Research progress on minimally invasive and non-invasive blood glucose detection methods_Journal of Biomedical Engineering

Authors：

GAO Xinyu ¹ , XU Zekai ² ,  CHEN Liqun ¹

1. Academy of Medical Engineering and Translational Medicine, TianJin University, TianJin 300072, P.R.China;
2. Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, TianJin 300072, P.R.China;

Corresponding author：

Keywords：

Integrated diabetes management; Blood glucose testing methods; Minimally invasive blood glucose detection; Non-invasive blood glucose detection; Continuous glucose monitoring

DOI：

10.7507/1001-5515.202202017

Video：

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Abstract Full text Figures/Tables Video References Cited by

Blood glucose monitoring has become the weakest point in the overall management of diabetes in China. Long-term monitoring of blood glucose levels in diabetic patients has become an important means of controlling the development of diabetes and its complications, so that technological innovations in blood glucose testing methods have far-reaching implications for accurate blood glucose testing. This article discusses the basic principles of minimally invasive and non-invasive blood glucose testing assays, including urine glucose assays, tear assays, methods of extravasation of tissue fluid, and optical detection methods, etc., focuses on the advantages of minimally invasive and non-invasive blood glucose testing methods and the latest relevant results, and summarizes the current problems of various testing methods and prospects for future development trends.

Citation： GAO Xinyu, XU Zekai, CHEN Liqun. Research progress on minimally invasive and non-invasive blood glucose detection methods. Journal of Biomedical Engineering, 2023, 40(2): 365-372. doi: 10.7507/1001-5515.202202017 Copy

