponded to NIR absorption of glucose (1.0–1.8 µ m), OA signal generation was assumed to be due to initial light absorption by the glucose molecules. A linear relationship between the OA signal and glucose concentration was found. It was also shown that the OA signal tracks changes in glucose concentration in human measurements. No specific advantages of OA spectroscopy over an NIR measurement of glucose are expected in this case. Another approach for OA glucose detection is based on the glucose property to change scattering parameters of tissues. The OA signal from the tissue depth is defined by optical attenuation, which is related to changes in the refractive index of the medium induced by changes in glucose concentration. Decreasing scattering increases the energy density in the OA sound source and induces higher OA signals. OA temporal profiles induced by laser pulses in in vivo tissues demonstrated that a 1 mM increase in glucose concentration resulted in up to a 5% decrease of effective optical attenuation. Measurement of glucose concentration and its diffusivity within the tissue is of widespread interest not only in the health care of diabetics, an automatic noninvasive monitoring of glucose concentration, it is also important for controlling growing cell cultures in tissue engineering, primarily for the production of implantable in vitro tissues and organs, as well as for controlling tissue optical properties [33-38]. At present only a few overview papers and sections in book chapters describing methods and techniques of optical glucose sensing and its influence on optical properties of tissues and blood are available. The recent book, edited by Geddes and Lakowicz [14], is devoted to glucose sensing using fluorescence spectroscopic method. No separate book, where various optical approaches are overviewed and discussed, could be found in literature. The lack of such a book makes it difficult to get a complete view of the field, since the information on optical glucose sensing and impact is spread over numerous publications in journals of physical, chemical, biophysical and biomedical specialization. Thus, a book that summarizes and analyzes new trends and perspectives of noninvasive glucose sensing and its impact on tissue optical properties seems to be needed and useful. The unique features of this book, which is a collection of 21 chapters written by world-recognized experts in the field, are the following: 1) for the first time in one book different noninvasive optical methods and techniques of glucose sensing are overviewed and analyzed; 2) the presence of chapters on basic research containing the updated results on coherent and polarization properties of light scattered by tissues and biological fluids containing glucose on physiological and hyperphysiological levels that allow for understanding optical techniques of glucose sensing and impact on tissue and blood optical properties; 3) discussion of the most recent prospective glucose-sensing methods based on light scattering, coherent-domain, polarization-sensitive, optoacoustic, and photonic crystal measurements; 4) new optical techniques for glucose diffusion coefficient measurements in human tissues; and 5) a description of glucose-induced optical clearing effects in tissue and blood. The book opens with a chapter that reviews data on glucose metabolism in physiological norm and pathology, shows the role of glucose in pathogenesis of diabetes mellitus and atherosclerosis, and describes problems of glucose sensing in clinical

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practice. The second chapter gives an introduction into commercial biosensors and the science behind their functionality. Chapter 3 describes the Monte Carlo method for statistical simulation of photons travelling in highly scattering tissues influenced by glucose. This chapter gives a theoretical background for a few basic optical techniques for glucose sensing, such as OCT, time-of-flight, spatial resolved reflectometry, frequency domain, and polarization sensitive. Two chapters, 4 and 5, describe methods that improve the accuracy of glucose prediction based on infrared absorption spectroscopy, such as a partial least squares regression loading vector analysis method; the method of minimization of spectral interference by other components; independent component analysis eliminating the calibration process; and the novel multivariate calibration method, the so-called “science based calibration” for estimating key performance parameters of multivariate spectrometric assays. Chapter 6 presents recent studies on the influence of acute hyperglycaemia on cerebral blood flow and spreading depression in rat cerebral cortex performed by the novel optical imaging techniques for investigating the cerebral hemodynamics, such as intrinsic optical signal imaging and laser speckle imaging. The correlation between diabetes and thermo-optical response of human skin is discussed in chapter 7. Both light absorption and scattering properties of human skin affected by temperature modulation and glucose concentration change are considered. Some systematic errors of the method, arising due to the contact between the skin and the measuring probe that causes changes in skin hydration and partial occlusion, are analyzed. In chapter 8 monitoring of glucose in skin by NIR diffusereflectance spectroscopy is provided. A fiber optical probe design enables selective optical signals from dermis tissue and reduces the interference noise arising from the stratum corneum. Authors carry out the partial least squares regression analysis for the NIR data and build calibration models for each subject individually. They are also developing new chemometrics algorithms and data pretreatment methods that are useful for blood glucose assays. Chapter 9 demonstrates correlation of glucose concentration with light scattering patterns. The author examines the light scattering related approach, the so-called “occlusion spectroscopy,” where light scattering fluctuations are associated with the red blood cells aggregation process which is triggered by artificial blood flow cessation and glucose concentration in blood. Due to the multiple-scattering in tissues that amplifies significantly scatter alterations, increased sensitivity to the changes in glucose concentration of blood plasma is found. NIR noninvasive blood glucose monitoring is discussed in chapter 10, regarding both methodological and instrumental implementations. The preliminary in vitro study of the suggested floating-reference method indicates the effect of the technique on improvement of the specificity of glucose signal extraction with the promising prospects for solving the interference caused by the variations of human physiological background. Fluorescence-based glucose biosensors are discussed in chapter 11, where the general requirements for using fluorescence spectroscopy for in vivo glucose monitoring are presented. Various fluorescence phenomena, glucose receptors, assays for glu-

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cose using these receptors, as well as the materials and methods for interfacing measurement instrumentation with receptors for construction of biosensors, are analyzed in this chapter. Chapter 12 discusses the application of quantitative Raman spectroscopy to biological tissue, in particular to glucose studies in blood serum, whole blood, and human subjects. Two new techniques, constrained regularization (CR) and intrinsic Raman spectroscopy (IRS), which are shown to significantly improve measurement accuracy, are presented. A photonic crystal contact lens sensor for the noninvasive determination of glucose in tear fluid is described in chapter 13. The drawbacks and advantages of tear fluid glucose studies in diabetic subjects are discussed. The authors present new measurements of tear glucose concentrations by using a method designed to avoid tear stimulation. On the basis of these results and recent studies on monitoring of tear glucose concentrations in situ by using contact lens-based sensing devices, the authors concluded that in vivo tear glucose sensing has a future. Chapter 14 gives an introduction into pulsed photoacoustic (PA) technique in its application to chemical trace measurement, including glucose determination in human blood and tissue. Discussion of in vitro studies of glucose determination in water, tissue phantoms, tissues, and blood, as well as in vivo studies of animals and human subjects, is presented. Major problems of PA glucose sensing are summarized together with discussion of future perspectives of the method. Three chapters, 15, 16, and 17, are devoted to discussion of problems and prospects of polarimetric glucose sensing in transparent (low scattering) and turbid (strongly scattering) tissues. In chapter 15, the reader will find a description of a noninvasive glucose sensor based on polarimetric measurements through the aqueous humor of the eye. The fundamentals of optical polarimetry and optical rotatory dispersion as a technique for monitoring chiral molecules are also presented. Sources of errors of optical measurements in the eye that include time lag between the blood and aqueous humor, corneal birefringence, and motion artefacts are analyzed. A multi-wavelength system having good prospects for in vivo measurements is described. In chapter 16 a new optical concept for measuring glucose concentration in the aqueous humor is discussed. The concept is based on reflecting the incident circularly polarized light from the ocular lens at the Brewster angle and by detecting and analyzing the linearly polarized light as it traverses through the eye’s anterior chamber. Chapter 17 shows prospects of noninvasive glucose sensing using polarization analysis of multiply scattered light. In this chapter a variety of experimental and theoretical tools of turbid polarimetry are described. Chapters 18 and 20 propose using a high-resolution optical technique, optical coherence tomography (OCT), for noninvasive, continuous, and accurate monitoring of blood glucose physiological concentration as well as for measurement of the diffusion of glucose and other related analytes in tissues. Major achievements in the development of this technique for noninvasive glucose monitoring from idea to successful clinical studies are presented in chapter 18. A description of recent progress made on developing a noninvasive molecular diffusion biosensor based on the OCT technique can be found in chapter 20. Due to the capability of the OCT technique

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for depth-resolved imaging of tissues with high in-depth resolution, glucose diffusion could be quantified not only as a function of time but also as a function of depth. Chapter 19 reviews the main experimental methods used for in vitro and in vivo measurements of glucose diffusion coefficients in human tissues, such as spectroscopic, photoacoustic, radioactive labeling, light scattering, including spectrophotometry, interferometry, and OCT. The last chapter (21) summarizes recent results on tissue and blood optical clearing effects induced by the action of glucose solutions. The refractive index concept as a main mechanism of optical clearing is discussed. Optical clearing properties of fibrous (eye sclera, skin dermis, and dura mater) and cell-structured tissues (liver, skin epidermis) are analyzed in the framework of tissue spectrophotometry, timeresolved, fluorescence, and polarization measurements, as well as usage of confocal microscopy, two-photon excitation imaging, and OCT. Results of in vitro, ex vivo, and in vivo studies of a variety of human and animal tissues and blood are presented. The audience at which this book is aimed is researchers, postgraduate and undergraduate students, laser engineers, biomedical engineers, and physicians who are interested in designing and applying noninvasive optical methods and instruments for glucose sensing and tissue optical clearing using glucose. Because of the large amount of basic research on light interactions with tissues and blood presented in the book it should be useful for a broad audience including students and physicians. Investigators who are strongly involved in the field will find updated results in any area discussed in the book. Physicians and biomedical engineers will be interested in clinical applications of designed techniques and instruments, which are described in some chapters. Optical engineers could be interested in the book, because their acquaintance with new fields of light applications can stimulate new ideas of optical instrumentation designing. This book represents a valuable contribution by well-known experts in the field of biomedical optics and biophotonics with their particular interest to the problem of glucose sensing and impact. The contributors are drawn from Russia, USA, UK, South Korea, Finland, Germany, China, Japan, Israel, Italy, and Canada. I greatly appreciate the cooperation and contributions of all authors of the book, who have done great work on preparation of their chapters. It should be mentioned that this book presents results of international collaborations and exchanges of ideas among all research groups participating in the book project. I would like to thank all those authors and publishers who freely granted permissions to reproduce their copyrighted works. I am grateful to Dr John Navas, senior editor, Physics, of Taylor & Francis/CRC Press, for his valuable suggestions and help on preparation of the manuscript and to Professor Vladimir L. Derbov, Saratov State University, for preparation of the camera-ready manuscript and help in technical editing of the book. I greatly appreciate the cooperation, contributions, and support of all my colleagues from the Optics and Biomedical Physics Division and Research-Educational Institute of Optics and Biophotonics of Physics Department of Saratov State University and the Institute of Precise Mechanics and Control of the Russian Academy of Science.

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Last, but not least, I express my gratitude to my family, especially to my wife Natalia and grandchildren Dasha, Zhenya, and Stepa, for their indispensable support, understanding, and patience during my writing and editing of the book. Valery V. Tuchin July 2008 Saratov, Russia

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[10] J.T. Bruulsema, J.E. Hayward, T.J. Farrell, M.S. Patterson, L. Heinemann, M. Berger, T. Koschinsky, J. Sandahal-Christiansen, H. Orskov, M. Essenpreis, G. Schmelzeisen-Redeker, and D. B¨ocker, “Correlation between blood glucose concentration in diabetics and noninvasively measured tissue optical scattering coefficient,” Opt. Lett., vol. 22, no. 3, 1997, pp. 190–192. [11] M. Kohl, M. Essenpreis, and M. Cope, “The influence of glucose concentration upon the transport of light in tissue-simulating phantoms,” Phys. Med. Biol., vol. 40, 1995, pp. 1267–1287. [12] L.D. Shvartsman and I. Fine, “Optical transmission of blood: effect of erythrocyte aggregation,” IEEE Trans. Biomed. Eng., vol. 50, 2003, pp. 1026– 1033. [13] O. Cohen, I. Fine, E. Monashkin, and A. Karasik, “Glucose correlation with light scattering patterns—a novel method for noninvasive glucose measurements,” Diabet. Technol. Ther., vol. 5, 2003, pp. 11–17. [14] C.D. Geddes and J.R. Lakowicz (eds.), Topics in Fluorescence Spectroscopy, vol. 11, Glucose Sensing, Springer, New York, 2006. [15] A.M.K. Enejder, T.G. Scecina, J. Oh, M. Hunter, W.-C. Shih, S. Sasic, G.L. Horowitz, and M. S. Feld, ”Raman spectroscopy for noninvasive glucose measurements,” J. Biomed. Opt., vol. 10, 2005, 031114. [16] M. Ren, M.A. Arnold, “Comparison of multivariate calibration models for glucose, urea, and lactate from near-infrared and Raman spectra,” Anal. Bioanal. Chem., vol. 387, 2007, pp. 879–888. [17] J.S. Baba, B.D. Cameron, S. Theru, and G.L. Cot´e, ”The effect of temperature, pH, and corneal birefringence on polarimetric glucose monitoring in the eye,” J. Biomed. Opt., vol. 7, 2002, pp. 321–328. [18] R.R. Ansari, S. Boeckle, and L. Rovati, “New optical scheme for a polarimetric-based glucose sensor,” J. Biomed. Opt., vol. 9, 2004, pp.1 03– 115. [19] X. Guo, M.F.G. Wood, and I.A. Vitkin, “Stokes polarimetry in multiply scattering chiral media: effects of experimental geometry,” Appl. Opt., vol. 46, 2007, pp. 4491–4500. [20] C. Chou, C.Y. Han, W.C. Kuo, Y.C. Huang, C.M. Feng, and J.C. Shyu, “Noninvasive glucose monitoring in vivo with an optical heterodyne polarimeter,” Appl. Opt., vol. 37, no.16, 1998, pp. 3553–3557. [21] V.L. Alexeev, S. Das, D.N. Finegold, and S.A. Asher, “Photonic crystal glucose-sensing material for noninvasive monitoring of glucose in tear fluid,” Clin. Chem., vol. 50, 2004, pp. 2353–2360. [22] H.A. MacKenzie, H.S. Ashton, S. Spiers, Y. Shen, S.S. Freeborn, J. Hannigan, J. Lindberg, and P. Rae, “Advances in photoacoustic noninvasive glucose testing,” Clin. Chem., vol. 45, 1999, pp. 1587–1595.

