Molecular enantiorecognition of <scp>l</scp>‐glucose and <scp>d</scp>‐glucose in whole blood samples

Staden, Raluca‐Ioana Stefan‐van; Mitrofan, Grigorina

doi:10.1002/chir.22843

Cited by 7 publications

(4 citation statements)

References 20 publications

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“…Lglucose and D-glucose have the same chemical formula (C6H12O6), but the molecular arrangement is different. The amount of L-glucose in a complex living organism such as human blood is very negligible (in order of nm/dL) compared to the amount of D-glucose (in order of mg/dL) [31], and commercially available glucose meters rely on measuring Dglucose levels within the blood [31].…”

Section: B Experimental Methodsmentioning

confidence: 99%

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

et al. 2021

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Diabetes is a major public health challenge affecting more than 451 million people. Physiological and experimental factors influence the accuracy of non-invasive glucose monitoring, and these need to be overcome before replacing the finger prick method. Also, the suitable employment of machine learning techniques can significantly improve the accuracy of glucose predictions. One aim of this study is to use light sources with multiple wavelengths to enhance the sensitivity and selectivity of glucose detection in an aqueous solution. Multiple wavelength measurements have the potential to compensate for errors associated with inter-and intra-individual differences in blood and tissue components. In this study, the transmission measurements of a custom built optical sensor are examined using 18 different wavelengths between 410 and 940 nm. Results show a high correlation value (0.98) between glucose concentration and transmission intensity for four wavelengths (485, 645, 860 and 940 nm). Five machine learning methods are investigated for glucose predictions. When regression methods are used, 9% of glucose predictions fall outside the correct range (normal, hypoglycemic or hyperglycemic). The prediction accuracy is improved by applying classification methods on sets of data arranged into 21 classes. Data within each class corresponds to a discrete 10 mg/dL glucose range. Classification based models outperform regression, and among them, the support vector machine is the most successful with F1-score of 99%. Additionally, Clarke error grid shows that 99.75% of glucose readings fall within the clinically acceptable zones. This is an important step towards critical diagnosis during an emergency patient situation.

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Section: B Experimental Methodsmentioning

confidence: 99%

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

et al. 2021

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“…The other enantiomer, i.e., l-glucose, is not naturally occurring but can be synthesized in a lab. It is generally accepted that l-glucose is not found in living organisms, but Stefan et al [ 113 ] detected a negligible amount in human blood. The amount of rotation can be represented by the following equation:

where

is the measured optical rotation,

is the rotary power of with respect to wavelength

and temperature T , C is the concentration of glucose, and

is the optical path length [ 114 ].…”

Section: Noninvasive Sensing Techniquesmentioning

confidence: 99%

Noninvasive Glucose Sensing In Vivo

Leung

Forlenza

Prioleau

et al. 2023

Sensors

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Blood glucose monitoring is an essential aspect of disease management for individuals with diabetes. Unfortunately, traditional methods require collecting a blood sample and thus are invasive and inconvenient. Recent developments in minimally invasive continuous glucose monitors have provided a more convenient alternative for people with diabetes to track their glucose levels 24/7. Despite this progress, many challenges remain to establish a noninvasive monitoring technique that works accurately and reliably in the wild. This review encompasses the current advancements in noninvasive glucose sensing technology in vivo, delves into the common challenges faced by these systems, and offers an insightful outlook on existing and future solutions.

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“…This relationship we proved now for more than 100 substances determined using the stochastic mode. [8][9][10] The unknown concentrations of bisphenols in water samples were determined by inserting the value of 1/t on in the equation of calibration of each bisphenol, for each of the stochastic sensors designed.…”

Section: Design Of the Stochastic Sensorsmentioning

confidence: 99%

“…Stochastic sensors are a good alternative for the classical methods of analysis, especially for the assay of small quantities of compounds from complex matrices due to the following advantages: their response does not depend on the nature of matrix; a qualitative analysis as well as a reliable quantitative analysis can be performed using these sensors. [8][9][10] Experimental Materials and reagents.-All reagents were of analytical grade and solutions were prepared using deionized water obtained from a Direct-Q 3 Water Purification System (Millipore Corporation, France). 2,2-diphenyl-1-picrylhydrazyl, dimethylsulfoxide, nanocarbon powder (nC), nanographene powder (nGr) and reduced graphene oxide powder (rGOx) were purchased from Sigma-Aldrich (Milwaukee, USA), tetrahydrofuran from Merck (Darmstadt, Germany), and paraffin oil from Fluka (Buchs, Switerland).…”

mentioning

confidence: 99%

Nanostructured Materials Used for Pattern Recognition of Bisphenols in Waste Water Samples

Staden¹,

Moscalu-Lungu²,

Staden³

2019

J. Electrochem. Soc.

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Three stochastic sensors based on modified nanocarbon paste (nC), nanographene paste (nGr), and reduced graphene oxide paste (rGOx) with 2, 2-diphenyl-1-picrylhydrazyl (DPPH) were proposed for the pattern recognition of bisphenols A (BPA), F (BPF), and Z (BPZ) in waste water samples. The linear concentration ranges recorded were: for BPA between 1fmol/L and 10μmol/L, for BPF between 10fmol/L and 1μmol/L, and for BPZ between 10fmol/L and 0.1mmol/L. The lowest limits of determination for each bisphenol were as following: 1fmol/L for BPA when the stochastic sensor based on nC was used, 10fmol/L for BPF when the stochastic sensors based on nC, nGr, and rGOx were used, and for BPZ 10gmol/L when the sensor based on nGr was used. The recoveries of the BPA, BPF, and BPZ were higher than 98.00% with relative standard deviation less than 0.20% (N = 10).

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Molecular enantiorecognition of l‐glucose and d‐glucose in whole blood samples

Cited by 7 publications

References 20 publications

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Non-Invasive Glucose Monitoring Using Optical Sensor and Machine Learning Techniques for Diabetes Applications

Noninvasive Glucose Sensing In Vivo

Nanostructured Materials Used for Pattern Recognition of Bisphenols in Waste Water Samples

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