| 研究生: |
陳映羽 Chen, Ying-Yu |
|---|---|
| 論文名稱: |
利用漫反射光譜技術進行非侵入式皮膚功能評估:從水合作用到血紅蛋白 Noninvasive Skin Function Assessment Using Diffuse Reflectance Spectroscopy:From Hydration to Hemoglobin |
| 指導教授: |
曾盛豪
Tseng, Sheng-Hao |
| 學位類別: |
博士 Doctor |
| 系所名稱: |
理學院 - 光電科學與工程學系 Department of Photonics |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 英文 |
| 論文頁數: | 125 |
| 中文關鍵詞: | 漫反射光譜 、非侵入式 、吸收 、散射 、血紅蛋白 、皮膚水合作用 |
| 外文關鍵詞: | Diffuse Reflectance Spectroscopy, Noninvasive, Absorption, Scattering, Hemoglobin, Skin Hydration |
| 相關次數: | 點閱:53 下載:0 |
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1. Barbieri, J.S., K. Wanat, and J. Seykora, Skin: Basic Structure and Function, in Pathobiology of Human Disease, L.M. McManus and R.N. Mitchell, Editors. 2014, Academic Press: San Diego. p. 1134-1144.
2. Honari, G. and H. Maibach, Chapter 1 - Skin Structure and Function, in Applied Dermatotoxicology, H. Maibach and G. Honari, Editors. 2014, Academic Press: Boston. p. 1-10.
3. Structure and function of the skin, in Paediatric Dermatology, S. Lewis-Jones, et al., Editors. 2020, Oxford University Press. p. 0.
4. Lai-Cheong, J.E. and J.A. McGrath, Structure and function of skin, hair and nails. Medicine, 2013. 41(6): p. 317-320.
5. Celleno, L. and F. Tamburi, Chapter 1 - Structure and Function of the Skin, in Nutritional Cosmetics, A. Tabor and R.M. Blair, Editors. 2009, William Andrew Publishing: Boston. p. 3-45.
6. Yuasa, T., et al. Construction of spectral reflectance database for estimation of absorption and scattering parameters in skin tissue. in European Conference on Biomedical Optics. 2019. Optica Publishing Group.
7. Jayanthi, J., et al., Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity. BMJ open, 2011. 1(1): p. e000071.
8. Tzeng, S.-Y., et al., Portable handheld diffuse reflectance spectroscopy system for clinical evaluation of skin: a pilot study in psoriasis patients. Biomedical Optics Express, 2016. 7(2): p. 616-628.
9. Verdel, N., et al., Physiological and structural characterization of human skin in vivo using combined photothermal radiometry and diffuse reflectance spectroscopy. Biomedical Optics Express, 2019. 10(2): p. 944-960.
10. Kumar, A., et al., Diffuse reflectance-based spectroscopic technique for real-time estimation of localized blood oxygenation parameters from human fingertips: a preliminary study. Sensors & Diagnostics, 2022. 1(6): p. 1236-1242.
11. Palmer, G.M. and N. Ramanujam, Monte Carlo-based inverse model for calculating tissue optical properties. Part I: Theory and validation on synthetic phantoms. Appl Opt, 2006. 45(5): p. 1062-71.
12. Cerussi, A., et al., Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy. Proceedings of the National Academy of Sciences, 2007. 104(10): p. 4014-4019.
13. Kienle, A. and T. Glanzmann, In vivo determination of the optical properties of muscle with time-resolved reflectance using a layered model. Phys Med Biol, 1999. 44(11): p. 2689-702.
14. Clemson, B., M. Mrsan, and K. Vishwanath, Development of a portable, non-contact diffuse reflectance system for tissue spectroscopy. SPIE BiOS. Vol. 11953. 2022: SPIE.
15. Guo, C., et al., Optimization on source detector distance for the glucose sensing in a tissue phantom using near-infrared diffuse spectra. SPIE/COS Photonics Asia. Vol. 10024. 2016: SPIE.
16. Cheng, N.Y., et al., Handheld diffuse reflectance spectroscopy system for noninvasive quantification of neonatal bilirubin and hemoglobin concentrations: a pilot study. Biomed Opt Express, 2023. 14(1): p. 467-476.