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[23] M. Kinnunen and R. Myllyl¨a, “Effect of glucose on photoacoustic signals at the wavelength of 1064 and 532 nm in pig blood and Intralipid,” J. Phys. D: Appl. Phys., vol. 38, 2005, pp. 2654–2661. [24] Glucon, Inc.: http://www.glucon.com/ [25] Y. Shen, Z. Lu, S. Spiers, H.A. MacKenzie, H.S. Ashton, J. Hannigan, S.S. Freeborn, and J. Lindberg, “Measurement of the optical absorption coefficient of a liquid by use of a time-resolved photoacoustic technique,” Appl. Opt., vol. 39, 2000, pp. 4007–4012. [26] R.O. Esenaliev, K.V. Larin, I.V. Larina, and M. Motamedi, “Noninvasive monitoring of glucose concentration with optical coherence tomography,” Opt. Let., vol. 26, no. 13, 2001, pp. 992–994. [27] K.V. Larin, M.S. Eledrisi, M. Motamedi, R.O. Esenaliev, “Noninvasive blood glucose monitoring with optical coherence tomography: a pilot study in human subjects,” Diabetes Care, vol. 25, no. 12, 2002, pp. 2263–2267. [28] K.V. Larin, M. Motamedi, T.V. Ashitkov, and R.O. Esenaliev, “Specificity of noninvasive blood glucose sensing using optical coherence tomography technique: a pilot study,” Phys. Med. Biol., vol. 48, 2003, pp. 1371–1390. [29] C.D. Malchoff, K. Shoukri, J.I. Landau, and J.M. Buchert, “A novel noninvasive blood glucose monitor,” Diabet. Care, vol. 25, 2002, pp. 2268–2275. [30] G. Mazarevica, T. Freivalds, and A. Jurka, “Properties of erythrocyte light refraction in diabetic patients,” J. Biomed. Opt., vol. 7, no. 2, 2002, pp. 244– 247. [31] V.V. Tuchin, R.K. Wang, E.I. Galanzha, N.A. Lakodina, and A.V. Solovieva, “Monitoring of glycated hemoglobin in a whole blood by refractive index measurement with OCT, Conference Program CLEO/QELS, Baltimore, June 1–6, 2003, p. 120. [32] A.K. Amerov, J. Chen, G.W. Small, and M.A. Arnold, “The influence of glucose upon the transport of light through whole blood,” Proc. SPIE 5330, 2004, pp. 101–111. [33] J. Qu and B.C. Wilson, “Monte Carlo modeling studies of the effect of physiological factors and other analytes on the determination of glucose concentration in vivo by near infrared optical absorption and scattering measurements,” J. Biomed. Opt., vol. 2, no. 3, 1997, pp. 319–325. [34] V.V. Tuchin, “Coherent optical techniques for the analysis of tissue structure and dynamics,” J. Biomed. Opt., vol. 4, 1999, pp. 106–124. [35] V.V. Tuchin, “Optical clearing of tissue and blood using immersion method,” J. Phys. D: Appl. Phys., vol. 38, 2005, pp. 2497–2518. [36] V.V. Tuchin, Optical Clearing in Tissues and Blood, SPIE Press, Bellingham, WA, 2005.

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[37] A.N. Bashkatov, E.A. Genina, Yu.P. Sinichkin, V.I. Kochubey, N.A. Lakodina, and V.V. Tuchin, “Glucose and manitol diffusion in human dura mater,“ Biophys. J., vol. 79, 2003, pp. 3310–3318. [38] M.G. Ghosn, V.V. Tuchin, and K.V. Larin, “Non-destructive quantification of analytes diffusion in cornea and sclera by using optical coherence tomography,” Invest. Ophthal. Vis. Sci., vol. 48, 2007, pp. 2726–2733.

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List of Contributors Rafat R. Ansari NASA Glenn Research Center, Cleveland, OH 44135, USA Sanford A. Asher Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, USA Justin T. Baca Department of Chemistry, University of Pittsburgh, Pittsburgh, PA 15260, USA Alexey N. Bashkatov Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia Kate L. Bechtel George R. Harrison Spectroscopy Laboratory Massachusetts Institute of Technology, Cambridge, MA 02139, USA Alexander V. Bykov Physics Department and International Laser Center, M.V. Lomonosov Moscow State University, Moscow, Russia, and Department of Technology, University of Oulu, Oulu, Finland Brent D. Cameron Texas A&M University, Department of Biomedical Engineering, College Station, TX 77843, USA Gerard L. Cot`e Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA

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Tatyana P. Denisova Saratov State Medical University Saratov, Russia Yi Ping Du Research and Analysis Center, East China University of Science and Technology, Shanghai 200237, China Rinat O. Esenaliev Laboratory for Optical Sensing and Monitoring, Center for Biomedical Engineeering, Department of Neuroscience and Cell Biology, and Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555-0456, USA Michael S. Feld George R. Harrison Spectroscopy Laboratory Massachusetts Institute of Technology, Cambridge, MA 02139, USA Ilya Fine Elfi-Tech Ltd., Science Park, Rehovot, Israel Vasiliki Fragkou Cranfield Health, Cranfield University, Silsoe, MK45 4D, UK Elina A. Genina Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia Nirmalya Ghosh Division of Biophysics and Bioimaging, Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto Toronto, Ontario, Canada Xinxin Guo Division of Biophysics and Bioimaging, Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto Toronto, Ontario, Canada H. Michael Heise ISAS - Institute for Analytical Sciences at the Technical University of Dortmund, Dortmund, 44139, Germany

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Sumaporn Kasemsumran Nondestructive Quality Evaluation Unit, Kasetsart Agricultural and Agro-Industrial Product Improvement Institute (KAPI), Kasetsart University, Bangkok 10900, Thailand Omar S. Khalil Pharos Biomedical Research, Chicago, IL, USA Matti Kinnunen Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland Mikhail Yu. Kirillin Physics Department and International Laser Center, M.V. Lomonosov Moscow State University, Moscow, Russia, and Department of Technology, University of Oulu, Oulu, Finland Peter Lampen ISAS - Institute for Analytical Sciences at the Technical University of Dortmund, Dortmund, 44139, Germany Kirill V. Larin Biomedical Engineering Program, University of Houston, Houston, TX 77204, USA; Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia Pengcheng Li Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, PR China Qingming Luo Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, PR China Weihua Luo Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, PR China

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Lidia I. Malinova Saratov Research Institute of Cardiology Saratov, Russia Ralf Marbach VTT Optical Instruments Centre, Oulu, Finland Katsuhiko Maruo Advanced Technologies Development Laboratory, Matsushita Electric Works Ltd., Kadoma, Osaka 571-8686, Japan M. McShane Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA Risto Myllyl¨a Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland Yukihiro Ozaki Department of Chemistry, School of Science and Technology, Kwansei-Gakuin University, Sanda 669-1337, Japan M.V. Pishko Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA Alexander V. Priezzhev Physics Department and International Laser Center, M.V. Lomonosov Moscow State University, Moscow, Russia, and Department of Technology, University of Oulu, Oulu, Finland Donald S. Prough Department of Anesthesiology, The University of Texas Medical Branch, Galveston, TX 77555-0591, USA Luigi Rovati Department of Information Engineering, University of Modena and Reggio Emilia, Modena, 41100 Italy

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Wei-Chuan Shih George R. Harrison Spectroscopy Laboratory Massachusetts Institute of Technology, Cambridge, MA 02139, USA Hideyuki Shinzawa Department of Chemistry, School of Science and Technology, Kwansei-Gakuin University, Sanda 669-1337, Japan Valery V. Tuchin Institute of Optics and Biophotonics, Saratov State University, Saratov, 410012, Russia, Institute of Precise Mechanics and Control of RAS, Saratov 410056, Russia Anthony P.F. Turner Cranfield Health, Cranfield University, Silsoe, MK45 4D, UK I. Alex Vitkin Division of Biophysics and Bioimaging, Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto Toronto, Ontario, Canada Ruikang Wang Department of Biomedical Engineering, Oregon Health & Science University Portland, OR 97239, USA Zhen Wang Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, PR China Michael F. G. Wood Division of Biophysics and Bioimaging, Ontario Cancer Institute and Department of Medical Biophysics, University of Toronto Toronto, Ontario, Canada Kexin Xu College of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin 300072, P.R. China

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Gilwon Yoon Seoul National University of Technology Nowon-gu Kongneung-dong Seoul, Korea Zuomin Zhao Department of Electrical and Information Engineering, University of Oulu, Oulu, Finland

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1 Glucose: Physiological Norm and Pathology

Lidia I. Malinova Saratov Research Institute of Cardiology, Saratov, Russia Tatyana P. Denisova Saratov State Medical University, Saratov, Russia 1.1 1.2 1.3 1.4 1.5 1.6

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . System of Blood Glucose Level Regulation and Carbohydrate Metabolism . Glucose and Carbohydrate Metabolism Violations . . . . . . . . . . . . . . . . . . . . . . . . . Blood Glucose Level Monitoring in Clinical Practice . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 4 14 21 24 25 26

In the present chapter the physiology and clinical applications of glucose metabolism are going to be discussed. Glucose is one of the central substances in human life supporting processes. Glucose distribution and concentration in blood, interstitial fluid, and tissues, kinetics and mechanisms of transcapillary glucose transport, kinetics and metabolism of glucose transport via its transporters into cells, detailed mechanisms of glucose level influence upon clinical course and prognosis in pathology are still in the area of uncertainty. Morbidity and mortality via diabetes mellitus and atherosclerosis are still increasing, enhancing attention of clinicians and biophysics to the problem of glucose sensing and impact. The purpose of this chapter is to review the data on glucose metabolism in physiological norm and pathology, the role of glucose in pathogenesis of diabetes mellitus and atherosclerosis and the glucose sensing in clinical practice. The described problems form three parts of the chapter. Key words: glucose, metabolism, diabetes mellitus, atherosclerosis, glucose level monitoring.

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1.1 Introduction 1.1.1 Goal Health and happiness of a human being were the main aim and a golden dream for scientists over the centuries. Attempts to reach this top resulted in thorough investigating of human physiology and pathology. Unfortunately unsolved problems are prevailing over solved ones even (especially!) nowadays. The 21st century meets us with an explosion in frequency of such a merciless disease as insulin dependent and non-insulin dependent diabetes mellitus. Coronary atherosclerosis becomes more “young” and a frequent form of pathology. The elderly population significantly increases in many countries all over the world. All listed above problems are overlapping. One of possible “joins” is well known and still a mysterious compound — glucose. Further investigations in this area are impossible without improvement of scientific research instruments. The ongoing discussions on the state-of-the-art of glucose sensors, the most prospective glucose-sensing methods, new optical techniques for glucose diffusion coefficient measurements, and glucose-induced optical clearing effects in tissues and blood demand clear understanding of glucose metabolism, distribution and concentrations in human organism compartments. So, the present chapter is dedicated to the problem of glucose’s role in life supporting processes both under physiological norm conditions and in pathology. We’ll review the most actual concepts on glucose transport, metabolic pathways and crosses, main glucose level regulation mechanisms in practically healthy persons and in patients with diabetes mellitus and coronary heart disease. Another point of interest to discuss in this chapter is clinical applications of glucose sensing and main problems in that field. Thus, the main goal of the present part is to form physiological and clinical grounds of the handbook, to reveal the urgency of cooperation of clinicians and physicists in the described area.

