Literature DB >> 33902999

A review of biosensor technology and algorithms for glucose monitoring.

Yaguang Zhang1, Jingxue Sun1, Liansheng Liu2, Hong Qiao3.   

Abstract

Diabetes mellitus (DM) has become a serious illness in the whole world. Until now, there is no effective cure for patients with DM. It is well known that the glucose level is one key factor to determine the progress of DM. It is also an important reference to carry out the accurate and timely treatment for patients with DM. In this article, the related biosensors technology that can be utilized to identify and predict glucose level are reviewed in detail, including the algorithms that can help to achieve numerical value of glucose level. Firstly, the biosensor technology based on the physiological fluids are illustrated, including blood, sweat, interstitial fluid, ocular fluid, and other available fluids. Secondly, the algorithms for achieving numerical value of glucose level are investigated, including the physiological model-based method and the machine learning-based method. Finally, the future development trend and challenges of glucose level monitoring are given and the conclusions are drawn.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Algorithms for glucose analysis; Biosensor technology; Diabetes mellitus; Glucose monitoring

Mesh:

Substances:

Year:  2021        PMID: 33902999     DOI: 10.1016/j.jdiacomp.2021.107929

Source DB:  PubMed          Journal:  J Diabetes Complications        ISSN: 1056-8727            Impact factor:   2.852


  5 in total

1.  Development of Nanocomposite Materials Based on Conductive Polymers for Using in Glucose Biosensor.

Authors:  Lyubov S Kuznetsova; Vyacheslav A Arlyapov; Olga A Kamanina; Elizaveta A Lantsova; Sergey E Tarasov; Anatoly N Reshetilov
Journal:  Polymers (Basel)       Date:  2022-04-11       Impact factor: 4.967

Review 2.  Advances in Medical Wearable Biosensors: Design, Fabrication and Materials Strategies in Healthcare Monitoring.

Authors:  Sangeeth Pillai; Akshaya Upadhyay; Darren Sayson; Bich Hong Nguyen; Simon D Tran
Journal:  Molecules       Date:  2021-12-28       Impact factor: 4.411

3.  Correlations of Salivary and Blood Glucose Levels among Six Saliva Collection Methods.

Authors:  Yangyang Cui; Hankun Zhang; Jia Zhu; Zhenhua Liao; Song Wang; Weiqiang Liu
Journal:  Int J Environ Res Public Health       Date:  2022-03-30       Impact factor: 3.390

Review 4.  Electrospun nanofiber-based glucose sensors for glucose detection.

Authors:  Yutong Du; Xinyi Zhang; Ping Liu; Deng-Guang Yu; Ruiliang Ge
Journal:  Front Chem       Date:  2022-08-11       Impact factor: 5.545

Review 5.  Commercial and Scientific Solutions for Blood Glucose Monitoring-A Review.

Authors:  Yirui Xue; Angelika S Thalmayer; Samuel Zeising; Georg Fischer; Maximilian Lübke
Journal:  Sensors (Basel)       Date:  2022-01-06       Impact factor: 3.576

  5 in total

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