17. Hsu, C.-K., et al., Investigating the clinical implication of corneometer and mexameter readings towards objective, efficient evaluation of psoriasis vulgaris severity. Scientific Reports, 2022. 12(1): p. 7469.
18. Yang, C.-C., et al., Investigation of water bonding status of normal and psoriatic skin in vivo using diffuse reflectance spectroscopy. Scientific Reports, 2021. 11(1): p. 8901.
19. Chen, Y.-Y., et al., Non-invasive assessment of skin hydration and sensation with diffuse reflectance spectroscopy. Scientific Reports, 2023. 13(1): p. 20149.
20. Chen, Y.Y., et al., Noninvasive hemoglobin quantification across different cohorts using a wearable diffuse reflectance spectroscopy system. Biomed Opt Express, 2024. 15(3): p. 1739-1749.
21. Chen, C.-T., et al., Noninvasive transcutaneous bilirubin measurement in adults using skin diffuse reflectance. Biomedical Optics Express, 2023. 14(10): p. 5405-5417.
22. Kevin, I., Chapter 2 - Anatomy of the human skin. 2020: p. 9-18.
23. Abdayem, R. and M. Haftek, Barrière épidermique. Annales de Dermatologie et de Vénéréologie, 2018. 145(4): p. 293-301.
24. Madison, K.C., Barrier function of the skin: "la raison d'être" of the epidermis. J Invest Dermatol, 2003. 121(2): p. 231-41.
25. Kolarsick, P.A.J., M.A. Kolarsick, and C. Goodwin, Anatomy and Physiology of the Skin. Journal of the Dermatology Nurses' Association, 2011. 3(4): p. 203-213.
26. Gilaberte, Y., et al., Chapter 1 - Anatomy and Function of the Skin. 2016: p. 1-14.
27. Vijayavenkataraman, S., W.F. Lu, and J.Y.H. Fuh, 3D bioprinting of skin: a state-of-the-art review on modelling, materials, and processes. Biofabrication, 2016. 8(3): p. 032001.
28. Lee, S.H., S.K. Jeong, and S.K. Ahn, An Update of the Defensive Barrier Function of Skin. Yonsei Med J, 2006. 47(3): p. 293-306.
29. Gunathilake, R., The Human Epidermal Antimicrobial Barrier: Current Knowledge, Clinical Relevance and Therapeutic Implications. Recent Patents on Anti-Infective Drug Discovery, 2015. 10(2): p. 84-97.
30. Knüttel, A. and M. Boehlau-Godau, Spatially confined and temporally resolved refractive index and scattering evaluation in human skin performed with optical coherence tomography. J Biomed Opt, 2000. 5(1): p. 83-92.
31. Barati, M., et al., Collagen supplementation for skin health: A mechanistic systematic review. J Cosmet Dermatol, 2020. 19(11): p. 2820-2829.
32. Bolke, L., et al., A Collagen Supplement Improves Skin Hydration, Elasticity, Roughness, and Density: Results of a Randomized, Placebo-Controlled, Blind Study. Nutrients, 2019. 11(10).
33. Constantin, M.M., et al., Skin Hydration Assessment through Modern Non-Invasive Bioengineering Technologies. Maedica (Bucur), 2014. 9(1): p. 33-8.
34. Greve, T.M., S. Kamp, and G.B. Jemec, Disease quantification in dermatology: in vivo near-infrared spectroscopy measures correlate strongly with the clinical assessment of psoriasis severity. J Biomed Opt, 2013. 18(3): p. 037006.
35. Cristiano, M.C., et al., In vitro and in vivo trans-epidermal water loss evaluation following topical drug delivery systems application for pharmaceutical analysis. J Pharm Biomed Anal, 2020. 186: p. 113295.
36. Seno, S.-i., et al., Quantitative evaluation of skin barrier function using water evaporation time related to transepidermal water loss. Skin Research and Technology, 2023. 29(1): p. e13242.
37. Schmid-Wendtner, M.H. and H.C. Korting, The pH of the skin surface and its impact on the barrier function. Skin Pharmacol Physiol, 2006. 19(6): p. 296-302.
38. Mogensen, M., et al., OCT imaging of skin cancer and other dermatological diseases. J Biophotonics, 2009. 2(6-7): p. 442-51.
39. Claudio, A.T.-S., et al., In vivo determination of dermal water content in chronological skin aging by confocal Raman spectroscopy. Vibrational Spectroscopy, 2021. 112: p. 103196.
