Literature DB >> 26524683

Whole blood glucose analysis based on smartphone camera module.

Jasmine Pramila Devadhasan1, Hyunhee Oh2, Cheol Soo Choi2, Sanghyo Kim3.   

Abstract

Complementary metal oxide semiconductor (CMOS) image sensors have received great attention for their high efficiency in biological applications. The present work describes a CMOS image sensor-based whole blood glucose monitoring system through a point-of-care (POC) approach. A simple poly-ethylene terephthalate (PET) chip was developed to carry out the enzyme kinetic reaction at various concentrations (110–586 mg∕dL) of mouse blood glucose. In this technique, assay reagent is immobilized onto amine functionalized silica (AFSiO2) nanoparticles as an electrostatic attraction in order to achieve glucose oxidation on the chip. The assay reagent immobilized AFSiO2 nanoparticles develop a semi-transparent reaction platform, which is technically a suitable chip to analyze by a camera module. The oxidized glucose then produces a green color according to the glucose concentration and is analyzed by the camera module as a photon detection technique; the photon number decreases when the glucose concentration increases. The combination of these components, the CMOS image sensor and enzyme immobilized PET film chip, constitute a compact, accurate, inexpensive, precise, digital, highly sensitive, specific, and optical glucose-sensing approach for POC diagnosis.

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Year:  2015        PMID: 26524683     DOI: 10.1117/1.JBO.20.11.117001

Source DB:  PubMed          Journal:  J Biomed Opt        ISSN: 1083-3668            Impact factor:   3.170


  24 in total

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Journal:  J Hazard Mater       Date:  2010-04-03       Impact factor: 10.588

2.  STED nanoscopy combined with optical tweezers reveals protein dynamics on densely covered DNA.

Authors:  Iddo Heller; Gerrit Sitters; Onno D Broekmans; Géraldine Farge; Carolin Menges; Wolfgang Wende; Stefan W Hell; Erwin J G Peterman; Gijs J L Wuite
Journal:  Nat Methods       Date:  2013-08-11       Impact factor: 28.547

3.  Smartphone based health accessory for colorimetric detection of biomarkers in sweat and saliva.

Authors:  Vlad Oncescu; Dakota O'Dell; David Erickson
Journal:  Lab Chip       Date:  2013-06-19       Impact factor: 6.799

Review 4.  Factors affecting blood glucose monitoring: sources of errors in measurement.

Authors:  Barry H Ginsberg
Journal:  J Diabetes Sci Technol       Date:  2009-07-01

5.  CMOS image sensor for detection of interferon gamma protein interaction as a point-of-care approach.

Authors:  Mohana Marimuthu; Karthikeyan Kandasamy; Chang Geun Ahn; Gun Yong Sung; Min-Gon Kim; Sanghyo Kim
Journal:  Anal Bioanal Chem       Date:  2011-07-20       Impact factor: 4.142

6.  CMOS image sensors as an efficient platform for glucose monitoring.

Authors:  Jasmine Pramila Devadhasan; Sanghyo Kim; Cheol Soo Choi
Journal:  Analyst       Date:  2013-10-07       Impact factor: 4.616

7.  Glucose biosensor based on covalent immobilization of enzyme in sol-gel composite film combined with Prussian blue/carbon nanotubes hybrid.

Authors:  Guanglei Fu; Xiuli Yue; Zhifei Dai
Journal:  Biosens Bioelectron       Date:  2011-03-17       Impact factor: 10.618

8.  Microdialysis coupled with an embedded systems controller and CMOS image sensor.

Authors:  Alan John Rosenbloom; Heer Robin Gandhi; George Lopez Subrebost
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2009

9.  Optical colorimetric sensor strip for direct readout glucose measurement.

Authors:  Xu-dong Wang; Hai-xu Chen; Ting-yao Zhou; Zhi-jie Lin; Jing-bin Zeng; Zhao-xiong Xie; Xi Chen; Kwok-yin Wong; Guo-nan Chen; Xiao-ru Wang
Journal:  Biosens Bioelectron       Date:  2009-05-21       Impact factor: 10.618

Review 10.  Fish-on-a-chip: a sensitive detection microfluidic system for Alzheimer's disease.

Authors:  Jasmine P Devadhasan; Sanghyo Kim; Jeongho An
Journal:  J Biomed Sci       Date:  2011-05-28       Impact factor: 8.410

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  3 in total

1.  Design, fabrication, and feasibility analysis of a colorimetric detection system with a smartphone for self-monitoring blood glucose.

Authors:  Hung-Chih Wang; Fuh-Yu Chang; Tung-Meng Tsai; Chieh-Hsiao Chen; Yen-Yu Chen
Journal:  J Biomed Opt       Date:  2019-02       Impact factor: 3.170

2.  Noninvasive Hemoglobin Level Prediction in a Mobile Phone Environment: State of the Art Review and Recommendations.

Authors:  Md Kamrul Hasan; Md Hasanul Aziz; Md Ishrak Islam Zarif; Mahmudul Hasan; Mma Hashem; Shion Guha; Richard R Love; Sheikh Ahamed
Journal:  JMIR Mhealth Uhealth       Date:  2021-04-08       Impact factor: 4.773

Review 3.  Smartphone-based clinical diagnostics: towards democratization of evidence-based health care.

Authors:  I Hernández-Neuta; F Neumann; J Brightmeyer; T Ba Tis; N Madaboosi; Q Wei; A Ozcan; M Nilsson
Journal:  J Intern Med       Date:  2018-09-12       Impact factor: 8.989

  3 in total

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