1.1.2 Terms and definitions Glucose, a monosaccharide, is the central carbohydrate in human physiology. The ´ ), which means “sweet.” There name comes from the Greek word “glykys” (γλ υκ υς are two optical isomers of glucose, D- and L-glucose (Fig. 1.1), and both of them are optically active with the opposite chirality (Fig. 1.2). However, only D-glucose is involved in human metabolism. The mirror-image of the molecule, L-glucose, is present in food but cannot be used by mammalian cells [1]. Hereinafter the term “glucose” will mean its biological active isoform (D-isoform). The central place of glucose in human metabolism is caused by its plural functions. Glucose is the transport form of carbohydrate, and important fuel for stoking metabolic energy in all human cells. Glucose is participating in a large amount of biochemical reactions modulating metabolic profile both at physiological norm and in pathology. Glucose role is critical in the production of proteins and in lipid

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FIGURE 1.1: D and L isoforms of glucose.

FIGURE 1.2: Polarimetry in study of carbohydrate: (A) - polarization planes of both linear polarizers placed before (polarizer) and after (analyzer) measuring cell with water are collinear; water is optically nonactive medium. Thus, polarized light freely passes through the analyzer (bright field of view); (B) - D- or L-glucose isoforms rotate the plane of light polarization; thus, polarized light is attenuated by the analyzer that is in parallel with the polarizer plane (darken field of view); (C) rotation of analyzer plane allows one to make field of view bright; measured polarizer rotation angle is equal to glucose rotation angle α .

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metabolism. According to metabolic theory glucose metabolism is involved in aging processes. Blood glucose level is a parameter of a high predictive value in such disorders as diabetes mellitus, coronary heart disease and arterial hypertension. Details are going to be discussed in other parts of the chapter.

1.2 System of Blood Glucose Level Regulation and Carbohydrate Metabolism Glucose metabolism presents a complex net of biological substances interaction. The result of these interactions is a relatively constant blood glucose level. The last one does not belong to strict biological constants. It means that blood glucose level may vary in the physiological range. All biological agents influencing upon carbohydrate metabolism, humans’ feed behavior and the very glucose concentration (as the result) form the functional system of blood glucose level regulation. Plural possible disorders in this system are nearly connected and form the pathophysiology sense of several forms of the internal pathology. Let’s consider glucose metabolism in norm. The initial step in glucose metabolism is the hexose transport across the cell membrane down a normally large concentration gradient from extracellular fluid to cytoplasm. This process is carried out by a family of glucose transport proteins.

1.2.1 Glucose transporters There are two categories of glucose transporters: Na+ dependent (SGLT) and Na+ independent (GLUT). SGLT glucose transport is coupled to Na+ transport and permits the glucose to cross the cell membrane against its concentration gradient. When the loss of glucose from isolated intestinal epithelial cells is prevented by blocking GLUTs, the cell can concentrate glucose, taken via its SGLT. The main location of SGLTs is the brush border of intestinal and proximal renal tubular cells. SGLT-1 cycles between the interior and the plasma membrane [2]. GLUTs are distributed widely in human organisms, although varying in density of localization. Each transport protein spans the plasma membrane 12 times and both its amino and carboxy termini are located within the cytoplasm. Glucose molecules move across a cell membrane via GLUT in the direction of glucose concentration gradient. There may be energy barriers in GLUTs that glucose molecules must overcome in connection with one or more association-dissociation steps. To date, 13 functional mammalian facilitated hexose carriers (GLUTs) have been detected in mammals. They were divided into three classes according to the sequence similarities. Class I GLUTs include the high-affinity binding proteins GLUT1, GLUT3, and GLUT4 and the lower-affinity transporter GLUT2. Class II transporters includes GLUT5, GLUT7, GLUT9, and GLUT11 or myoinositol transporter (HMIT1).

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Transporters of this class have a very low affinity for glucose and preferentially transport fructose, and thus they are not discussed in this review. Class III transporters comprise four novel GLUTs, GLUT6, GLUT8, GLUT10, and GLUT12 [3]. The GLUT1 transporter is expressed constitutively and is responsible for the low level of basal glucose uptake alone or in company with other GLUT isoforms. This transporter expression is increased by fasting and decreased by glycemia. GLUT1 is located mainly in pancreatic β -cells. GLUT2 is expressed by hepatic and renal tubular cells that transfer glucose level out of the cell into extracellular fluid and plasma and by small intestinal epithelial cells that transport glucose from the gut lumen into the plasma. GLUT2 is also present in insulin secreting cells of the pancreatic islets, where it maintains a virtual equilibrium between the glucose concentrations in the extracellular liquid and cytoplasm. GLUT3 is a high affinity glucose transporter expressed in neurons and the placenta. GLUT4 is the major glucose transporter in insulin sensitive cells, in which insulin increases the glucose uptake. The GLUT4 transporter is expressed exclusively in cardiac and skeletal muscle and adipose tissue. In skeletal muscle there may be no GLUT4 in the plasma membrane in the basal state. GLUT4 and possibly GLUT1 in the plasma membrane exist in at least two stable configurations, and they can change them, and, because of that, change their ability to transport glucose in response to predominantly insulin concentration. GLUT7 has been localized at hepatocyte endoplasmic reticulum. GLUT7 transports newly produced glucose out of endoplasmic reticulum lumen into cytoplasm, from which it leaves the cell via GLUT2. Less is known about the Class III transporter proteins. They exhibit tissue- and cell-specific expression patterns and demonstrate preferential transport of glucose similar to class I transporters. GLUT6 (previously named GLUT9) is predominantly expressed in spleen, leukocytes, and brain [4]. In humans, GLUT8 (previously named GLUTX1) is predominantly found in insulin-sensitive tissues such as muscle, fat, and liver, as well as in testis, and is inhibited by fructose [5]. GLUT10 is predominantly expressed in insulin-sensitive tissues (liver, muscle, and pancreas), but is also found in lung, brain, placenta, and kidney [6]. GLUT12 is predominantly expressed in skeletal muscle, heart, fat, and prostate. In the absence of insulin, this receptor is found in a perinuclear location [7]. To date, there are no known human or animal diseases associated with alterations in either protein structure or expression of the class III transporters.

1.2.2 Pathways of glucose concentration change: glucose distribution and concentrations in human organism Plasma glucose concentration is a result of balance between glucose entering the circulation and glucose removal from the circulation. Basal plasma glucose level is tightly regulated around 80 mg/dl (4.5 µ mol/L), with a range of 60 to 110 mg/dl [8]. There are three main blood glucose sources: intestinal absorption during the meal, glycogenolysis, and gluconeogenesis (Fig. 1.3). The first one component is predominantly determined by the digestive tract function, i.e., the rate of gastric emptying. Two peptides decrease the rate of gastric emptying: glucagon-like polypeptide-1

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FIGURE 1.3: Glucose fate in human organism. Comments are in the text.

(GLP-1) [9, 10] and amylin [11]. Other sources of circulating glucose are derived chiefly from hepatic processes: glycogenolysis (the breakdown of glycogen), and gluconeogenesis (the formation of glucose primarily from lactate and amino acids during the fasting state). Glycogenolysis and gluconeogenesis are united in terms of endogenous glucose production (EGP). EGP by the splanchnic bed is about 0.62 mmol min−1 on 1.73 m2 (body surface)−1 [12], by the kidney 0.07 mmol min−1 on 1.73 m2 (body surface)−1, and total production 0.69 mmol min−1 on 1.73 m2 (body surface)−1 [13]. EGP is partly under the control of glucagon [14]. Glucagon is a key catabolic hormone secreted from pancreatic α -cells. Described by Roger Unger in the 1950s, glucagon was characterized as opposing the effects of insulin. Glucagon facilitates glycogenolysis during the first 8-12 hours of fasting and thus promotes glucose appearance in the circulation. Over longer periods of fasting, glucose is produced by gluconeogenesis primarily in the liver. In the presence of constant basal insulin concentrations TGP is regulated by rapid and reciprocal changes in glucose within the physiological range [15]. Glucose disappearance is the result of its consumption and use by peripheral tissues (Fig. 1.3). Approximately 55% of glucose use results from terminal oxidation. Another 20% results from glycolysis; the resulting lactate then returns to the liver for resynthesis into glucose (Cori cycle). Reuptake by the liver and other splanchnic tissues accounts for the remaining 25% of glucose use. Glucose uptake by skeletal muscle would be 0.12 mmol min−1 [17], by heart 0.08 mmol min−1 [16] per 1.73 m2 (body surface)−1, and the total glucose uptake with brain tissue participation will be ∼ 90% of the reported glucose production. At real conditions glucose is not oxidized completely. The circulating pool of glucose in one hour of fasting is only slightly

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FIGURE 1.4: Glucose turnover: the balance of glycogen. Comments are in the text.

larger than the liver output. This concentration is hardly sufficient to maintain brain oxidation for several hours in absence of any other glucose sources. Hepatic uptake and use of circulating lactate account for more than half the glucose supplied by gluconeogenesis. The remainder is largely accounted for by amino acids, especially alanine. Glycolysis in muscle, red blood cells, white blood cells, and a few other tissues provides main lactate supply of these processes (Fig. 1.4). The amino acids precursors come from proteolysis of muscle. The only amino acids precursors increase does not result in the “explosion” of gluconeogenesis rate. The necessary enzymes must be upregulated by hormonal modulation or by hepatic autoregulation responses to the failing glucose level. Glycolysis, oxidation, and storage as glycogen are the main but not the only pathways of glucose metabolism. The pentose phosphate pathway or hexose monophosphate chunt is active in several tissues. When glucose concentrations are high, the enzyme aldose reductase reduces glucose to sorbitol, which can subsequently be oxidized to fructose (polyol pathway). The major products of glycolysis, lactate and piruvate, circulate at average concentrations of 0.7 and 0.07 mM, respectively. This 10:1 ratio of lactate to piruvate ordinarily prevails as long as oxygen is plentiful. Oxidation of glucose uptake by the beating heart accounts for only 35% of observed O2 uptake [15]. Unlike skeletal muscle, the heart takes up lactate and can use it as substrate for oxidation. This still leaves most of the O2 uptake to be accounted for by FFA [16]. In fact little or none of the glucose taken up by muscle in the basal state is stored as glycogen [17], a fact demonstrated by nuclear magnetic resonance spectroscopy [18].

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FIGURE 1.5: Simplified scheme of blood glucose level regulation. Comments are in the text.

1.2.3 Regulation of glucose metabolism: main pathways and processes Regulation of glucose metabolism is still in the area of uncertainty. One of the accepted hypotheses still of current importance is the food and famine hypothesis, which considers effects of meals and periods between them on blood glucose concentration and glucose uptake and output [19]. The hypothesis deals with three time periods: prandial and immediate postprandial, delayed postprandial, and remote postprandial periods. During these periods the reciprocal relations between glucoregulatory hormones have taken place. Glucoregulatory hormones include insulin, glucagon, amylin, GLP-1, glucosedependent insulinotropic peptide (GIP), epinephrine, cortisol, and growth hormone. Of these, insulin and amylin are derived from the β -cells, glucagon from the α -cells of the pancreas, and GLP-1 and GIP from the L-cells of the intestine (Fig. 1.5). Amylin is a neuroendocrine hormone expressed and secreted with insulin as a response to nutrient stimuli [20–23]. In healthy adults, fasting plasma amylin concentrations range from 4 to 8 pmol/l rising as high as 25 pmol/l postprandially. In diabetic patients, amylin is deficient (IDDM) or impaired (NIDDM) [24, 25]. Amylin’s main task is to prevent an abnormal rise in glucose concentrations via two main mechanisms: suppression of postprandial glucagon secretion [26], and inhibition of the rate of gastric emptying [27]. GIP stimulates insulin secretion and regulates fat metabolism, but does not inhibit glucagon secretion or gastric emptying [28]. GLP-1 stimulates insulin secretion in the pancreas [29, 30]. Artificial infusion of GLP-1 results in decrease of postprandial glucose as well as fasting blood glucose concentrations. GLP-1 helps to regulate