40. Tsai, T.H., et al., Multiphoton microscopy in dermatological imaging. J Dermatol Sci, 2009. 56(1): p. 1-8.
41. Arimoto, H., M. Egawa, and Y. Yamada, Depth profile of diffuse reflectance near-infrared spectroscopy for measurement of water content in skin. Skin Res Technol, 2005. 11(1): p. 27-35.
42. Egawa, M., et al., Regional difference of water content in human skin studied by diffuse-reflectance near-infrared spectroscopy: consideration of measurement depth. Appl Spectrosc, 2006. 60(1): p. 24-8.
43. Heise, H., Clinical applications of near- and mid-infrared spectroscopy. Infrared and Raman Spectroscopy of Biological Materials, Chapter 8, 2001: p. 259-322.
44. Arista Romeu, E.J., et al., Diffuse reflectance spectroscopy accurately discriminates early and advanced grades of fatty liver in mice. Journal of Biomedical Optics, 2018. 23(11): p. 115005.
45. Bydlon, T.M., et al., Chromophore based analyses of steady-state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. Journal of Biophotonics, 2015. 8(1-2): p. 9-24.
46. Hsu, C.K., et al., Non-invasive evaluation of therapeutic response in keloid scar using diffuse reflectance spectroscopy. Biomed Opt Express, 2015. 6(2): p. 390-404.
47. Naglič, P., et al., Applicability of diffusion approximation in analysis of diffuse reflectance spectra from healthy human skin. 1st International Conference "Biophotonics Riga 2013". Vol. 9032. 2013: SPIE.
48. Post, A.L., et al., Subdiffuse scattering model for single fiber reflectance spectroscopy. J Biomed Opt, 2020. 25(1): p. 1-11.
49. Tzeng, S.Y., et al., Skin collagen can be accurately quantified through noninvasive optical method: Validation on a swine study. Skin Res Technol, 2018. 24(1): p. 59-64.
50. Shalaby, N., et al., Time-resolved fluorescence (TRF) and diffuse reflectance spectroscopy (DRS) for margin analysis in breast cancer. Lasers in Surgery and Medicine, 2018. 50(3): p. 236-245.
51. Kinoshita, S., et al., Real-time Monitoring of Hypoxic-Ischemic Brain Damage in Neonatal Rats Using Diffuse Light Reflectance Spectroscopy. Reprod Sci, 2020. 27(1): p. 172-181.
52. Tseng, S.H., et al., Quantitative spectroscopy of superficial turbid media. Optics Letters, 2005. 30(23): p. 3165-3167.
53. Cheng, N.-Y., et al., Noninvasive transcutaneous bilirubin assessment of neonates with hyperbilirubinemia using a photon diffusion theory-based method. Biomedical Optics Express, 2019. 10(6): p. 2969-2984.
54. Tseng, S.H., et al., Noninvasive evaluation of collagen and hemoglobin contents and scattering property of in vivo keloid scars and normal skin using diffuse reflectance spectroscopy: pilot study. J Biomed Opt, 2012. 17(7): p. 077005.
55. Chen, Y.-W., et al., Toward reliable retrieval of functional information of papillary dermis using spatially resolved diffuse reflectance spectroscopy. Biomedical optics express, 2016. 7(2): p. 542-558.
56. Perutz, M.F., Stereochemistry of Cooperative Effects in Haemoglobin: Haem–Haem Interaction and the Problem of Allostery. Nature, 1970. 228(5273): p. 726-734.
57. An, R., et al., Emerging point-of-care technologies for anemia detection. Lab Chip, 2021. 21(10): p. 1843-1865.
58. Hultcrantz, M., et al., Hemoglobin concentration and risk of arterial and venous thrombosis in 1.5 million Swedish and Danish blood donors. Thromb Res, 2020. 186: p. 86-92.
59. Weiss, G. and L.T. Goodnough, Anemia of chronic disease. N Engl J Med, 2005. 352(10): p. 1011-23.
60. Heinz, L. and S. Kathrin, Symptomatology of anemia. Seminars in Oncology, 2001. 28: p. 7-14.
61. Kassebaum, N.J., The Global Burden of Anemia. Hematol Oncol Clin North Am, 2016. 30(2): p. 247-308.
62. Babitt, J.L. and H.Y. Lin, Mechanisms of anemia in CKD. J Am Soc Nephrol, 2012. 23(10): p. 1631-4.
63. Groopman, J.E. and L.M. Itri, Chemotherapy-Induced Anemia in Adults: Incidence and Treatment. JNCI: Journal of the National Cancer Institute, 1999. 91(19): p. 1616-1634.
