Page 64 - Read Online
P. 64

Jung et al. Soft Sci 2024;4:15  https://dx.doi.org/10.20517/ss.2024.02          Page 43 of 44

               247.      Hong YJ, Lee H, Kim J, et al. Multifunctional wearable system that integrates sweat-based sensing and vital-sign monitoring to
                    estimate pre-/post-exercise glucose levels. Adv Funct Mater 2018;28:1805754.  DOI
               248.      Gil B, Anastasova S, Yang GZ. A smart wireless ear-worn device for cardiovascular and sweat parameter monitoring during physical
                    exercise: design and performance results. Sensors 2019;19:1616.  DOI  PubMed  PMC
               249.      Yu Y, Nassar J, Xu C, et al. Biofuel-powered soft electronic skin with multiplexed and wireless sensing for human-machine
                    interfaces. Sci Robot 2020;5:eaaz7946.  DOI  PubMed  PMC
               250.      Sempionatto JR, Lin M, Yin L, et al. An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic
                    biomarkers. Nat Biomed Eng 2021;5:737-48.  DOI
               251.      Sempionatto JR, Nakagawa T, Pavinatto A, et al. Eyeglasses based wireless electrolyte and metabolite sensor platform. Lab Chip
                    2017;17:1834-42.  DOI  PubMed  PMC
               252.      Park J, Sempionatto JR, Kim J, et al. Microscale biosensor array based on flexible polymeric platform toward lab-on-a-needle: real-
                    time multiparameter biomedical assays on curved needle surfaces. ACS Sens 2020;5:1363-73.  DOI
               253.      Yokus MA, Songkakul T, Pozdin VA, Bozkurt A, Daniele MA. Wearable multiplexed biosensor system toward continuous
                    monitoring of metabolites. Biosens Bioelectron 2020;153:112038.  DOI  PubMed
               254.      Misra S, Oliver NS. Utility of ketone measurement in the prevention, diagnosis and management of diabetic ketoacidosis. Diabet Med
                    2015;32:14-23.  DOI  PubMed
               255.      Forrow NJ, Sanghera GS, Walters SJ, Watkin JL. Development of a commercial amperometric biosensor electrode for the ketone D-
                    3-hydroxybutyrate. Biosens Bioelectron 2005;20:1617-25.  DOI  PubMed
               256.      Wang CC, Hennek JW, Ainla A, et al. A paper-based “pop-up” electrochemical device for analysis of beta-hydroxybutyrate. Anal
                    Chem 2016;88:6326-33.  DOI  PubMed  PMC
               257.      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;92:2291-300.  DOI  PubMed
               258.      Moon JM, Del Caño R, Moonla C, et al. Self-testing of ketone bodies, along with glucose, using touch-based sweat analysis. ACS
                    Sens 2022;7:3973-81.  DOI
               259.      Vargas E, Teymourian H, Tehrani F, et al. Enzymatic/immunoassay dual-biomarker sensing chip: towards decentralized insulin/
                    glucose detection. Angew Chem Int Ed Engl 2019;58:6376-9.  DOI  PubMed
               260.      Liu S, Shen Z, Deng L, Liu G. Smartphone assisted portable biochip for non-invasive simultaneous monitoring of glucose and insulin
                    towards precise diagnosis of prediabetes/diabetes. Biosens Bioelectron 2022;209:114251.  DOI
               261.      Buxton OM, Cain SW, O’Connor SP, et al. Adverse metabolic consequences in humans of prolonged sleep restriction combined with
                    circadian disruption. Sci Transl Med 2012;4:129ra43.  DOI  PubMed  PMC
               262.      Munje RD, Muthukumar S, Prasad S. Lancet-free and label-free diagnostics of glucose in sweat using Zinc Oxide based flexible
                    bioelectronics. Sen Actuators B Chem 2017;238:482-90.  DOI
               263.      Tian G, Zhou Z, Li M, Li X, Xu T, Zhang X. Oriented antibody-assembled metal-organic frameworks for persistent wearable sweat
                    cortisol detection. Anal Chem 2023;95:13250-7.  DOI  PubMed
               264.      Wu G. Amino acids: metabolism, functions, and nutrition. Amino Acids 2009;37:1-17.  DOI  PubMed
               265.      Kim J, Sempionatto JR, Imani S, et al. Simultaneous monitoring of sweat and interstitial fluid using a single wearable biosensor
                    platform. Adv Sci 2018;5:1800880.  DOI  PubMed  PMC
               266.      Tehrani F, Teymourian H, Wuerstle B, et al. An integrated wearable microneedle array for the continuous monitoring of multiple
                    biomarkers in interstitial fluid. Nat Biomed Eng 2022;6:1214-24.  DOI
               267.      Wang M, Yang Y, Min J et al. A wearable electrochemical biosensor for the monitoring of metabolites and nutrients. Nat Biomed
                    Eng 2022;6:1225-35.  DOI  PubMed  PMC
               268.      Rodríguez-Rodríguez I, Rodríguez JV, Chatzigiannakis I, Zamora Izquierdo MÁ. On the possibility of predicting glycaemia ‘on the
                    fly’ with constrained iot devices in type 1 diabetes mellitus patients. Sensors 2019;19:4538.  DOI  PubMed  PMC
               269.      Sudharsan B, Peeples M, Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J
                    Diabetes Sci Technol 2015;9:86-90.  DOI  PubMed  PMC
               270.      Reifman J, Rajaraman S, Gribok A, Ward WK. Predictive monitoring for improved management of glucose levels. J Diabetes Sci
                    Technol 2007;1:478-86.  DOI  PubMed  PMC
               271.      Li K, Liu C, Zhu T, Herrero P, Georgiou P. GluNet: a deep learning framework for accurate glucose forecasting. IEEE J Biomed
                    Health Inform 2020;24:414-23.  DOI  PubMed
               272.      Gu W, Zhou Y, Zhou Z, et al. SugarMate: non-intrusive blood glucose monitoring with smartphones. Proc ACM Interact Mob
                    Wearable Ubiquitous Technol 2017;1:1-27.  DOI
               273.      Beauchamp J, Bunescu R, Marling C, Li Z, Liu C. LSTMs and deep residual networks for carbohydrate and bolus recommendations
                    in type 1 diabetes management. Sensors 2021;21:3303.  DOI  PubMed  PMC
               274.      Li K, Daniels J, Liu C, Herrero P, Georgiou P. Convolutional recurrent neural networks for glucose prediction. IEEE J Biomed
                    Health Inform 2020;24:603-13.  DOI  PubMed
               275.      Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An insulin bolus advisor for type 1 diabetes using deep reinforcement learning.
                    Sensors 2020;20:5058.  DOI  PubMed  PMC
               276.      Aliberti A, Pupillo I, Terna S, et al. A multi-patient data-driven approach to blood glucose prediction. IEEE Access 2019;7:69311-25.
                    DOI
   59   60   61   62   63   64   65   66   67   68   69