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gastric emptying and gastric acid secretion [31]. GH “violates” insulin-stimulated glucose uptake. Froesch with coauthors described that IGF-I has an “insulin-sparing” effect. IGF-I is lipolytic and enhances GH in its prevention of protein catabolism [32]. Exposure to increasing concentrations of beta-adrenergic agonists and glucagon in the intermediate and late postprandial period causes glycogenolysis and gluconeogenesis. The basal state is a quite late postprandial period in which glucose metabolic quasi-stability is maintained. Most of glucose use (about 70%) in the basal state is independent of insulin for insulin and GH blood levels are at their lowest concentration of the 24-h period [33, 34]. In a subject in the basal state, glucose turnover is about 2 mg/kg/min (11 µ mol/kg/min), and a slow decline of blood glucose concentration, by less than 1% per hour, has taken place. The next period is a prandial state. Glucose in the gut is absorbed mainly in the jejunum and ileum, by two routes: transcellular and paracellular. The paracellular route includes absorption across the tight junction between enterocytes and lateral clefts between cells. The transcellular route is via SGLT and GLUT-2. An increase in dietary carbohydrate increases glucose absorption from small intestinal segments via an unknown path that leads to an increase in the number of SGLT in the brushborder membrane [35, 36]. High concentration of glucose in the gut lumen also increases paracellular glucose transport possibly by a cascade of signals from brushborder SGLT [37]. Translocation through GLUT-2 in the basolateral membrane is increased by hyperglycemia within 30 min [38]. When an individual ingests glucose after overnight fasting, a significant proportion of the load is assimilated by peripheral tissues, mainly muscle, and the rest by the splanchnic tissues, mainly liver. Only from 20% to 30% of a glucose load is oxidized during 3 to 5 hours required to its absorption from the gastrointestinal tract. The remaining glucose is stored as glycogen, partly in liver. Glucose initially stored as muscle glycogen can later be transferred to the liver by undergoing glycolysis to lactate, which is released into the circulation. The lactate is then taken up by the liver, rebuilt into glucose, and stored as glycogen in that organ. During the period of peak absorption of exogenous glucose, hepatic output of the sugar is largely unnecessary and is greatly reduced from the basal levels. Hyperglycemia declines as glucose is transferred from interstitial fluid into cells. There are two components to this increased glucose uptake: an insulin-dependent and an insulin-independent component [39, 40]. Increased glucose uptake has to occur with hyperglycemia, without intervention of hormones due to glucose concentration gradient and GLUTs saturation degree. There are only two tissues that do not increase glucose uptake in response only to increased glucose delivery: brain and, in humans, skeletal muscle [41]. 1.2.3.1 Free fatty acids role in glucose metabolism regulation Glucose metabolism is interlaced with lipid metabolism pathways, i.e., free fatty acids (FFA). Randle et al. [42] based on the animal model proposed that circulating FFA led to increased FFA uptake, followed by t decreased glucose oxidation, and

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the citrate inhibition of phosphofructokinase, thereby blocking glycolysis [43]. This process was considered to proceed independently of any hormonal control. Support of Randle’s hypothesis in humans comes from input-output studies of legs in healthy young adults with analyses of biopsied muscle and euglycemic insulin clamps [44]. When FFA concentration was kept constant during hyperinsulinemia there was less glucose uptake, less glycogen store, reduced leg respiratory quotient, and reduced muscle pyruvate dehydrogenase activity. The major factor in insulin’s indirect suppression of EGP is decreased plasma FFA concentration, due to the antilipolytic effect of insulin on adipose tissue. There is experimental evidence that increased peripheral insulin concentration causes a small decrease in hepatic uptake of gluconeogenic substrate, prevented by fatty acid infusion [45]. Whether most of insulin induced suppression is direct or indirect is disputable. Bergman’s group [46] had shown it was indirect, whereas Cherrington lab [45] calculates that most of the effect is directly on the liver via the hepatic insulin receptor. 1.2.3.2 Physical activity and glucose metabolism Glucose metabolism intensity is the result of the physical activity degree in the individual. Muscle glucose uptake during or immediately after muscle exercise increased at least in part due to increased plasma membrane GLUT4 [47]. Exercise has been reported to increase sensitivity to insulin. Some researchers had shown that there is a factor in serum, probably a protein, which interacts with exercise to increase glucose [48]. There are no reports of an acute effect of physical activity on adipocyte glucose uptake and plasma membrane GLUT density [49]. 1.2.3.3 Oxygen and glucose metabolism Another factor to be a modulator of glucose metabolism is hypoxia. It has been known at least since 1958 that hypoxia stimulates glucose uptake by skeletal muscle [42]. Lasting hypoxia causes an inhibition of oxidative phosphorylation leading to significant increases in glucose transport. Hypoxia increases blood-brain barrier transport of glucose and binding of cytochalasin B to GLUT1 in cerebral microvessels [50]. Hypoxia also modulates glucose transport in human fetal and bovine aortic endothelial cells [51]. Hypoxia activates glucose transport during several hours. This process is associated with an increased expression of GLUT1 protein and mRNA. In some researchers’ opinion fat oxidative metabolism might serve an important signal for adaptive responses in endothelial cells to hypoxia [51]. In the absence of glucose, coronary microvascular endotheial cells become markedly sensitive to hypoxia [52].

1.2.4 Insulin: the key hormone of glucose metabolism Insulin is a key anabolic hormone (Fig. 1.6), which exerts its actions through binding to specific receptors present mainly on fat, liver, and muscle cells. Insulin takes part in postprandial glucose level control in three ways. The first one, insulin, promotes glucose uptake by insulin-sensitive peripheral tissues, primarily skeletal

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FIGURE 1.6: Anabolic role of insulin. Comments are in text.

muscle. Then insulin stimulates glycogenesis in liver and simultaneously inhibits glucagon secretion from pancreatic α -cells and ends endogenous glucose production by liver. Insulin action is carefully regulated by circulating glucose concentrations. Insulin is not secreted at all if the blood glucose concentration is ≤ 3.3 mmol/l. The secretion of insulin occurs in two phases: an initial rapid release of preformed insulin, and increased insulin synthesis and release. Long-term release of insulin occurs if glucose concentrations remain high [53, 54]. With glucose concentration decrease plasma insulin concentration continues to fall to the upper range of normal basal and then falls slowly during the intermediate and late postprandial period. During the rising and peak portions of the blood insulin concentration curve, blood levels of GH decrease, even to immeasurably small concentrations. GH, glucagon, betaadrenergic agonists, and cortisol are released into the circulation at some point on the downlimb of falling blood glucose concentration (∼ 1.6 mM below the subject’s basal level or less) [55]. These agents act to prevent insulin-induced hypoglycemia [53]. When hyperinsulinemia becomes sufficiently high, the insulin-receptor complex in insulin sensitive cells (adipocytes, myocardiocytes, and skeletal muscle cells) initiates a transduction chain that results in translocation of intracellular vesicles to fuse with the plasma membrane. While glucose is the most potent stimulus of insulin, other factors stimulate insulin secretion. These additional stimuli of insulin secretion and release include increased plasma concentrations of some amino acids, especially arginine, leucine, and lysine; GLP-1 and GIP released from the gut following a meal; and parasympathetic stimulation via the vagus nerve. The first stimuli for insulin release from pancreatic β -cells are a family of peptides, incretins, GLP-

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1, glucagon, vasoactive intestinal peptide, and pituitary adenylate cyclase-activating peptide. Incretins are released into the circulation in response to carbohydrate and long-chain fatty acids in the gut. Insulin decreases or prevents net release of glucose from the liver, and perhaps the kidney, by two mechanisms, direct and indirect. Direct effects of insulin on liver and kidney are exerted by way of insulin actions on the insulin receptor, initiating transduction cascades that lead to increased glycogen synthase activity and decreased glycogenolysis [56]. Increased systemic insulin concentration, in the estimated absence of increased portal venous insulin concentration, suppressed hepatic glucose output [57]. The phenomenon was confirmed and demonstrated convincingly in humans by a series of reports mainly from the laboratories of Giacca [58], Bergman [46], and Cherrington [59]. A contributory factor is insulin inhibition of glucagon release from the pancreas. Insulin stimulates much greater multiples of increase in skeletal muscle glucose uptake in the intact subject than in excised muscle or its membrane fractions. 1.2.4.1 Nonglycemical insulin activities Blood glucose level changes, associated with plasma insulin concentration increase and decrease. Besides its glycemic properties discussed above insulin possesses several nonglycemical ones. For instance, insulin can increase blood flow in skeletal muscle, and whether this increased flow is an additional cause of increased glucose uptake is controversial [60]. Baron et al. have a series of reports that insulin, administered by continuous intravenous infusion for hours, increases leg blood flow [61, 62]. Deussen studied heterogeneity of blood flow and glucose uptake in the dog heart and concluded that variations in local blood flow cannot explain the differences in local glucose uptake [63]. Insulin stimulates nitric oxide (NO) synthase in vascular endothelium [64, 65] and therefore is capable of vasodilating. One of the possible mechanisms is stimulation of NO synthase in endothelium. The more obvious this fact becomes in the light of experiments demonstrated that administration of N-monomethyl-L-arginine, a competitive inhibitor of NO synthase, prevents increased blood flow otherwise seen with intravenous insulin after several hours. This fact remains disputable due to some experiments in which researchers could not detect an insulin effect on the relative dispersion of blood flow [66, 67]. A NO synthase (NOS) inhibitor decreased basal and exercise-stimulated glucose uptake but had no effect on insulin-stimulated glucose uptake. Addition of the NO donor, Na nitroprusside, increased basal glucose uptake; this effect was additive to the effects of submaximal and maximal insulin concentrations [68]. A role for NO as a modulator of glucose transport in skeletal muscle has been documented, with GLUT4 implicated in the four- to fivefold increase in basal glucose transport mediated by sodium nitroprusside [69]. In contrast, myocardial glucose uptake is increased in the presence of NO syntetase inhibitor L-NAME and in preparation isolated from eNOS knok-out mice, suggesting that NO may in some vascular beds down regulate glucose uptake via cGMP-dependent mechanism [70]. The role NO plays in physiological insulin secretion has been controversial. The evidence that

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exogenous NO stimulates insulin secretion and that endogenous NO production occurs and is involved in the regulation of insulin release was received in animal model experiments. Applied NO is able to exert an insulinotropic effect, and implicated endogenously produced NO in the physiological regulation of insulin release. When the insulin concentration is high and glucagon and epinephrine concentrations are low, hepatic and renal glycogenolysis and gluconeogenesis cease. Whereas in the basal state little or none of skeletal muscle glucose uptake can be accounted for by glycogen deposition, in response to insulin ≤70% of glucose uptake is accounted for by an increase in muscle glycogen [71, 72].

1.2.5 Endothelium and glucose metabolism Endothelium, the internal vessel layer, may be defined as a special organ with powerful synthetic activity. Endothelial functional state determines one of the main life supporting processes in human organism - tissue perfusion. Glucose is actively metabolized in endothelial cells [73] and sustains anaerobic and aerobic metabolism [52, 74]. In the presence of 5 mM D-glucose, catabolism of aminoacids, palmitate, and lactate is reduced significantly. In experimental rat models it was shown that, in coronary microvascular endothelial cells, < 98% of incorporated glucose is metabolized to lactate [74]. At physiological glucose levels in microvascular endothelial cells almost all of the energy obtained from glucose metabolism obtained from catabolism of glucose is generated glycolytically. At lower glucose concentration (∼1 mM), the oxidation of glucose via Krebs cycle is higher. Thus oxidative metabolism of glucose is inhibited at physiological concentrations of glucose, demonstrating that endothelial cells express the Grabtree effect (i.e., an inhibitory effect of glucose on mitochondrial respiration). Endothelial cells are of high glycolitic activity [75, 76]. The effects of elevated glucose level on endothelial cell are often specific [77-79]. Elevated glucose also increases the generation of superoxide anions known to react with NO to form peroxinitrite, which upon decomposition generates a strong oxidant with reactivity similar to hydroxyl radicals [80]. Human endothelial cells exposed to hyperglycemia (established diabetes mellitus) are more sensitive to reactive oxygen species, since intracellular levels of glutathione, vitamin E, superoxide dismutase, catalase, and ascorbic acid are reduced significantly [81, 82]. Transport of glucose analogs has been characterized in cultured endothelial cells isolated from bovine aorta and a bovine aortic endothelial cell line GM7373 [51, 77, 83, 84]. The absence of GLUT isoforms in pulmonary endothelial cells suggests that pulmonary vessels may have a low requirement of glucose. Bradikinin apparently stimulates glucose uptake by the coronary microcirculation of the rat isolated perfused heart [85]. In cultured coronary microvascular endothelial cells, activation of H1 receptors by histamine stimulates glucose transport (∼ 10–50%), reaching a maximum after 5 min of histamine application. GLUT1 mRNA and protein was detected in these microvascular cells, suggesting that acute stimulation of glucose transport by histamine may involve modulation of GLUT1 expression and/or activity. Endothelial dysfunction has been implicated in the pathogenesis of diabetic vascular disorders such as di-

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abetic retinopathy. The study of Schmetterer and colleagues indicated that either NO-syntase activity was increased or NO sensitivity was decreased in patients with IDDM and supported the concept of an involvement of the L-arginine-NO system in the pathophysiology of diabetic retinopathy [86]. Capillary endothelial cells are thought to limit the transport of insulin from the vascular to the interstitial space, resulting in attenuated hormonal action at target sites. It was shown that capillary endothelial cells affect the transcapillary transport of insulin by slowing the transfer to interstitium. Insulin is transported by a bidirectional convective transport rather than by a saturable receptor-mediated mechanism. Endothelium-derived NO is without effect on transcapilary insulin transport in rat heart model [87].

1.3 Glucose and Carbohydrate Metabolism Violations Carbohydrate metabolism violations are very common in clinical practice. They have taken place in such widespread internal pathologies, as diabetes mellitus, coronary heart disease, and arterial hypertension. In this review we’ll focus only on the part of the problem — the role of glucose in pathogenesis and clinical course of diabetes mellitus (reference carbohydrate metabolism disorder) and coronary heart disease (patients whose carbohydrate metabolic state contribution on disease pathogenesis are still controversial).