64. Akhtar, K., et al., HemoCue photometer: a better alternative of hemoglobin estimation in blood donors? Indian J Hematol Blood Transfus, 2008. 24(4): p. 170-2.
65. Osborn, Z.T., et al., Accuracy of Point-of-Care Testing for Anemia in the Emergency Department. Respir Care, 2019. 64(11): p. 1343-1350.
66. Hiscock, R., et al., Comparison of Massimo Pronto-7 and HemoCue Hb 201+ with laboratory haemoglobin estimation: a clinical study. Anaesth Intensive Care, 2014. 42(5): p. 608-13.
67. Jost, G.B., et al., Evaluation of Noninvasive Hemoglobin Measurements in Trauma Patients: A Repeat Study. J Surg Res, 2021. 266: p. 213-221.
68. Patino, M., et al., Trending and accuracy of noninvasive hemoglobin monitoring in pediatric perioperative patients. Anesth Analg, 2014. 119(4): p. 920-925.
69. Shah, N., E.A. Osea, and G.J. Martinez, Accuracy of noninvasive hemoglobin and invasive point-of-care hemoglobin testing compared with a laboratory analyzer. Int J Lab Hematol, 2014. 36(1): p. 56-61.
70. Mannino, R.G., et al., Smartphone app for non-invasive detection of anemia using only patient-sourced photos. Nature Communications, 2018. 9(1): p. 4924.
71. Young, M.F., et al., Non-invasive hemoglobin measurement devices require refinement to match diagnostic performance with their high level of usability and acceptability. PLoS One, 2021. 16(7): p. e0254629.
72. Tzeng, S.Y., et al., Skin collagen can be accurately quantified through noninvasive optical method: Validation on a swine study. Skin Research and Technology, 2018. 24(1): p. 59-64.
73. Hsu, C.-K., et al., Non-invasive evaluation of therapeutic response in keloid scar using diffuse reflectance spectroscopy. Biomedical optics express, 2015. 6(2): p. 390-404.
74. Cheng, N.Y., et al., Noninvasive transcutaneous bilirubin assessment of neonates with hyperbilirubinemia using a photon diffusion theory-based method. Biomed Opt Express, 2019. 10(6): p. 2969-2984.
75. Ding, H., et al., Refractive indices of human skin tissues at eight wavelengths and estimated dispersion relations between 300 and 1600 nm. Physics in Medicine & Biology, 2006. 51(6): p. 1479.
76. Prahl, S., Optical absorption of hemoglobin. http://omlc. ogi. edu/spectra/hemoglobin, 1999.
77. Ross, E.V. and N. Uebelhoer, Laser-tissue interactions. Lasers in dermatology and medicine, 2012: p. 1-23.
78. Mishchenko, M.I., V. Tuchin, Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnostics , SPIE Press, Bellingham, WA (2007) Hardbound, ISBN 0-8194-6433-3, xl+ 841pp. Journal of Quantitative Spectroscopy and Radiative Transfer, 2009. 110: p. 528.
79. Busch Jr, D.R., Computer-aided, multi-modal, and compression diffuse optical studies of breast tissue. 2011, Citeseer.
80. Ishimaru, A., Wave propagation and scattering in random media. Vol. 2. 1978: Academic press New York.
81. Kroese, D.P., et al., Why the Monte Carlo method is so important today. Wiley Interdisciplinary Reviews: Computational Statistics, 2014. 6(6): p. 386-392.
82. Wang, L.V. and H.-i. Wu, Biomedical optics: principles and imaging. 2012: John Wiley & Sons.
83. Tuchin, V.V. and V. Tuchin, Tissue optics: light scattering methods and instruments for medical diagnosis. Vol. 13. 2007: SPIE press Bellingham.
84. Haskell, R.C., et al., Boundary conditions for the diffusion equation in radiative transfer. Journal of the Optical Society of America A, 1994. 11(10): p. 2727-2741.