1.	Cole J B, Florez J C. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol, 2020, 16(7): 377-390.
2.	Zheng Y, Ley S H, Hu F B. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol, 2018, 14(2): 88-98.
3.	Saran R, Robinson B, Abbott K C, et al. US renal data system 2019 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis, 2020, 75(1): A6-A7.
4.	王富军, 王文琦. 《中国2型糖尿病防治指南(2020年版)》解读. 河北医科大学学报, 2021, 42(12): 1365-1371.
5.	Kovatchev B. Diabetes technology: monitoring, analytics, and optimal control. Cold Spring Harb Perspect Med, 2019, 9(6): a034389.
6.	Vettoretti M, Facchinetti A. Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review. Biomed Eng Online, 2019, 18(1): 37.
7.	Kovatchev B. A century of diabetes technology: signals, models, and artificial pancreas control. Trends Endocrinol Metab, 2019, 30(7): 432-444.
8.	Kropff J, Choudhary P, Neupane S, et al. Accuracy and longevity of an implantable continuous glucose sensor in the PRECISE study: a 180-day, prospective, multicenter, pivotal trial. Diabetes Care, 2017, 40(1): 63-68.
9.	Christiansen M P, Klaff L J, Brazg R, et al. A prospective multicenter evaluation of the accuracy of a novel implanted continuous glucose sensor: PRECISEⅡ. Diabetes Technol Ther, 2018, 20(3): 197-206.
10.	Marks B E, Williams K M, Sherwood J S, et al. Practical aspects of diabetes technology use: continuous glucose monitors, insulin pumps, and automated insulin delivery systems. J Clin Transl Endocrinol, 2021, 27: 100282.
11.	Wang M, Singh L G, Spanakis E K. Advancing the use of CGM devices in a non-ICU setting. J Diabetes Sci Technol, 2019, 13(4): 674-681.
12.	Huang J H, Lin Y K, Lee T W, et al. Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring. Diabetol Metab Syndr, 2021, 13(1): 94.
13.	Tang L, Chang S J, Chen C J, et al. Non-invasive blood glucose monitoring technology: a review. Sensors (Basel), 2020, 20(23): 6925.
14.	Slugocki M, Bialonczyk D, Özdener A E. A review of emerging technologies in diabetes management for multiple-dose insulin-injecting patients with type 2 diabetes who self-monitor blood glucose. J Pharm Technol, 2019, 35(2): 69-81.
15.	Mian Z, Hermayer K L, Jenkins A. Continuous glucose monitoring: review of an innovation in diabetes management. Am J Med Sci, 2019, 358: 332-339.
16.	Svertoka E, Saafi S, Rusu-Casandra A, et al. Wearables for industrial work safety: a survey. Sensors (Basel), 2021, 21(11): 3844.
17.	Meetoo D, Wong L, Ochieng B. Smart tattoo: technology for monitoring blood glucose in the future. Br J Nurs, 2019, 28(2): 110-115.
18.	Heo Y J, Takeuchi S. Towards smart tattoos: implantable biosensors for continuous glucose monitoring. Adv Healthc Mater, 2013, 2(1): 43-56.
19.	Dai J, Zhang H, Huang C, et al. A gel-based separation-free point-of-care device for whole blood glucose detection. Anal Chem, 2020, 92(24): 16122-16129.
20.	Zhang H, Yang Y, Dai J, et al. Fabrication methods for a gel-based separation-free device for whole blood glucose detection. MethodsX, 2021, 8: 101236.
21.	Malik B H, Coté G L. Real-time, closed-loop dual-wavelength optical polarimetry for glucose monitoring. J Biomed Opt. 2010, 15(1): 017002.
22.	Stark C, Behroozian R, Redmer B, et al. Real-time compensation method for robust polarimetric determination of glucose in turbid media. Biomed Opt Express, 2018, 10(1): 308-321.
23.	Stark C, Carvajal Arrieta C A, Behroozian R, et al. Broadband polarimetric glucose determination in protein containing media using characteristic optical rotatory dispersion. Biomed Opt Express, 2019, 10(12): 6340-6350.
24.	许婷, 彭玉峰, 韩雪云. 基于法拉第旋光效应的葡萄糖浓度传感研究. 光电子•激光, 2021, 32(02): 173-180.
25.	Chen T L, Lo Y L, Liao C C, et al. Noninvasive measurement of glucose concentration on human fingertip by optical coherence tomography. J Biomed Opt, 2018, 23(4): 1-9.
26.	Alsunaidi B, Althobaiti M, Tamal M, et al. A review of non-invasive optical systems for continuous blood glucose monitoring. Sensors (Basel), 2021, 21(20): 6820.
27.	Weatherbee A, Popov I, Vitkin A. Accurate viscosity measurements of flowing aqueous glucose solutions with suspended scatterers using a dynamic light scattering approach with optical coherence tomography. J Biomed Opt, 2017, 22(8): 1-10.
28.	Shokrekhodaei M, Cistola D P, Roberts R C, et al. Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access, 2021, 9: 73029-73045.
29.	Heise H M, Delbeck S, Marbach R. Noninvasive monitoring of glucose using near-infrared reflection spectroscopy of skin-constraints and effective novel strategy in multivariate calibration. Biosensors (Basel), 2021, 11(3): 64.
30.	Wu M, Liu R, Xu K. Near-infrared diffuse reflectance measurement method based on temperature-insensitive radial distance. Appl Spectrosc, 2018, 72(7): 1021-1028.
31.	黄珊,朱书缘,赵蒙蒙, 等. 组织模型中葡萄糖的近红外光谱特性. 激光与光电子学进展, 2020, 57(15): 287-293.
32.	栗红, 宋范蕾, 宋莉. 基于近红外光谱法无创检测血糖浓度校正模型的建立. 中国科技信息, 2021(16): 105-106.
