AIMC Topic: Glucose

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A regression-based machine learning approach for pH and glucose detection with redox-sensitive colorimetric paper sensors.

Analytical methods : advancing methods and applications
Colorimetric paper sensors are used in various fields due to their convenience and intuitive manner. However, these sensors present low accuracy in practical use because it is difficult to distinguish color changes for a minute amount of analyte with...

Machine Learning with Neural Networks to Enhance Selectivity of Nonenzymatic Electrochemical Biosensors in Multianalyte Mixtures.

ACS applied materials & interfaces
Nonenzymatic biosensors hold great potential in the field of analysis and detection due to long-term stability, high sensitivity, and low cost. However, the relative low selectivity, especially the overlapped oxidation peaks of biomarkers, in the bio...

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.

Frontiers in public health
OBJECTIVE: The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the ...

Circulating serum metabolites as predictors of dementia: a machine learning approach in a 21-year follow-up of the Whitehall II cohort study.

BMC medicine
BACKGROUND: Age is the strongest risk factor for dementia and there is considerable interest in identifying scalable, blood-based biomarkers in predicting dementia. We examined the role of midlife serum metabolites using a machine learning approach a...

Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.

Computational and mathematical methods in medicine
We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20-64 years) from Tokyo and surrounding areas were recruited...

Machine Learning Methods of Regression for Plasmonic Nanoantenna Glucose Sensing.

Sensors (Basel, Switzerland)
The measurement and quantification of glucose concentrations is a field of major interest, whether motivated by potential clinical applications or as a prime example of biosensing in basic research. In recent years, optical sensing methods have emerg...

Improvement of the Force Field for -d-Glucose with Machine Learning.

Molecules (Basel, Switzerland)
While the construction of a dependable force field for performing classical molecular dynamics (MD) simulation is crucial for elucidating the structure and function of biomolecular systems, the attempts to do this for glycans are relatively sparse co...

Genetic dissection of complex traits using hierarchical biological knowledge.

PLoS computational biology
Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical bio...

An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from , Decreases HbA1c in a Prediabetic Population.

Nutrients
The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose l...

A Zwitterionic-Aromatic Motif-Based ionic skin for highly biocompatible and Glucose-Responsive sensor.

Journal of colloid and interface science
Electronic skins that can sense external stimuli have been of great significance in artificial intelligence and smart wearable devices in recent years. However, most of current skin materials are unable to achieve high biocompatibility and anti-bacte...