1.3.1 Diabetes mellitus: glucose — victim or culprit? Diabetes mellitus (DM) is impaired insulin secretion and variable degrees of peripheral insulin resistance leading to hyperglycemia. There are two main categories of diabetes mellitus (DM). Type 1 (Insulin Dependent Diabetes Mellitus - IDDM) and type 2 (Non Insulin Dependent Diabetes Mellitus - NIDDM) differ one from another by etiology, several pathogenesis pathways, and clinical course. In the sequel we will focus our attention predominantly on NIDDM. Glucose concentration measurement is the major diagnostic criterion of diabetes (Table 1.1). The pathophysiology of NIDDM includes impairments in both insulin action and insulin secretion [88–90]. In insulin-resistant conditions, glucose clearance in response to insulin decreases. The most conclusive evidence for defective insulin sensitivity in type 2 diabetes comes from euglycemic hyperinsulinemic clamp studies, in which total body glucose clearance is shown to be reduced in NIDDM patients compared with age and weight-matched controls [91]. Alterations in insulin “physiology” are not the only component in DM pathogenesis. R.H. Unger was the first to describe the diabetic state as a “bi-hormonal” disease characterized by insulin deficiency and glucagon excess [92]. He further speculated that a therapy targeting the correction of glucagon excess would offer an important advancement in the treat-

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TABLE 1.1: Glucose concentrations as diagnostic criteria of diabetes Test FPG OGTT

Normal IGR DM 0, and, in the contrary case, µtr asymptotically grows, while transmission declines. The transition from the first kind of behavior (i.e., monotonic) to the non-monotonic one happens on wavelengths λcr satisfying the equation ρas = ρmin,max , where the symbol ρmin,max corresponds to the extreme points of function K(ρ ). At these wavelengths, the first order term in Eq. (9.34) disappears and the resulting dependence becomes very “flat” and all the time dependencies ultimately saturate. For the standard set of RBC parameters, (i.e., a ∼ 4 µ m, ∆n ∼ 0.07 ÷ 0.074), one can expect λcr corresponding to the minimum of K(ρ ) to be around 725–767 nm. As it has been shown experimentally, this is very close to what has been observed. While approaching the asymptotic limit the λcr can be found. λcr is directly proportional to the mismatch of refractive index; however, for this end a very precise spectrophotometer measurement has to be performed.

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712nm 727nm 735nm 750nm 760nm

C ritical wavelenght 1.000

T ransmission (A. U.)

0.9 9 5

0.9 9 0

0.9 85

0.9 80

0.9 75

0.9 70 0

10

20

30

4 0

50

60

70

80

T ime (in sec)

FIGURE 9.24: Transmission changes in close proximity to the critical wavelength point.

9.7.4 Effect of glucose on the light transmission for very long aggregates As shown [1–4], detailed behavior of transparency as a function of time contains the information on the volume fraction of scatterers (hematocrit), the mismatch of refraction indexes (glucose), and the average absorption of whole blood (oxygen saturation). Across the whole range of possible physiological changes of glucose (0 mg/dl< G 300 nm in diameter) exhibit mainly forward scattering. Whereas melanin dust whose particles are small (< 30 nm in diameter) has the isotropy in the scattering profile, and optical properties of the melanin particles (30–300 nm in diameter) may be predicted by the Mie theory. Absorption of hemoglobin and water in skin dermis and lipids in skin epidermis define absorption properties of whole skin. It should be noted that absorption of hemoglobin is defined by the hemoglobin oxygen saturation, since absorption coefficients of hemoglobin are different for oxy and deoxy forms. For an adult the arterial oxygen saturation is generally above 95% [81]. Typical venous oxygen saturation is 60–70% [82]. Thus, absorption properties of skin in the visible spectral range depend on absorption of both oxy- and deoxyhemoglobin. In the IR spectral range absorption properties of skin dermis depend on absorption of water. To design the optical model of fibrous tissue, in addition to form, size and density of the scatterers (collagen fibrils) and the tissue thickness, we are able to have information on the refractive indices of the tissue components. In the visible and near infrared spectral ranges, the refractive index of collagen fibrils and interstitial fluid (ISF) of human tissues has a weak dispersion and, thus, in a first approximation, can be used as a constant. The experimental mean values of refractive indices of tissues, blood and their compounds, measured in vitro and in vivo, have been presented [4, 65, 83].

21.2.3 Optical model of fibrous tissue The optical model of fibrous tissue can be presented as a slab with a thickness l containing scatterers (collagen fibrils) – thin dielectric cylinders with an average diameter of 100 nm, which is considerably smaller than their lengths. Taking into account the similar structure of the dura mater, eye sclera and skin dermis, we can assume that the refractive index of the collagen fibrils (nc ) and ISF (nI ) has the

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similar wavelength dependence in the visible spectral range for all fibrous tissues [60]. nc (λ ) = 1.439 +

15880.4 1.48 × 109 4.39 × 1013 − + λ2 λ4 λ6

(21.1)

nI (λ ) = 1.351 +

2134.2 5.79 × 108 8.15 × 1013 + − , λ2 λ4 λ6

(21.2)

and

where λ is the wavelength, nm. These cylinders are located in planes that are in parallel to the sample surfaces, but within each plane their orientations are random. These simplifications reduce considerably the difficulties in the description of the light scattering by fibrous tissue. For a thin dielectric cylinder in the Rayleigh-Gans approximation of the Mie scattering theory the scattering cross-section σs (t) for unpolarized incident light is given by [84, 85]
2 2 π 2 ax3 2 1+ m −1 σs = 2 2 8 (m + 1)

!

,

(21.3)

where m = nc /nI is the relative refractive index of the scattering particle, i.e., ratio of the refractive indices of the scatterers and the ground materials (i.e., ISF), and x is the dimensionless relative scatterers size which is determined as x = 2π anI /λ , where λ is the wavelength and a is the cylinder radius. Considerable refractive indices mismatching between collagen fibers and a ground substance causes the system to become turbid, i.e., causes multiple scattering and poor transmittance of propagating light. The refractive index of the background is a controlled parameter and may transit the system from multiple to low-step and even single-scattering mode. For nc = nI , the medium becomes totally homogeneous and optically transparent if absorption is negligible. The temporal dependence of the refractive index of the ISF caused by the clearing agent permeation into a tissue can be derived using the law of Gladstone and Dale, which states that for a multi-component system the resulting value of the refractive index represents an average of the refractive indices of the components related to their volume fractions [86, 87]. Such dependence is defined as nI (t) = [1 − C (t)] nbase + C (t) nosm ,

(21.4)

where nbase is the refractive index of the tissue ISF at the initial moment and nosm is the refractive index of an agent solution. In practice any of the available optical clearing agents (OCAs) can be taken to provide light scattering reduction [65]; however in this chapter the advantages of the glucose solution will be considered. Wavelength dependence of aqueous glucose solution can be estimated as [63] nosm (λ ) = nw (λ ) + 0.1515C,

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(21.5)

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where nw (λ ) is the wavelength dependence of the refractive index of water [88], and C is the glucose concentration, g/ml. As a first approximation we assume that during the interaction of the immersion liquid with a tissue, the size of the scatterers does not change. This assumption is confirmed by the results presented by Huang and Meek [89] for scleral and corneal tissue at action of polyethylene glycol solutions at pH values near 4. In the case of unchangeable scatterer size, all changes in the tissue scattering are connected with the changes of the refractive index of the ISF described by Eq. (21.1). The increase of the refractive index of the ISF provides the decrease of the relative refractive index of the scattering particles and, consequently, the decrease of the scattering coefficient. For noninteracting particles the scattering coefficient of a tissue is defined by the following equation

µs (t) = N σs (t) ,

(21.6)

where N is the number of the scattering particles (fibrils) per unit area and σs (t) is the time-dependent cross-section of scattering [Eq. (21.3)]. The number of the scattering particles per unit area can be estimated as N = φ /(π a)2 [84], where φ is the volume fraction of the tissue scatterers. For fibrous tissues φ is usually equal to 0.3 [36, 40, 90]. To take into account interparticle correlation effects, which are important for tissues with densely packed particles, the scattering cross-section has to be corrected by the packing factor of the scattering particles, (1 − φ )3 / (1 + φ ) [36]. Thus, Eq. (21.6) has to be rewritten as

µs (t) =

φ (1 − φ )3 σ . (t) s π a2 1+φ

(21.7)

Tissue swelling (or shrinkage) caused by action of an OCA leads to change of tissue sample volume that produces the corresponding change of the volume fraction of the scatterers and their packing factor, as well as the numerical concentration, i.e., the number of the scattering particles per unit area.

21.2.4 Structure, physical and optical properties of blood From an optical point of view, whole blood is a highly concentrated turbid medium consisting of plasma (55–60 vol%) and blood particles (40–45 vol%) [91, 92], 99% of which are erythrocytes and 1% are leukocytes and platelets [92]. In normal physiological conditions human erythrocytes (red blood cells – RBCs) are anucleate cells in the form of biconcave disks with a diameter ranging between 5.7 and 9.3 µ m and mean size of about 7.5 µ m [92], and maximal thickness varying between 1.7 and 2.4 µ m [93]. Average volume of a RBC is about 90 µ m3 [91, 94, 95] and, according to different data, varies between 70 and 100 µ m3 [93], from 50 to 200 µ m3 [94] or from 30 to 150 µ m3 [95]. In the presence of different pathologies, as well as under changes of osmolarity or pH of the blood plasma, the normal (discocytes) can change shape without changing in volume [92, 93]. The RBCs consist

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of a thin membrane (with a thickness from 7 nm [94] to 25 nm [93]) and cytoplasm, which, in general, is an aqueous hemoglobin solution [95, 96]. Hematocrit is the volume fraction of cells within the whole blood volume and ranges from 36.8% to 49.2% under physiological conditions [91]. The hemoglobin concentration in completely hemolyzed blood lies between 134 and 173 g/L [91], while the hemoglobin concentration in erythrocytes varies from 300 to 360 g/L, with mean concentration being equal to 340 g/L [95]. The content of salts in the erythrocyte cytoplasm is about 7 g/L, and concentration of other organic components (lipids, sugars, enzymes and proteins) is approximately equal to 2 g/L [96]. A change in osmolarity induces a variation of the RBC volume due to water exchange and therefore has an impact on the hemoglobin concentration within the RBC [91]. Flow induced shear stress can influence RBC sedimentation, their reversible agglomeration, axial migration or deformation, and orientation. The flow parameters depend on the blood viscosity and they are influenced by the fact that blood is not a Newton fluid [91, 96]. Under normal physiological conditions, the RBCs may aggregate into rouleaux. The rouleaux may further interact with other rouleaux to form rouleaux networks (or clumps in pathological cases) [91, 93, 96].

21.2.5 Optical model of blood The optics of whole blood at physiological conditions is determined mainly by the optical properties of RBCs and plasma, whereas the contribution to scattering from the remaining blood particles can be neglected. Analysis of light propagation and scattering in such medium can be performed on the basis of description of absorption and scattering characteristics of individual blood particles by taking into account concentration effects and polydispersity of the particles. In radiative transfer theory the absorption coefficient µa , scattering coefficient µs and anisotropy factor g of an elementary volume of investigated medium are determined by the size of RBCs, as well as real (n) and imaginary (χ ) parts of the complex refractive index (n + iχ ) of the scattering particles (RBCs) and their environment (blood plasma). The scattering properties of blood are also dependent on RBC volume, shape and orientation, which are defined in part by blood plasma osmolarity [91], aggregation and disaggregation capability and hematocrit [97]. In the blood optical model, scattering particles can be represented as absorbing and scattering homogeneous spherical particles with volume of each particle being equal to the volume of a real RBC [98, 99]. Contribution to the scattering from the RBC membrane can be neglected due to small thickness of the membrane [95]. Polydispersity of RBCs can be taken into account based on the data presented in Ref. [94]. According to the data obtained in Ref. [95], the concentration of hemoglobin can be related to the RBC volume as:

CHb = 0.72313 − 0.00451V,

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(21.8)

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where CHb is the concentration of hemoglobin, g/ml, and V is the erythrocyte volume, µ m3 . Both the real and the imaginary parts of the refractive index of erythrocytes are directly proportional to the hemoglobin concentration in erythrocytes [91, 92], i.e.: ne = n0 + α CHb ,

(21.9)

