AIMC Topic: Glucose
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Modification and applications of glucose oxidase: optimization strategies and high-throughput screening technologies.
World journal of microbiology & biotechnology
Jul 12, 2025
Glucose oxidase (GOD), an oxidoreductase (EC 1.1.3.4), catalyzes the oxidation of β-D-glucose to gluconic acid using molecular oxygen as the electron acceptor, with concomitant generation of hydrogen peroxide. Owing to its versatile catalytic propert...
Targeting PFKFB3 to restore glucose metabolism in acute pancreatitis via nanovesicle delivery.
Molecular medicine (Cambridge, Mass.)
Jul 5, 2025
BACKGROUND: Acute pancreatitis (AP) is a severe inflammatory disease frequently accompanied by disturbances in glucose metabolism, which further complicate the disease prognosis. This study aims to explore the role of PFKFB3, a key glycolytic enzyme,...
Dynamic mode decomposition for analysis and prediction of metabolic oscillations from time-lapse imaging of cellular autofluorescence.
Scientific reports
Jul 2, 2025
Oscillations are a common phenomenon in cell biology. They are based on non-linear coupling of biochemical reactions and can show rich dynamic behavior as found in, for example, glycolysis of yeast cells. Here, we show that dynamic mode decomposition...
Spatial patterns of hepatocyte glucose flux revealed by stable isotope tracing and multi-scale microscopy.
Nature communications
Jul 1, 2025
Metabolic homeostasis requires engagement of catabolic and anabolic pathways consuming nutrients that generate and consume energy and biomass. Our current understanding of cell homeostasis and metabolism, including how cells utilize nutrients, comes ...
In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.
Expert opinion on drug discovery
Jun 28, 2025
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).
Fully Wearable Devices for Real-Time Health Monitoring and Multimodal Sensing in Nanomedicine Using Multiplexed Green Biofuels.
Nano letters
Jun 9, 2025
Fully wearable devices are crucial for real-time health monitoring, but existing devices often lack stable power, on-site signal processing, and multimodal sensing. To overcome these limitations, we introduce the first self-powered and fully wearable...
Multimodal Wearable Sensing for Biomechanics and Biomolecules Enabled by the M-MPM/VCFs@Ag Interface with Machine Learning Pipeline.
ACS sensors
Jun 6, 2025
The addition sensing device of sweat to wearable biostress sensors would eliminate the need for using multiple gadgets for healthcare analysis. Due to the distinct package fashion of sensor interface for biostress and biomolecule, achieving permeabil...
Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches.
Ecotoxicology and environmental safety
Jun 5, 2025
BACKGROUND: Sarcopenia, a condition marked by the decline of skeletal muscle mass and function, is prevalent in the elderly and closely linked to abnormal glucose metabolism, particularly type 2 diabetes. Hyperglycemia can increase the formation of a...
A Genetic Algorithms-Based Neural Network Model to Monitor Gibberellic Acid GA3 Fermentation Process by Fusarium fujikuroi.
Biotechnology and bioengineering
May 8, 2025
A genetic algorithm-optimized neural network (ANN-GA) was developed for real-time monitoring of gibberellin (GA3) production during Fusarium fujikuroi fermentation. This model addresses the limitations of traditional off-line detection methods, such ...
Transfer learning and data augmentation for glucose concentration prediction from colorimetric biosensor images.
Mikrochimica acta
Apr 8, 2025
A deep learning algorithm is introduced to accurately predict glucose concentrations using colorimetric paper sensor (CPS) images. We used an image dataset from CPS treated with five different glucose concentrations as input for deep learning models....
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