33.	Pullano S A, Greco M, Bianco M G, et al. Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices. Theranostics, 2022, 12(2): 493-511.
34.	Shih W C, Bechtel K L, Rebec M V. Noninvasive glucose sensing by transcutaneous Raman spectroscopy. J Biomed Opt, 2015, 20(5): 051036.
35.	Yang D, Afroosheh S, Lee J O, et al. Glucose sensing using surface-enhanced Raman-mode constraining. Anal Chem, 2018, 90(24): 14269-14278.
36.	Kang J W, Park Y S, Chang H, et al. Direct observation of glucose fingerprint using in vivo Raman spectroscopy. Sci Adv, 2020, 6(4): eaay5206.
37.	Oyaert M, Delanghe J R. Semiquantitative, fully automated urine test strip analysis. J Clin Lab Anal. 2019, 33(5): e22870.
38.	Luo Y, Shen R, Li T, et al. The peroxidase-mimicking function of acetate and its application in single-enzyme-based glucose test paper. Talanta, 2019, 196: 493-497.
39.	Mohammadifar M, Tahernia M, Choi S. An equipment-free, paper-based electrochemical sensor for visual monitoring of glucose levels in urine. SLAS Technol, 2019, 24(5): 499-505.
40.	Chen J, Guo H, Yuan S, et al. Efficacy of urinary glucose for diabetes screening: a reconsideration. Acta Diabetol, 2019, 56(1): 45-53.
41.	Aldridge C F, Behrend E N, Smith J R, et al. Accuracy of urine dipstick tests and urine glucose-to-creatinine ratios for assessment of glucosuria in dogs and cats. J Am Vet Med Assoc, 2020, 257(4): 391-396.
42.	Badugu R, Reece E A, Lakowicz J R. Glucose-sensitive silicone hydrogel contact lens toward tear glucose monitoring. J Biomed Opt. 2018, 23(5): 1-9.
43.	Aihara M, Kubota N, Minami T, et al. Association between tear and blood glucose concentrations: random intercept model adjusted with confounders in tear samples negative for occult blood. J Diabetes Investig. 2021, 12(2): 266-276.
44.	Lee S H, Cho Y C, Bin C Y. Noninvasive self-diagnostic device for tear collection and glucose measurement. Sci Rep, 2019, 9(1): 4747.
45.	Sempionatto J R, Brazaca L C, García-Carmona L, et al. Eyeglasses-based tear biosensing system: non-invasive detection of alcohol, vitamins and glucose. Biosens Bioelectron, 2019, 137: 161-170.
46.	Han J H, Cho Y C, Koh W G, et al. Preocular sensor system for concurrent monitoring of glucose levels and dry eye syndrome using tear fluids. PLoS One, 2020, 15(10): e0239317.
47.	Lin Y R, Hung C C, Chiu H Y, et al. Noninvasive glucose monitoring with a contact lens and smartphone. Sensors (Basel), 2018, 18(10): 3208.
48.	Li X, Huang X, Mo J, et al. A Fully integrated closed-loop system based on mesoporous microneedles-iontophoresis for diabetes treatment. Adv Sci (Weinh), 2021, 8(16): e2100827.
49.	Cheng S, Gu Z, Zhou L, et al. Recent progress in intelligent wearable sensors for health monitoring and wound healing based on biofluids. Front Bioeng Biotechnol, 2021, 9: 765987.
50.	Zhang S, Zeng J, Wang C, et al. The application of wearable glucose sensors in point-of-care testing. Front Bioeng Biotechnol, 2021, 9: 774210.
51.	Teymourian H, Moonla C, Tehrani F, et al. Microneedle-based detection of ketone bodies along with glucose and lactate: toward real-time continuous interstitial fluid monitoring of diabetic ketosis and ketoacidosis. Anal Chem, 2020, 92(2): 2291-2300.
52.	Lipani L, Dupont B G R, Doungmene F, et al. Non-invasive, transdermal, path-selective and specific glucose monitoring via a graphene-based platform. Nat Nanotechnol, 2018, 13(6): 504-511.
53.	Hakala T A, García Pérez A, Wardale M, et al. Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring. Sci Rep, 2021, 11(1): 7609.
54.	Ye S, Feng S, Huang L, et al. Recent progress in wearable biosensors: from healthcare monitoring to sports analytics. Biosensors (Basel), 2020, 10(12): 205.
55.	Moyer J, Wilson D, Finkelshtein I, et al. Correlation between sweat glucose and blood glucose in subjects with diabetes. Diabetes Technol Ther, 2012, 14(5): 398-402.
56.	Bandodkar A J, Jeang W J, Ghaffari R, et al. Wearable sensors for biochemical sweat analysis. Annu Rev Anal Chem (Palo Alto Calif), 2019, 12(1): 1-22.
57.	Shu Y, Su T, Lu Q, et al. Highly stretchable wearable electrochemical sensor based on Ni-Co MOF nanosheet-decorated Ag/rGO/PU fiber for continuous sweat glucose detection. Anal Chem, 2021, 93(48): 16222-16230.
58.	Zhao Y, Zhai Q, Dong D, et al. Highly stretchable and strain-insensitive fiber-based wearable electrochemical biosensor to monitor glucose in the sweat. Anal Chem, 2019, 91(10): 6569-6576.
59.	Vaquer A, Barón E, Rica R. Detection of low glucose levels in sweat with colorimetric wearable biosensors. Analyst. 2021, 146(10): 3273-3279.
60.	Boscari F, Vettoretti M, Cavallin F, et al. Implantable and transcutaneous continuous glucose monitoring system: a randomized cross over trial comparing accuracy efficacy and acceptance. J Endocrinol Invest, 2022, 45(1): 115-124.