85. Hielscher, A.H., R.E. Alcouffe, and R.L. Barbour, Comparison of finite-difference transport and diffusion calculations for photon migration in homogeneous and heterogeneous tissues. Physics in medicine and biology, 1998. 43(5): p. 1285.
86. Kienle, A., et al., Noninvasive determination of the optical properties of two-layered turbid media. Applied Optics, 1998. 37(4): p. 779-91.
87. Tseng, S.-H., et al., Quantitative spectroscopy of superficial turbid media. Optics Letters 2005. 30(23): p. 3165-3167.
88. Tseng, S.-H., et al., Determination of optical properties of superficial volumes of layered tissue phantoms. IEEE Trans Biomed Eng, 2008. 55(1): p. 335-9.
89. Tseng, S.-H., A. Grant, and A.J. Durkin, In vivo determination of skin near-infrared optical properties using diffuse optical spectroscopy. Journal of Biomedical Optics 2008. 13(1): p. 014016.
90. Tseng, S.-H., et al., Chromophore concentrations, absorption andscattering properties of human skin in-vivo. Optics Express, 2009. 17(17): p. 14599-14617.
91. Tseng, S.-H., et al., Investigation of a probe design for facilitating the uses of the standard photon diffusion equation at short source-detector separations: Monte Carlo simulations. J Biomed Opt, 2009. 14(5): p. 054043.
92. Wang, L., S.L. Jacques, and L. Zheng, MCML—Monte Carlo modeling of light transport in multi-layered tissues. Computer methods and programs in biomedicine, 1995. 47(2): p. 131-146.
93. Lux, I., Monte Carlo particle transport methods. 2018: CRC press.
94. Metropolis, N. and S. Ulam, The monte carlo method. Journal of the American statistical association, 1949. 44(247): p. 335-341.
95. Wilson, B.C. and G. Adam, A Monte Carlo model for the absorption and flux distributions of light in tissue. Medical physics, 1983. 10(6): p. 824-830.
96. Flock, S.T., et al., Monte Carlo modeling of light propagation in highly scattering tissues. I. Model predictions and comparison with diffusion theory. IEEE Transactions on Biomedical Engineering, 1989. 36(12): p. 1162-1168.
97. Liu, Q. and N. Ramanujam, Scaling method for fast Monte Carlo simulation of diffuse reflectance spectra from multilayered turbid media. JOSA A, 2007. 24(4): p. 1011-1025.
98. Hayakawa, C.K., et al., Perturbation Monte Carlo methods to solve inverse photon migration problems in heterogeneous tissues. Optics letters, 2001. 26(17): p. 1335-1337.
99. Hayashi, T., Y. Kashio, and E. Okada, Hybrid Monte Carlo-diffusion method for light propagation in tissue with a low-scattering region. Applied optics, 2003. 42(16): p. 2888-2896.
100. Yusoff, M.S. and M. Jaafar. Performance of CUDA GPU in Monte Carlo simulation of light-skin diffuse reflectance spectra. in 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences. 2012. IEEE.
101. Yang, O. and B. Choi, Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU). Biomedical Optics Express, 2013. 4(11): p. 2667-2672.
102. Ren, N., et al., GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues. Optics express, 2010. 18(7): p. 6811-6823.
103. Fang, Q. and D.A. Boas, Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Optics express, 2009. 17(22): p. 20178-20190.
104. Zhu, C. and Q. Liu, Review of Monte Carlo modeling of light transport in tissues. Journal of biomedical optics, 2013. 18(5): p. 050902-050902.
105. Alerstam, E., T. Svensson, and S. Andersson-Engels, Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration. Journal of biomedical optics, 2008. 13(6): p. 060504-060504-3.
106. Chen, Y.-W. and S.-H. Tseng, Efficient construction of robust artificial neural networks for accurate determination of superficial sample optical properties. Biomedical optics express, 2015. 6(3): p. 747-760.
107. Tseng, T.-Y., et al., Quantification of the optical properties of two-layered turbid media by simultaneously analyzing the spectral and spatial information of steady-state diffuse reflectance spectroscopy. Biomedical optics express, 2011. 2(4): p. 901-914.
108. Murata, N., S. Yoshizawa, and S.-i. Amari, Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE transactions on neural networks, 1994. 5(6): p. 865-872.