χe = β CHb ,

(21.10)

where n0 = 1.34 is the refractive index of the erythrocyte cytoplasm [95] and α and β are spectrally dependent coefficients. For the wavelength of 589 nm, α = 0.1942 ml/g [91], while, for the wavelength 640 nm, α = 0.284 ml/g and β = 0.0001477 ml/g [92]. Since the content of salts, sugars and other organic components in the erythrocyte cytoplasm is insignificant, the spectral dependence of the erythrocyte cytoplasm correlates with the spectral dependence of the refractive index of water, i.e., n0 (λ ) = nw (λ ) + 0.007. Spectral dependence of the refractive index of water is determined in Ref. [88]. The spectral dependences of the coefficients α and β can be calculated on the basis of the data presented in Ref. [92] and using a value of the hemoglobin concentration in RBC equal to 322 g/l, which can be obtained from Eq. (21.9) with the coefficient α = 0.1942 ml/g. The blood plasma contains up to 91% water, 6.5–8% (about 70 g/l) proteins (hemoglobin, albumin and globulin) and about 2% low-molecular-weight compounds [61]. The spectral dependence of the real part of the refractive index of the blood plasma (n p ) in the spectral range 400–1000 nm is determined by the expression [64, 100] 8.4052 × 103 3.9572 × 108 2.3617 × 1013 − − , (21.11) λ2 λ4 λ6 where λ is the wavelength (nm). Since the blood plasma does not have pronounced absorption bands in this spectral range, the imaginary part of the refractive index of the plasma can be neglected in calculations. In terms of the Mie theory, the scattering (σs ) and the anisotropy factor of a homogeneous sphere are expressed by Eqs. (19.37) and (19.38) of Ref. [85]. According to [36], the scattering and absorption coefficients and the anisotropy factor of whole blood considered as a system of closely packed polydisperse particles are given by n p = 1.3254 +

M

µs = (1 − H) ∑ Ni σsi ,

(21.12)

i=1

M

µa = ∑ Ni σai ,

(21.13)

i=1

M

g = ∑ µ si g i i=1

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,

M

∑ µ si .

i=1

(21.14)

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Here H is the hematocrit value; Mis the number of volume fractions of erythrocytes; Ni = Ci /Vei is the number of particles per unit volume of the medium; Ci is the volume fraction occupied by particles of the ith diameter; Vei = 4π a3i /3 is the erythrocyte volume. In Eq. (21.12) the necessity of introduction of the factor (1 − H) [94, 101, 102], which is called the packing factor of scatterers, is determined by interference effects of radiation scattered by the neighboring particles.

21.3 Glucose-Induced Optical Clearing Effects in Tissues 21.3.1 Mechanisms of optical immersion clearing Numerous publications discuss advantages of methods of tissue optical clearing using OCAs and investigation of the mechanisms of clearing [45, 47, 49, 54, 57, 60, 65, 103–106]. There are a few main mechanisms of light scattering reduction induced by an OCA [57, 65, 103–106]: 1) dehydration of tissue constituents, 2) partial replacement of the ISF by the immersion substance and 3) structural modification or dissociation of collagen. Both the first and the second processes mostly cause matching of the refractive indices of the tissue scatterers (cell compartments, collagen and elastin fibers) and the cytoplasm and/or ISF. Tissue dehydration and structural modification lead to tissue shrinkage, i.e., to the near-order spatial correlation of scatterers and, as a result, the increased constructive interference of the elementary scattered fields in the forward direction and destructive interference in perpendicular direction of the incident light, that may significantly increase tissue transmittance even at some refractive index mismatch [65]. For some tissues and for nonoptimized pH of clearing agents, tissue swelling may take place that may be considered as a competitive process in providing of tissue optical clearing [65, 89]. It was shown that dehydration induced by osmotic stimuli such as OCA appears to be a primary mechanism of optical clearing in collagenous and cellular tissues, whereas dehydration induces intrinsic matching effect [65, 103]. The space between fibrils and cell organelles is filled up by water and suspended salts and proteins. Water escaping from tissue having cellular or fibrillar structure is a more rapid process than OCA entering into tissue interstitial space due to the fact that OCA typically has greater viscosity (lower diffusion coefficient) than water. As water is removed from the intrafibrillar or intracellular space, soluble components of ISF or cytoplasm become more concentrated and a refractive index increases. The resulting intrinsic refractive index matching between fibrils or organelles and their surrounding media, as well as density of packing and particle ordering, may significantly contribute to optical clearing [56, 65, 103]. Replacement of water in the interstitial space by the immersion substance leads to the additional matching of the refractive indices between tissue scatterers and ground matter [4, 56].

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21.3.2 Optical clearing of fibrous tissues 21.3.2.1 In vitro spectral measurements For in vitro studies of tissue optical glucose-induced clearing fiber optical gratingarray spectrometer LESA-5, 6med (Biospec, Russia) or similar are suitable thanks to their fast spectra collection during glucose solution action [11, 41, 42, 44–52, 56–58, 60]. Typically, the spectral range of interest is from 400 to 1000 nm. For providing of measurements in transmittance mode the glass cuvette with the tissue sample was placed between two optical fibers. One fiber transmitted the excitation radiation to the sample, and another fiber collected the transmitted radiation. The 0.5-mm diaphragm placed 100 mm apart from the tip of the receiving fiber was used to provide collimated transmittance measurements [41, 44, 45, 49, 51, 60]. The tissue samples were fixed inside cuvette filled up with the glucose solution. In the reflectance mode the spectrometer fiber probe consists of seven optical fibers: one fiber delivers light to the object, and six fibers collect the reflected radiation [44, 46, 47, 51, 52, 57]. The same configuration was used also in in vivo investigations. The total transmittance and diffuse reflectance were measured in the wavelength range 200-2200 nm using spectrophotometer with an internal integrating sphere Cary 2415 (Varian, Australia) [40, 43] or PC1000 (Ocean Optics Inc., USA) [59]. The spectrometers were calibrated using a white slab of BaSO4 with a smooth surface. To reconstruct the absorption and reduced scattering coefficients of a tissue from the measurements, the inverse adding-doubling (IAD) [107] or inverse Monte Carlo (IMC) were applied [108]. Figures 21.1 and 21.2 illustrate dynamics of glucose-induced change of the transmittance spectra of two types of fibrous tissues: sclera and dura mater. In the figures symbols correspond to experimental data [42, 60], the error bars show the standard deviation values and the solid lines correspond to the data calculated using the optical model of fibrous tissue. It is easily seen that the untreated tissue is poorly transparent for the visible light. Administration of 40%- as well as 20%-glucose solutions makes fibrous tissue highly transparent. The approximated time of maximal tissue clearing is about 8 min. Figures show that the clearing process has at least two stages. At the beginning of the process the increase of the transmittance is seen; that is followed by saturation and even the decrease of the transmittance. Two major processes could take place. One of them is diffusion of glucose inside tissue and another is tissue dehydration caused by osmotic properties of glucose. In general, both processes lead to matching of refractive indices of the scatterers and the ISF that causes the decrease of tissue scattering and, therefore, the increase of the collimated transmittance. Dehydration also leads to the additional increase of optical transmission due to decrease of tissue thickness (shrinkage) and corresponding scatterer ordering (packing in order) process due to increase of particle volume fraction. However, the increase of scatterer volume fraction may also cause some competitive increase of scattering coefficient due to random packing process (growth of particle density) that partly compensates the immersion effect. The saturation of optical clearing kinetic curves (see Figs. 21.1 and 21.2) can be explained as the saturation of glucose and water diffusion processes.

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FIGURE 21.1: The time-dependent collimated transmittance of the human sclera sample measured in vitro at different wavelengths concurrently with administration of 40%-glucose solution. The symbols correspond to the experimental data. The solid lines correspond to the data calculated using the optical model of fibrous tissue [42].

FIGURE 21.2: The time-dependent collimated transmittance of the human dura mater sample measured at different wavelengths concurrently with administration of 20%-glucose solution. The symbols correspond to the experimental data. The error bars show the standard deviation values. The solid lines correspond to the data calculated using the optical model of fibrous tissue [60].

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Some darkening at the late time period may be caused by a few reasons, such as the above mentioned particle density growth and interaction of modified ISF (containing glucose and less water) with hydrated collagenous fibrils [65]. 21.3.2.2

In vivo spectral measurements

It is known that at in vivo application of the designed optical immersion technology additional factors such as metabolic reaction of living tissue on clearing agent application, the specificity of tissue functioning and its physiological temperature can significantly change kinetic characteristics and the magnitude of the clearing effect. Temporal dependence of the sclera reflectance at two wavelengths in the course of the optical clearing of rabbit eye in vivo is presented in Fig. 21.3 [46]. As it was shown in in vitro studies [40, 41, 43], visible and NIR reflectance of scleral samples monotonically decreases under the action of the 40%-glucose solution, unlike in vivo measurements. The oscillatory character of kinetic curves is caused by the alternation of processes of clearing, which appears after the application of glucose solution to the eye surface (glucose solution drop), and recovery of the optical properties of sclera after diffusion of glucose from the detection region to surrounding tissues and diffusion of water from surrounding tissues to the detection region. Each oscillation corresponds to time of a new glucose drop applied topically. The time during which the maximum transparency of sclera was achieved in vivo considerably exceeds the clearing time of sclera in vitro. While this time was 8–10 min upon the action of the 40%-glucose solution on sclera samples in vitro [40, 41, 43], the clearing processes during in vivo experiments proceeded for no less than 20 min. There are at least two reasons for that. In in vitro studies typically both sides of the samples are impregnated by a solution; in in vivo case glucose solution could be applied only from one side of a tissue at topical application. Another reason is that the upper cellular epithelial layer of the sclera may have some impact (hindering) on glucose diffusivity. The kinetic curves measured for two wavelengths, one of which (568 nm) is within and another (610 nm) is outside the absorption band of blood (Fig. 21.3), are considerably different. The reflectance within blood absorption band decreased much faster (for 10–12 min) that is explained by the response of the eye (inflammation) to intense illumination during measurements as well as by the osmotic action of glucose. Such induced inflammation increases the local concentration of hemoglobin due to blood inflow through vessels. A further small increase in R in this wavelength range can be explained by a decrease in the absorption of light in sclera due to the stasis of capillaries and microvessels caused by the hyperosmotic action of glucose inside sclera [52]. This effect will be discussed below. Since sclera is the tissue with low blood content, blood absorption practically does not become apparent in the spectra at in vitro measurements [40, 41, 43]. Therefore the reflectance demonstrates uniform falling due to the immersion clearing at all wavelengths. The appearance of the blood spectral bands at in vivo measurements is mainly connected with the existence of choroid under scleral layer, which is usually deleted at in vitro experiments, and functioning capillary net inside the sclera.

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FIGURE 21.3: Temporal dependence of the sclera reflectance at two wavelengths during the optical clearing. Symbols and solid curves correspond to experimental data and result of their approximation, respectively [46].

The change of regime of photon scattering from multiple to low-step leads to the increase of photon’s free path length. Thus, more photons pass the scleral layer almost without the scattering and are absorbed in choroid layer. This corresponds to more significant decreasing of the reflectance in the blood absorption bands in comparison with the range 600–750 nm. Besides, the action of osmotic agent causes the irritation of eyeball that causes more blood coming to the area under study and, thus, additionally reduces the reflectance of tissue in the range of blood absorption bands. By numerical simulation of scleral optical clearing process under the action of the 40%-glucose solution, the time dependences of the fraction of absorbed photons in each layer of the eye cover (sclera, retinal pigmented epithelium and choroid) has been evaluated [46]. The fraction of photons absorbed in sclera decreases with time, on average, by 10%, in accordance with sclera clearing. The fraction of photons absorbed in retinal pigmented epithelium layer increases, on average, by 30%. The fraction of photons absorbed in the choroid increases by 40%. This means that, despite sclera clearing, the main part of light transmitted through sclera is absorbed in pigmented and vascular layers. As a result, the intensity of light incident on the internal tissues of the eye increases insignificantly. This should be taken into account in the dosimetry of laser radiation in transscleral surgery of the inner eye ball tissues because a considerable increase in the absorption of light in retinal pigmented epithelium and choroid layers at sclera clearing can cause their overheating and damage.

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FIGURE 21.4: The time-dependent polarization degree (I|| − I⊥)/(I|| + I⊥ ) of collimated transmittance measured at different wavelengths for the rabbit eye sclera sample at administration of 40%-glucose [41].

On the other hand, scleral optical clearing may provide more precise and effective coagulation of retinal pigmented epithelium and choroid layers [65]. 21.3.2.3 Polarization measurements The kinetics of the polarization properties of the tissue sample at immersion can be easily observed using an optical scheme with a white light source and a tissue sample placed between two parallel or crossed polarizers [41]. At a reduction of scattering, the degree of linearly polarized light propagating in fibrous tissue improves [11, 41, 109]. This is clearly seen from the experimental graphs in Fig. 21.4. As far as the tissue is immersed, the number of scattering events decreases and the residual polarization degree of transmitted linearly polarized light increases. As a result, the kinetics of the average transmittance and polarization degree of the tissue are similar. It follows from Fig. 21.4 that glucose-induced optical clearing leads to increasing in the depolarization length [109]. Due to less scattering of the longer wavelengths, the initial polarization degree is higher for these wavelengths. Tissue clearing has the similar impact on scattering and correspondingly on the improvement of polarization properties on these long wavelengths and especially on the shorter ones.