1. Cole J B, Florez J C. Genetics of diabetes mellitus and diabetes complications. Nat Rev Nephrol, 2020, 16(7): 377-390.
2. Zheng Y, Ley S H, Hu F B. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nat Rev Endocrinol, 2018, 14(2): 88-98.
3. Saran R, Robinson B, Abbott K C, et al. US renal data system 2019 annual data report: epidemiology of kidney disease in the United States. Am J Kidney Dis, 2020, 75(1): A6-A7.
4. 王富军, 王文琦. 《中国2型糖尿病防治指南(2020年版)》解读. 河北医科大学学报, 2021, 42(12): 1365-1371.
5. Kovatchev B. Diabetes technology: monitoring, analytics, and optimal control. Cold Spring Harb Perspect Med, 2019, 9(6): a034389.
6. Vettoretti M, Facchinetti A. Combining continuous glucose monitoring and insulin pumps to automatically tune the basal insulin infusion in diabetes therapy: a review. Biomed Eng Online, 2019, 18(1): 37.
7. Kovatchev B. A century of diabetes technology: signals, models, and artificial pancreas control. Trends Endocrinol Metab, 2019, 30(7): 432-444.
8. Kropff J, Choudhary P, Neupane S, et al. Accuracy and longevity of an implantable continuous glucose sensor in the PRECISE study: a 180-day, prospective, multicenter, pivotal trial. Diabetes Care, 2017, 40(1): 63-68.
9. Christiansen M P, Klaff L J, Brazg R, et al. A prospective multicenter evaluation of the accuracy of a novel implanted continuous glucose sensor: PRECISEⅡ. Diabetes Technol Ther, 2018, 20(3): 197-206.
10. Marks B E, Williams K M, Sherwood J S, et al. Practical aspects of diabetes technology use: continuous glucose monitors, insulin pumps, and automated insulin delivery systems. J Clin Transl Endocrinol, 2021, 27: 100282.
11. Wang M, Singh L G, Spanakis E K. Advancing the use of CGM devices in a non-ICU setting. J Diabetes Sci Technol, 2019, 13(4): 674-681.
12. Huang J H, Lin Y K, Lee T W, et al. Correlation between short- and mid-term hemoglobin A1c and glycemic control determined by continuous glucose monitoring. Diabetol Metab Syndr, 2021, 13(1): 94.
13. Tang L, Chang S J, Chen C J, et al. Non-invasive blood glucose monitoring technology: a review. Sensors (Basel), 2020, 20(23): 6925.
14. Slugocki M, Bialonczyk D, Özdener A E. A review of emerging technologies in diabetes management for multiple-dose insulin-injecting patients with type 2 diabetes who self-monitor blood glucose. J Pharm Technol, 2019, 35(2): 69-81.
15. Mian Z, Hermayer K L, Jenkins A. Continuous glucose monitoring: review of an innovation in diabetes management. Am J Med Sci, 2019, 358: 332-339.
16. Svertoka E, Saafi S, Rusu-Casandra A, et al. Wearables for industrial work safety: a survey. Sensors (Basel), 2021, 21(11): 3844.
17. Meetoo D, Wong L, Ochieng B. Smart tattoo: technology for monitoring blood glucose in the future. Br J Nurs, 2019, 28(2): 110-115.
18. Heo Y J, Takeuchi S. Towards smart tattoos: implantable biosensors for continuous glucose monitoring. Adv Healthc Mater, 2013, 2(1): 43-56.
19. Dai J, Zhang H, Huang C, et al. A gel-based separation-free point-of-care device for whole blood glucose detection. Anal Chem, 2020, 92(24): 16122-16129.
20. Zhang H, Yang Y, Dai J, et al. Fabrication methods for a gel-based separation-free device for whole blood glucose detection. MethodsX, 2021, 8: 101236.
21. Malik B H, Coté G L. Real-time, closed-loop dual-wavelength optical polarimetry for glucose monitoring. J Biomed Opt. 2010, 15(1): 017002.