109. Bydlon, T.M., et al., Chromophore based analyses of steady‐state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. Journal of biophotonics, 2015. 8(1-2): p. 9-24.
110. Kocsis, L., P. Herman, and A. Eke, The modified Beer–Lambert law revisited. Physics in Medicine & Biology, 2006. 51(5): p. N91.
111. Jacques, S.L., Optical properties of biological tissues: a review. Phys Med Biol, 2013. 58(11): p. R37-61.
112. Bydlon, T.M., et al., Chromophore based analyses of steady-state diffuse reflectance spectroscopy: current status and perspectives for clinical adoption. J Biophotonics, 2015. 8(1-2): p. 9-24.
113. Yudovsky, D. and L. Pilon, Rapid and accurate estimation of blood saturation, melanin content, and epidermis thickness from spectral diffuse reflectance. Appl Opt, 2010. 49(10): p. 1707-19.
114. Taroni, P., et al., Absorption of collagen: effects on the estimate of breast composition and related diagnostic implications. J Biomed Opt, 2007. 12(1): p. 014021.
115. Cappon, D.J., et al., Fiber-optic probe design and optical property recovery algorithm for optical biopsy of brain tissue. J Biomed Opt, 2013. 18(10): p. 107004.
116. Nachabe, R., et al., Effect of bile absorption coefficients on the estimation of liver tissue optical properties and related implications in discriminating healthy and tumorous samples. Biomed Opt Express, 2011. 2(3): p. 600-14.
117. Kim, A., et al., A fiberoptic reflectance probe with multiple source-collector separations to increase the dynamic range of derived tissue optical absorption and scattering coefficients. Opt Express, 2010. 18(6): p. 5580-94.
118. Liu, Q. and N. Ramanujam, Sequential estimation of optical properties of a two-layered epithelial tissue model from depth-resolved ultraviolet-visible diffuse reflectance spectra. Applied Optics, 2006. 45(19): p. 4776-4790.
119. Bays, R., et al., Clinical determination of tissue optical properties by endoscopic spatially resolved reflectometry. Applied Optics, 1996. 35(10): p. 1756-66.
120. Doornbos, R.M., et al., The determination of in vivo human tissue optical properties and absolute chromophore concentrations using spatially resolved steady-state diffuse reflectance spectroscopy. Phys Med Biol, 1999. 44(4): p. 967-81.
121. Farrell, T.J., M.S. Patterson, and B. Wilson, A Diffusion-Theory Model of Spatially Resolved, Steady-State Diffuse Reflectance for the Noninvasive Determination of Tissue Optical-Properties Invivo. Medical Physics, 1992. 19(4): p. 879-888.
122. Pilz, M., S. Honold, and A. Kienle, Determination of the optical properties of turbid media by measurements of the spatially resolved reflectance considering the point-spread function of the camera system. J Biomed Opt, 2008. 13(5): p. 054047.
123. Hsu, C.-K., et al., Non-invasive evaluation of therapeutic response in keloid scar using diffuse reflectance spectroscopy. Biomed Opt Express, 2015. 6(2): p. 390-404.
124. Coleman, T. and Y. Li, On the convergence of interior-reflective Newton methods for nonlinear minimization subject to bounds. Mathematical Programming, 1994. 67(1-3): p. 189-224.
125. Chen, Y.-W. and S.-H. Tseng, Efficient construction of robust artificial neural networks for accurate determination of superficial sample optical properties. Biomed Opt Express, 2015. 6(3): p. 747-60.
126. Cheng, N.-Y., et al., Handheld diffuse reflectance spectroscopy system for noninvasive quantification of neonatal bilirubin and hemoglobin concentrations: a pilot study. Biomedical Optics Express, 2023. 14(1): p. 467-476.
127. Caberlotto, E., et al., Synchronized in vivo measurements of skin hydration and trans‐epidermal water loss. Exploring their mutual influences. International Journal of Cosmetic Science, 2019. 41(5): p. 437-442.
128. Grinich, E.E., A.V. Shah, and E.L. Simpson, Validation of a novel smartphone application‐enabled, patient‐operated skin barrier device. Skin Research and Technology, 2019. 25(5): p. 612-617.
129. Murphrey, M.B., et al., Can a handheld device accurately measure barrier function in ichthyoses? Pediatric dermatology, 2020. 37(5): p. 860-863.