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21.3.3 Optical clearing of skin 21.3.3.1 Confocal microscopy Skin has a principle limitation that makes the optical clearing method less efficient and more complicated to be applied and described. The origin of this limitation is the dense upper cellular layer stratum corneum (SC), which has a protective function preventing penetration of any chemicals including immersion agents inside the skin. The specific structure of skin defines the methods of its effective optical clearing. The heterogeneous nature of skin provides some possible pathways for solute transport: appendageal, transcellular (through corneocytes) and intercellular (through the lipid phase — lipid bridges) [110]. Lipophilic solutes are permeants believed to be transported via the lipoidal pathway of SC and polar permeants are transported via the pore pathway of SC [111]. It is well known that the diffusion of aqueous solutions of substances (such as glucose) through SC barrier is hindered. The main limitation of the confocal microscopy in skin studies is skin high scattering that distorts the quality of cell images. The increase in the transparency of the upper skin layers can improve the penetration depth, image contrast, and spatial resolution of confocal microscopy [53, 54]. At administration of an OCA to the skin superficial layers they are greatly cleared during the first minute of the process. It is connected mainly with a high porosity of the SC dead cell structure, where airfilled small spaces exist. In depth of SC, where very dense structure is formed by corneocytes that are attached to each other by lipid bridges, diffusion of any agent, including glucose and water, is dramatically reduced. Living epidermis cell layers are a few orders more permeative; however its thickness is 5–10 times bigger than of SC, thus the overall diffusion rate through SC and living epidermis may be comparable [58]. Skin dermis is characterized by a faster diffusion that is characteristic to any fibrous tissue. In the upper blood net plexus region permeability of dermis may be modified (typically fastened) due to blood and lymph vascular net structure [112]. In the deep skin layers OCA diffusion is more homogeneous. The diffusivity of glucose and water is a few orders higher than in living epidermis, and only a half of the order less than diffusion in water [65]. However, because of considerable thickness of dermis in comparison with SC and living epidermis the overall permeation could be comparable with permeation of SC and living epidermis. Using Monte Carlo simulation of the point spread function it was shown recently that confocal microscopic probing of skin at optical clearing is potentially useful for deep reticular dermis monitoring and improving the image contrast and spatial resolution of the upper cell layers [54]. The results of the simulation predict that, to 20th min of glucose diffusion after its intradermal injection, a signal from layers located twice as deeply in the skin can be detected [53]. 21.3.3.2

Two-photon microscopy

The application of glucose may prove to be particularly relevant for enhancing two-photon microscopy [113], since it has been shown that the effect of scattering is to drastically reduce penetration depth to less than that of the equivalent single pho-

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ton fluorescence while largely leaving resolution unchanged [114, 115]. This happens mostly due to excitation beam defocusing (distortion) in the scattering media. On the other hand, this technique is useful in understanding molecular mechanism of tissue optical clearing upon immersion and dehydration [56]. For two-photon scanning fluorescence microscopy system, a mode-locked laser provides the excitation light. The fluorescence is collected by the objective and retraces the same optical path as the laser excitation. In Ref. [55] Ti:Sapphire laser (Coherent MIRA900) as a source of the excitation light, which comprises 100-fs width pulses at an 80-MHz repetition rate, tuneable in wavelength between 700 and 1000 nm, was used. The wavelength range of the detection has an upper limit of 670 nm, and a lower limit of 370 nm. Samples were taken from normal human skin excised during plastic surgery procedures. Images were taken at depths of 20, 40, 60 and 80 µ m from the sectioned surface of the skin tissue. Aqueous solution of glucose (5 M) was investigated. The tissue was immersed in 0.5 ml of the glucose solution and one image stack was acquired every 30 seconds for 6–7 minutes. After the OCA was removed the sample was immersed in 0.1 ml of phosphate buffered saline (PBS), in order to observe the reversibility of the clearing process. The upper limit of tissue shrinkage was estimated as 2% in the course of 6–7 min of OCA application [55]. The average contrast in each image and relative contrast (RC) were defined as [55] Contrast =

Nlines

∑

i, j=1

RC = 100

 Ii, j − Ii, j ,

Contrast[OCA] − Contrast[PBS] Contrast[PBS]

(21.15)

(21.16)

 where Ii, j is the mean intensity of the nearest eight pixels and Nlines = N − 2, with N = 500; Contrast[OCA] and Contrast[PBS] are calculated using Eq. (21.15), for OCA and PBS immersion, respectively. Contrast, as defined by Eq. (21.15), is linearly dependent on the fluorescence intensity and varies according to structures in the image. Hence, its usefulness is primarily to enable comparison between images of the same sample at the same depth maintaining the same field of view. Normalization to the total intensity would be required in order to compare different images. The relative contrast RC also serves for the purpose of comparison. In Ref. [55] it was shown that glucose is effective in improving the image contrast and penetration depth (by up to a factor of two) in two-photon microscopy of ex vivo human dermis. Such improvements were obtained within a few minutes of application. For 5M glucose solution RC = 10.9% at 20 µ m depth, ∼ 134% at 40 µ m depth, ∼ 471% at 60 µ m depth and ∼ 406% at 80 µ m depth. These data are worst in comparison with both pure glycerol and propylene glycol, but glucose diffuses three times faster than glycerol and five times faster than propylene glycol. There was found some specificity in action of glucose in comparison with other OCAs. Effects of glucose have not been shown to be reversible. Results presented in Ref. [55] have not demonstrated for glucose a slowing in rate of contrast increase

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following addition of PBS rather than a decrease as it was seen for glycerol. Such behavior may be associated with a lesser inclusion of the dehydration mechanism in optical clearing for glucose, and a greater amount of this OCA diffused into a tissue in comparison with glycerol [4]. 21.3.3.3

OCT imaging

The typical optical coherence tomography (OCT) fiber optical system employs a broadband light source (a superluminescent diode) to deliver light at a central wavelength of 820 to 1300 nm with a bandwidth of 25-50 nm. Such OCT provides 10– 20 µ m of axial and transverse resolution in free space with a signal to noise ratio up to 100 dB [116, 117]. For OCT measurements, the intensity of reflected light as a function of the depth z and transverse scanning of the sample is obtained as the magnitude of the digitized interference fringes. The result is the determination of optical backscattering or reflectance, R(z, x), versus the axial ranging distance, or depth, z, and the transverse axis x. The reflectance depends on the optical properties of the tissue or blood, i.e., the absorption (µa ) and scattering coefficients (µs ). The relationship between R(z) and attenuation coefficient, µt = µa + µs , is, however, very complicated due to the high and anisotropic scattering of tissue and blood, but for optical depths less than four, the reflected power will be approximately proportional to −2µt z on an exponential scale according to the single scattering model [56, 118], and µt can be obtained from reflectance measurements at two different depths z1 and z2 :

µt =

  R (z1 ) 1 , ln 2 (∆z) R (z2 )

(21.17)

where ∆z = |z1 − z2 |. A few dozen of repeated scan signals from the sample are usually averaged to estimate the total attenuation coefficient µt of the sample. The optical clearing (enhancement of transmittance) ∆T by agents is calculated according to [61] ∆T =

Ra − R × 100%, R

(21.18)

where Ra is the reflectance from the back surface of the sample with an agent and R that with a control sample. The OCT images captured from a skin site of a volunteer at a hyperdermal injection of 40%-glucose allowed one to estimate the total attenuation coefficient [see Eq. (21.17)] [62]. The attenuation initially goes down and then over time goes up. Such a behavior correlates well with the spectral measurements shown in Fig. 21.6 and also illustrates the index matching mechanism induced by the glucose injection. The light beam attenuation in tissue, I/I0 ∼ exp(−µt ), for intact skin (0 min) was found from OCT measurements as I/I0 ∼ = 0.14, and, for immersed skin at 13 min, I/I0 ∼ = 0.30; i.e., the intensity of the transmitted light increased by 2.1 times. That value also correlates well with the spectral measurements.

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FIGURE 21.5: The time-dependent collimated transmittance of the rat skin sample measured in vitro at different wavelengths concurrently with administration of 40%-glucose solution [48].

It should be noted that the high sensitivity of the OCT signal to immersion of living tissue by glucose allows one to monitor its concentration in the skin at a physiological level [25–27]. 21.3.3.4

In vitro spectral measurements

Figure 21.5 shows the time-dependent collimated transmittance of the rat skin samples measured in vitro at different wavelengths concurrently with administration of 40%-glucose solution through dermis, which has fibrous structure [48]. Experimental studies of glucose-induced optical clearing of skin in vitro presented in Refs. [44, 47, 48, 51] have demonstrated similarity in kinetics of the process in skin and fibrous tissues as sclera and dura mater. However, comparing time of the clearing of skin with data obtained at the clearing of another fibrous tissue, it can be concluded that permeability of the agent into the skin is less than that into the sclera [40, 41, 43] or into the dura mater [44, 59, 60]. From Fig. 21.5 it is seen that the glucose solution can effectively control the optical properties of whole skin. At the initial moment the skin is nontransparent for optical radiation. Application of the OCA makes the skin to be more transparent: during 60 min the collimated transmittance increases by more than 30 times at the wavelength 700 nm. 21.3.3.5

In vivo spectral and fluorescence measurements

When in vivo measurements of skin reflectance are carried out, one needs to exclude influence of SC corneum barrier on the clearing process. To increase efficiency

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FIGURE 21.6: The time-dependent reflectance of the human skin measured in vivo at different wavelengths concurrently with administration of the 40%-glucose solution. The symbols correspond to the experimental data [57].

of OCA permeation through SC, epidermal stripping, electrophoresis or flashlampinduced micro-damages can be used [58, 119]. For topical application of glucose-gel compositions these methods lead to the enhancement of the kinetics of in vivo optical clearing of human skin. For example, application of glucose-gel to skin with removed upper layers of SC gave a rapid 10% drop of reflected light intensity [119]. The administration of glucose solution by intradermal injection is a more effective method. In vivo investigations of skin clearing were done with hamsters [47], white rats [44, 48, 51, 57] and male volunteers [49, 51, 57]. As OCAs 40%-glucose solution [44, 48, 51, 57] and highly concentrated glucose (7 M) [47] were used. Kinetics of reflectance spectra, measured concurrently with intradermal injection of 40%-glucose solution, has shown more than 2-times decreasing of the signal [44, 51]. Figure 21.6 presents kinetics of the human skin reflectance measured at different wavelengths. In the figure it is well seen that immediately after glucose injection the skin reflectance significantly decreases. During the first 20 min the reflectance increases but during the following time interval from 20 to 60 min the skin reflectance decreases again. In the time interval from 60 to 140 min the skin reflectance increases slowly, with oscillating behavior. The reflectance of the skin decreased by about 3.5 times at 700 nm, and then the tissue went slowly back to its normal state. The significant decreasing of reflectance observed at initial moment is connected with changing geometry of the experiment after glucose injection. The injected solution forms a vesicle filled with the glucose solution in skin, and the vesicle is observed on the skin surface as a swell. Presence of the swell reduces the distance between the collecting fiber and skin surface, decreases area of detection of the backreflected radiation and hence decreases the reflectance. Injection of the 40%-glucose

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FIGURE 21.7: The changes of skin reaction on injection of 40%-glucose solution. The symbols correspond to the experimental data: bullets — the diameter of virtual transparent window, mm; squares — the diameter of swelling area around the window, mm; triangles — the height of the swell, mm [57].

solution creates the virtual transparent window in skin, which is observed during about 30 min. This window allows one to clearly identify visually blood microvessels in the skin by the naked eye [50, 52]. The swelling white ring appears around the window after the glucose injection. The images of skin were recorded by a digital video camera; diameters of the swelling area and the transparent window were measured [50]. The results are presented in Fig. 21.7. Assuming that the shape of the vesicle can be presented as an ellipsoid of rotation and taking into account the temporal evolution of the diameter of the swelling area, the height of the swell can be calculated. In 1 min after injection the height is 3.5 mm. During first 5 min after injection the height of the swell decreases to 2.4 mm. In the next 10 min (from 5 to 15 min) the height of the swell does not change. In 30 min after injection the swell on the skin surface disappears. Schematically the optical clearing at glucose injection can be represented as the following. The clearing agent forms a vesicle filled up with the aqueous solution of glucose in skin dermis. Since skin dermis is elastic porous medium and the glucose solution is incompressible liquid then tissue surrounding the vesicle becomes compressed. Its porosity decreases [120] and ISF is extruded from pores of the dermis. In the initial moment from 0 to 5 min after injection the size of the vesicle decreases significantly under the influence of mechanical pressure of deformed tissue. In the time interval from 5 to 15 min the skin pressure compensates by elastic properties of the glucose solution, and, as a result, the skin reflectance (and size of swell on the