22. Stark C, Behroozian R, Redmer B, et al. Real-time compensation method for robust polarimetric determination of glucose in turbid media. Biomed Opt Express, 2018, 10(1): 308-321.
23. Stark C, Carvajal Arrieta C A, Behroozian R, et al. Broadband polarimetric glucose determination in protein containing media using characteristic optical rotatory dispersion. Biomed Opt Express, 2019, 10(12): 6340-6350.
24. 许婷, 彭玉峰, 韩雪云. 基于法拉第旋光效应的葡萄糖浓度传感研究. 光电子•激光, 2021, 32(02): 173-180.
25. Chen T L, Lo Y L, Liao C C, et al. Noninvasive measurement of glucose concentration on human fingertip by optical coherence tomography. J Biomed Opt, 2018, 23(4): 1-9.
26. Alsunaidi B, Althobaiti M, Tamal M, et al. A review of non-invasive optical systems for continuous blood glucose monitoring. Sensors (Basel), 2021, 21(20): 6820.
27. Weatherbee A, Popov I, Vitkin A. Accurate viscosity measurements of flowing aqueous glucose solutions with suspended scatterers using a dynamic light scattering approach with optical coherence tomography. J Biomed Opt, 2017, 22(8): 1-10.
28. Shokrekhodaei M, Cistola D P, Roberts R C, et al. Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access, 2021, 9: 73029-73045.
29. Heise H M, Delbeck S, Marbach R. Noninvasive monitoring of glucose using near-infrared reflection spectroscopy of skin-constraints and effective novel strategy in multivariate calibration. Biosensors (Basel), 2021, 11(3): 64.
30. Wu M, Liu R, Xu K. Near-infrared diffuse reflectance measurement method based on temperature-insensitive radial distance. Appl Spectrosc, 2018, 72(7): 1021-1028.
31. 黄珊,朱书缘,赵蒙蒙, 等. 组织模型中葡萄糖的近红外光谱特性. 激光与光电子学进展, 2020, 57(15): 287-293.
32. 栗红, 宋范蕾, 宋莉. 基于近红外光谱法无创检测血糖浓度校正模型的建立. 中国科技信息, 2021(16): 105-106.
33. Pullano S A, Greco M, Bianco M G, et al. Glucose biosensors in clinical practice: principles, limits and perspectives of currently used devices. Theranostics, 2022, 12(2): 493-511.
34. Shih W C, Bechtel K L, Rebec M V. Noninvasive glucose sensing by transcutaneous Raman spectroscopy. J Biomed Opt, 2015, 20(5): 051036.
35. Yang D, Afroosheh S, Lee J O, et al. Glucose sensing using surface-enhanced Raman-mode constraining. Anal Chem, 2018, 90(24): 14269-14278.
36. Kang J W, Park Y S, Chang H, et al. Direct observation of glucose fingerprint using in vivo Raman spectroscopy. Sci Adv, 2020, 6(4): eaay5206.
37. Oyaert M, Delanghe J R. Semiquantitative, fully automated urine test strip analysis. J Clin Lab Anal. 2019, 33(5): e22870.
38. Luo Y, Shen R, Li T, et al. The peroxidase-mimicking function of acetate and its application in single-enzyme-based glucose test paper. Talanta, 2019, 196: 493-497.
39. Mohammadifar M, Tahernia M, Choi S. An equipment-free, paper-based electrochemical sensor for visual monitoring of glucose levels in urine. SLAS Technol, 2019, 24(5): 499-505.
40. Chen J, Guo H, Yuan S, et al. Efficacy of urinary glucose for diabetes screening: a reconsideration. Acta Diabetol, 2019, 56(1): 45-53.
41. Aldridge C F, Behrend E N, Smith J R, et al. Accuracy of urine dipstick tests and urine glucose-to-creatinine ratios for assessment of glucosuria in dogs and cats. J Am Vet Med Assoc, 2020, 257(4): 391-396.
42. Badugu R, Reece E A, Lakowicz J R. Glucose-sensitive silicone hydrogel contact lens toward tear glucose monitoring. J Biomed Opt. 2018, 23(5): 1-9.