130. Logger, J.G., et al., Value of GPSkin for the measurement of skin barrier impairment and for monitoring of rosacea treatment in daily practice. Skin Research and Technology, 2021. 27(1): p. 15-23.
131. Ye, L., et al., Validation of GPS kin Barrier® for assessing epidermal permeability barrier function and stratum corneum hydration in humans. Skin research and technology, 2019. 25(1): p. 25-29.
132. Tuchin, V.V. Immersion effects in tissues. in Controlling Tissue Optical Properties: Applications in Clinical Study. 2000. SPIE.
133. Chung, S., et al., Non-invasive tissue temperature measurements based on quantitative diffuse optical spectroscopy (DOS) of water. Physics in Medicine & Biology, 2010. 55(13): p. 3753.
134. Chandan, N., et al., A new era of moisturizers. Journal of Cosmetic Dermatology, 2021. 20(8): p. 2425-2430.
135. Horii, I., et al., Stratum corneum hydration and amino acid content in xerotic skin. British Journal of Dermatology, 1989. 121(5): p. 587-592.
136. Matsui, T. and M. Amagai, Dissecting the formation, structure and barrier function of the stratum corneum. International immunology, 2015. 27(6): p. 269-280.
137. Visscher, M.O., et al., Effect of soaking and natural moisturizing factor on stratum corneum water-handling properties. Journal of cosmetic science, 2003. 54(3): p. 289-300.
138. Trianse, S., The search for the ideal moisturizer. Cosmet. Perfum, 1974. 89: p. 57-67.
139. Rawlings, A.V., et al., Stratum corneum moisturization at the molecular level. Journal of investigative dermatology, 1994. 103(5): p. 731-740.
140. Danby, S.G., et al., Different types of emollient cream exhibit diverse physiological effects on the skin barrier in adults with atopic dermatitis. Clinical and experimental dermatology, 2022. 47(6): p. 1154-1164.
141. Warner, R.R., K.J. Stone, and Y.L. Boissy, Hydration disrupts human stratum corneum ultrastructure. Journal of investigative dermatology, 2003. 120(2): p. 275-284.
142. Wölfle, U., et al., Dermatology in the Darwin anniversary. Part 2: Evolution of the skin‐associated immune system. JDDG: Journal der Deutschen Dermatologischen Gesellschaft, 2009. 7(10): p. 862-869.
143. Furuse, M., et al., Claudin-based tight junctions are crucial for the mammalian epidermal barrier: a lesson from claudin-1–deficient mice. The Journal of cell biology, 2002. 156(6): p. 1099-1111.
144. Schmidt, D., A.M. Germano, and T.L. Milani, Effects of water immersion on sensitivity and plantar skin properties. Neuroscience Letters, 2018. 686: p. 41-46.
145. Blank, I.H., Factors which influence the water content of the stratum corneum. Journal of Investigative Dermatology, 1952. 18(6): p. 433-440.
146. Zhai, H. and H.I. Maibach, Occlusion vs. skin barrier function. Skin Research and technology, 2002. 8(1): p. 1-6.
147. Yeh, C., et al. Enhancing diagnosis of gingivitis by quantifying gingival tissue functional parameters with diffuse reflectance spectroscopy. in Biomedical Imaging and Sensing Conference 2021. 2021. SPIE.
148. Marn, H. and J.A. Critchley, Accuracy of the WHO Haemoglobin Colour Scale for the diagnosis of anaemia in primary health care settings in low-income countries: a systematic review and meta-analysis. The Lancet Global Health, 2016. 4(4): p. e251-e265.
149. Feiner, J.R., J.W. Severinghaus, and P.E. Bickler, Dark skin decreases the accuracy of pulse oximeters at low oxygen saturation: the effects of oximeter probe type and gender. Anesthesia & Analgesia, 2007. 105(6): p. S18-S23.
150. Al-Khabori, M., et al., Validation of a non-invasive pulse CO-oximetry based hemoglobin estimation in normal blood donors. Transfusion and Apheresis Science, 2014. 50(1): p. 95-98.
151. Jain, A. and N. Chowdhury, Comparison of the accuracy of capillary hemoglobin estimation and venous hemoglobin estimation by two models of HemoCue against automated cell counter hemoglobin measurement. Asian journal of transfusion science, 2020. 14(1): p. 49-53.
校內:2029-07-19公開