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skin surface) does not change [57]. During the time interval from 20 to about 60 min glucose solution diffuses from the vesicle to the surrounding tissue and the corresponding tissue clearing takes place. The glucose-injected region becomes more transparent. Skin scattering decreases and, as consequence of the fact, the reflectance of the skin decreases by about 3.8 times in an hour. Then the reflectance increases gradually, that shows the beginning of glucose diffusion from the observed area and corresponding reduction of tissue immersion. On the basis of the experiments, one can conclude that partial matching of refractive indices of the collagen fibres of dermis and the interstitial medium under action of 40%-glucose solution prevails. It should be noted that the skin was transparent during a few hours. The second phase of tissue interaction with glucose is connected with taking down of the matching effect. It is determined by diffusion of glucose along the skin surface between two cellular layers with a few orders less permeability — epidermal and subdermal fat cells. For the used aperture of the detector system, optical clearing was registered during a few hours [57]. Intradermal injection of glucose influences also the functioning properties of skin, in particular the state of blood microcirculation in dermis. Glucose penetrates vessel walls, interacts with blood cells and leads to local dehydration of tissue and cells [4, 47, 91]. It causes a short-term slowing down and local stasis in different microvessels (arterioles, venules, capillaries), and dilation of microvessels in the area of its application [52]. The effect of glucose has some specific features [52] in comparison with other agents. The degree of dilation of vessels for glucose is larger than that for glycerol. The mean diameter increased by 30% at 30 s after glucose topical application to rat mesentry, and it continued to rise constantly throughout. At the fourth minute it rose by 2.5 fold on average. On the other hand, stasis was maintained in the majority of vessels, but blood flow appeared again in a few of the microvessels from the third to the fifth minute. The velocity of reflow was markedly slower than in controls; throughout the observation the intravascular hemolysis was not seen. There were only aggregates of cells. Individual cells with a clear form in blood aggregates in the lumens of microvessels were found. Vessel walls were registered exactly and, as a whole, after glucose application microvessels were visualized better than in controls (before glucose action) (see Fig. 21.8). The changes in blood flow were also local, but they were observed in a larger area (approximately 1 × 1 cm2 ) in comparison with the glycerol action [52]. It is important to know how the function of blood microvessels changes with decreasing of glucose concentration, i.e., with the loss of its hyperosmolarity. A 30%glucose solution caused stasis and dilation only in a part of the microvessels. In a few vessels a slowly oscillating blood motion without hemostasis was observed. A 20%-glucose solution also immediately slowed down the blood flow in all microvessels, but stasis is not observed. After 0.5–1.5 s of glucose application the flow rate reduced by half, and it continued to decrease till 20–25th second. From 35 to 40th second the rate in microvessels began to rise. Sometimes reversed shunts can be observed. After 3–4 min of glucose application blood flow in all vessels was not significantly different from the initial one [52].

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FIGURE 21.8: The effect of 40%-glucose solution on blood microvessels of rat mesentery: (a)—intact state before glucose application (control); (b)—at the 30th second of glucose action; (c)—at the 5th minute of glucose action [52].

Fluorescence measurements were performed for hamster dorsal skin with glucose applied to the subdermal side of the skin and rhodamine fluorescent film placed against the same skin side. Fluorescence was induced by a dye laser pulse at 542 nm delivered to the skin epidermal side by a fiber bundle and was detected by a collection fiber bundle from the epidermal surface at wavelengths longer than 565 nm. On average, up to 100% increase in fluorescence intensity was seen for 20-min glucose application [47].

21.4 Glucose-Induced Optical Clearing Effects in Blood and Cellular Structures 21.4.1 Optical clearing of blood The main scatterers in blood are RBCs. The major part of this cell is the hemoglobin solution: 90% of the weight of dry RBC is hemoglobin [121]. So the refractive index mismatch between hemoglobin solution in RBC cytoplasm and blood plasma provides strong blood scattering. Intravenous injection of glucose aqueous solutions is widely used in clinical practice [122]; thus optical clearing effects could be ex-

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FIGURE 21.9: Absorption spectra of blood at glucose injection calculated in the context of the optical model of blood. The glucose concentration is Cgl = 0, 0.5, and 1 g/ml [64]. pected. Upon introduction of glucose into blood, the refractive index of the blood plasma increases and becomes comparable with that of RBCs. As a consequence, the scattering coefficient decreases, while the blood anisotropy factor increases [65]. The spectral dependence of the refractive index of an aqueous glucose solution is defined by the Eq. (21.5). By analogy with this expression, the refractive index of a glucose solution in the blood plasma can be defined as nim p (λ ) = n p (λ ) + 0.1515Cgl,

(21.19)

where n p (λ ) is the refractive index of the blood plasma defined by Eq. (21.11). A change in the osmolarity of the plasma leads to changes in the size and the complex refractive index of RBCs due to their osmotic dehydration [91] and, consequently, to changes in their scattering and absorption properties. Normally, the osmolarity of blood amounts to 280–300 mosm/l [91]. The introduction of glucose into the blood plasma leads to a linear increase in the osmolarity, which reaches the value 6000 mosm/l at a glucose concentration in the blood plasma of about 1 g/ml. At introduction of glucose into the blood plasma, the hematocrit of the blood decreases. The osmotic dehydration leads to an increase in the concentration of hemoglobin in blood and, as a consequence, to an increase in both the real and the imaginary parts of the refractive index of RBCs. The changes in the real and imaginary parts of the refractive index were estimated using Eqs. (21.9) and (21.10) and account for the change in the hemoglobin concentration defined by Eq. (21.8). The absorption and reduced scattering coefficients and anisotropy factor of whole

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FIGURE 21.10: Calculated spectra of the transport scattering coefficient of blood at glucose injection. Cgl = 0, 0.1, 0.3, 0.4, 0.5, and 0.65 g/ml [64].

FIGURE 21.11: Calculated spectral dependence of the scattering anisotropy factor of blood at glucose injection. Cgl = 0, 0.2, 0.4, 0.6, and 1.0 g/ml [64].

blood at glucose injection (see Figs. 21.9–21.11) were calculated on the basis of optical model of blood [see Eqs. (21.12)–(21.14)]. Maximal optical clearing is observed at a glucose concentration of 0.65 g/ml [64].

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21.4.2 Time-domain and frequency-domain measurements The kinetic response of optical properties of blood, cell suspensions and tissue treated by glucose was measured using a time-domain and frequency-domain techniques [37–39, 63]. The waves in the near infrared (816, 830 and 850 nm) were used. Time-resolved spectroscopy is effective for measuring the optical properties of highly scattering medium; the frequency-domain can give a transient response of mean path length change [122, 123]. Intensity and phase of photon density waves are measured at several source-detector separations.

21.4.3 Experimental results For the noninvasive in vivo measurement the response of a nondiabetic male subject to a glucose load of 1.75 g/kg body weight, as in a standard glucose tolerance test, was used by continuously monitoring the product nµs′ measured on muscle tissue of the subject’s thigh [63]. The values of blood glucose concentrations lay in physiological range 80–150 mg/dL. The correlation between the blood glucose as measured with the home blood glucose monitor with the measured product nµs′ was indicated. An increase of glucose concentration in the physiological range decreases the total amount of tissue scattering [68]. A number of studies deals with the control of scattering properties of cellular tissues such as liver [37–39] and cell cultures and phantoms [37, 68, 88] using aqueous glucose solutions. It was demonstrated that light scattering of the rat liver results mainly from both the whole hepatocyte volume and the intracellular organelles, including mitochondria [37–39, 123]. Those studies suggested that mitochondria are the major source for light scattering in tissue by showing that about 85% of the reduced scattering of the liver originates from mitochondria. In living tissue light scattering depends not only from the extracellular refractive index (nex ) but also from the intracellular refractive index (nin ) and cell size upon exposure to osmotic pressure. If additions of OCAs are involved, one may encounter multiple effects due to changes in cell size and in cellular refractive indexes [37–39]. The addition of glucose solution into tissue can cause both a decrease in cell volume and an increase in refractive index of the extracellular fluid. These two changes contradict each other in the overall scattering behavior of the tissue. The effect of an increase of extracellular refractive index is larger, giving an overall decrease in µs′ . However, if the intracellular refractive index also increases when the added glucose permeates to the cells, the change of cell size becomes the major factor since the effects of intra- and extracellular refractive indexes cancel one another. Specifically, in the liver glucose perfusion measurements represented by Ref. [39], the mean path length of the perfused liver increased rapidly and then returned to its original value within 2 to 3 min. This increase in path length indicates that: (1) glucose may enter the cells and result in increases of both nin and nex so that the effect of changes in refractive indexes is relatively small, and (2) a decrease in cell size and cell volume fraction must occur in the beginning of the perfusion, leading to an increase in path length and µs′ , but soon the shrunken cells regain some of their original volumes [39].

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Thus, addition of glucose solution to cellular suspension and tissue affects the size of cells and the refractive indices of extra- and intracellular fluid, and thus affects the overall tissue scattering properties.

21.5 Conclusion This chapter shows that glucose administration in tissues and blood allows one to control effectively its optical properties. Such control leads to the essential reduction of scattering and therefore causes much higher transmittance (optical clearing) and the appearance of a large amount of least-scattered and ballistic photons, allowing for successful applications of different imaging techniques for medicine. The kinetics of tissue optical clearing, defined, in general, by both the kinetics of dehydration and refractive index matching, is characterized by different time intervals in dependence on tissue and used agents. The swelling or shrinkage of the tissue and cells under action of clearing agents may play an important role in the tissue clearing process. Along with common features in character of tissue clearing under action of immersion agent, glucose has a number of peculiarities in its influence on tissues and blood. The immersion technique has a great potential for noninvasive medical diagnostics using reflectance spectroscopy, frequency-domain measurements, OCT, confocal microscopy and other methods where scattering is a serious limitation. Optical clearing can increase effectiveness of a number of therapeutic and surgical methods using laser action on a target area hindered in depth of a tissue.

Acknowledgment This work has been supported in part by grants PG05-006-2 and REC-006 of U.S. Civilian Research and Development Foundation for the Independent States of the Former Soviet Union (CRDF) and the Russian Ministry of Science and Education, and grant of RFBR No. 06-02-16740-a.

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[14] C.S. Robertson, S.P. Gopinath, and B. Chance, “A new application for nearinfrared spectroscopy: detection of delayed intracranial hematomas after head injury,” J. Neurotrauma (Suppl.), vol. 12, 1995, pp. 591-600. [15] F. Bevilacqua, D. Piguet, P. Marquet, J.D. Gross, B.J. Tromberg, and C. Depeursinge, “In vivo local determination of tissue optical properties: applications to human brain,” Appl. Opt. vol. 38, 1999, pp. 4939-4950. [16] W.-C. Lin, S.A. Toms, M. Johnson, E.D. Jansen, and A. Mahadevan-Jansen, “In vivo brain tumor demarcation using optical spectroscopy,” Photochem. Photobiol., vol. 73, 2001, pp. 396-402. [17] W.A. Kalender, “X-ray computed tomography,” Phys. Med. Biol., vol. 51, 2006, pp. R29-R43. [18] Y. Chen, D.R. Tailor, X. Intes, and B. Chance, “Correlation between nearinfrared spectroscopy and magnetic resonance imaging of rat brain oxygenation modulation,” Phys. Med. Biol., vol. 48, 2003, pp. 417-427. [19] A.M. Siegel, J.P. Culver, J.B. Mandeville, and D.A. Boas, “Temporal comparison of functional brain imaging with diffuse optical tomography and fMRI during rat forepaw stimulation,” Phys. Med. Biol., vol. 48, 2003, pp. 13911403. [20] X. Wang, Y. Pang, G. Ku, X. Xie, G. Stoica, and L.V. Wang, “Noninvasive laser-induced photoacoustic tomography for structural and functional in vivo imaging of the brain,” Nature Biotechnol., vol. 21, 2003, pp. 803-806. [21] A.J. du Plessis and J.J. Volpe, “Cerebral oxygenation and hemodynamic changes during infant cardiac surgery: measurements by near infrared spectroscopy,” J. Biomed. Opt., vol. 1, 1996, pp. 373-386. [22] S. Fantini, E.L. Heffer, V.E. Pera, A. Sassaroli, and N. Liu, “Spatial and spectral information in optical mammography,” Technol. Cancer Res. Treat., vol. 4, 2005 pp. 471-482. [23] A. Gerger, S. Koller, T. Kern, et al., “Diagnostic applicability of in vivo confocal laser scanning microscopy in melanocytic skin tumors,” J. Invest. Dermatol., vol. 124, 2005, pp. 493-498. [24] B.R. Masters and P.T.C. So, “Confocal microscopy and multi-photon excitation microscopy of human skin in vivo,” Opt. Express, vol. 8, 2001, pp. 2-10. [25] P.J. Campagnola, H.A. Clark, W.A. Mohler, A. Lewis, and L.M. Loew, “Second-harmonic imaging microscopy of living cells,” J. Biomed. Opt., vol. 6, 2001, pp. 277-286. [26] K.V. Larin, M.S. Eledrisi, M. Motamedi, and R.O. Esenaliev, “Noninvasive blood glucose monitoring with optical coherence tomography,” Diabetes Care, vol. 25, 2002, pp. 2263-2267.

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