43. Aihara M, Kubota N, Minami T, et al. Association between tear and blood glucose concentrations: random intercept model adjusted with confounders in tear samples negative for occult blood. J Diabetes Investig. 2021, 12(2): 266-276.
44. Lee S H, Cho Y C, Bin C Y. Noninvasive self-diagnostic device for tear collection and glucose measurement. Sci Rep, 2019, 9(1): 4747.
45. Sempionatto J R, Brazaca L C, García-Carmona L, et al. Eyeglasses-based tear biosensing system: non-invasive detection of alcohol, vitamins and glucose. Biosens Bioelectron, 2019, 137: 161-170.
46. Han J H, Cho Y C, Koh W G, et al. Preocular sensor system for concurrent monitoring of glucose levels and dry eye syndrome using tear fluids. PLoS One, 2020, 15(10): e0239317.
47. Lin Y R, Hung C C, Chiu H Y, et al. Noninvasive glucose monitoring with a contact lens and smartphone. Sensors (Basel), 2018, 18(10): 3208.
48. Li X, Huang X, Mo J, et al. A Fully integrated closed-loop system based on mesoporous microneedles-iontophoresis for diabetes treatment. Adv Sci (Weinh), 2021, 8(16): e2100827.
49. Cheng S, Gu Z, Zhou L, et al. Recent progress in intelligent wearable sensors for health monitoring and wound healing based on biofluids. Front Bioeng Biotechnol, 2021, 9: 765987.
50. Zhang S, Zeng J, Wang C, et al. The application of wearable glucose sensors in point-of-care testing. Front Bioeng Biotechnol, 2021, 9: 774210.
51. Teymourian H, Moonla C, Tehrani F, et al. Microneedle-based detection of ketone bodies along with glucose and lactate: toward real-time continuous interstitial fluid monitoring of diabetic ketosis and ketoacidosis. Anal Chem, 2020, 92(2): 2291-2300.
52. Lipani L, Dupont B G R, Doungmene F, et al. Non-invasive, transdermal, path-selective and specific glucose monitoring via a graphene-based platform. Nat Nanotechnol, 2018, 13(6): 504-511.
53. Hakala T A, García Pérez A, Wardale M, et al. Sampling of fluid through skin with magnetohydrodynamics for noninvasive glucose monitoring. Sci Rep, 2021, 11(1): 7609.
54. Ye S, Feng S, Huang L, et al. Recent progress in wearable biosensors: from healthcare monitoring to sports analytics. Biosensors (Basel), 2020, 10(12): 205.
55. Moyer J, Wilson D, Finkelshtein I, et al. Correlation between sweat glucose and blood glucose in subjects with diabetes. Diabetes Technol Ther, 2012, 14(5): 398-402.
56. Bandodkar A J, Jeang W J, Ghaffari R, et al. Wearable sensors for biochemical sweat analysis. Annu Rev Anal Chem (Palo Alto Calif), 2019, 12(1): 1-22.
57. Shu Y, Su T, Lu Q, et al. Highly stretchable wearable electrochemical sensor based on Ni-Co MOF nanosheet-decorated Ag/rGO/PU fiber for continuous sweat glucose detection. Anal Chem, 2021, 93(48): 16222-16230.
58. Zhao Y, Zhai Q, Dong D, et al. Highly stretchable and strain-insensitive fiber-based wearable electrochemical biosensor to monitor glucose in the sweat. Anal Chem, 2019, 91(10): 6569-6576.
59. Vaquer A, Barón E, Rica R. Detection of low glucose levels in sweat with colorimetric wearable biosensors. Analyst. 2021, 146(10): 3273-3279.
60. Boscari F, Vettoretti M, Cavallin F, et al. Implantable and transcutaneous continuous glucose monitoring system: a randomized cross over trial comparing accuracy efficacy and acceptance. J Endocrinol Invest, 2022, 45(1): 115-124.

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