AIMC Topic: Glutathione

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Tb(III)-Functionalized Hydrogen-Bonded Organic Framework with Dual-Emission for Liver Health Biomarker Detection and a Smartphone-Integrated Bionic Visual Diagnostic Platform.

Analytical chemistry
Developing a sensitive analytical platform for monitoring tiopronin (MPG), its metabolite 2-mercaptopropionic acid (MPA), and the key liver biomarker glutathione (GSH) is crucial for liver health assessment. Here, an artificial intelligence-assisted ...

Kinetic and Mechanistic Discrepancies of Single/Dual-Atom Nanozymes Drive a Triple-Channel Sensing Array for Machine Learning-Assisted Antioxidant Discrimination.

Analytical chemistry
Current colorimetric sensing arrays for antioxidant detection often struggle with discrimination due to cross-reactive signals from individual nanozymes. These signals are typically modulated by external factors such as pH or chromogenic substrates, ...

A hybrid machine learning framework for functional annotation of mitochondrial glutathione transport and metabolism proteins in cancers.

BMC bioinformatics
BACKGROUND: Alterations of metabolism, including changes in mitochondrial metabolism as well as glutathione (GSH) metabolism are a well appreciated hallmark of many cancers. Mitochondrial GSH (mGSH) transport is a poorly characterized aspect of GSH m...

Bioinspired Iron Porphyrin Covalent Organic Frameworks-Based Nanozymes Sensor Array: Machine Learning-Assisted Identification and Detection of Thiols.

ACS applied materials & interfaces
Given the crucial role of thiols in maintaining normal physiological functions, it is essential to establish a high-throughput and sensitive analytical method to identify and quantify various thiols accurately. Inspired by the iron porphyrin active c...

Predicting lifespan-extending chemical compounds for with machine learning and biologically interpretable features.

Aging
Recently, there has been a growing interest in the development of pharmacological interventions targeting ageing, as well as in the use of machine learning for analysing ageing-related data. In this work, we use machine learning methods to analyse da...

Deep learning algorithm reveals two prognostic subtypes in patients with gliomas.

BMC bioinformatics
BACKGROUND: Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-s...

Deep learning based prediction of species-specific protein S-glutathionylation sites.

Biochimica et biophysica acta. Proteins and proteomics
As a widespread and reversible post-translational modification of proteins, S-glutathionylation specifically generates the mixed disulfides between cysteine residues and glutathione, which regulates various biological processes including oxidative st...

Discovery of Small-Molecule Activators for Glucose-6-Phosphate Dehydrogenase (G6PD) Using Machine Learning Approaches.

International journal of molecular sciences
Glucose-6-Phosphate Dehydrogenase (G6PD) is a ubiquitous cytoplasmic enzyme converting glucose-6-phosphate into 6-phosphogluconate in the pentose phosphate pathway (PPP). The G6PD deficiency renders the inability to regenerate glutathione due to lack...

Micelle-dominated distribution strategy for non-matrix matched calibration without an internal standard: "Extract-and-shoot" approach for analyzing hydrophilic targets in blood and cell samples.

Analytica chimica acta
The analysis of trace hydrophilic targets in complex aqueous-rich matrices is considerably challenging, generally requiring matrix-matched calibration, internal standard, or time-and-labor-intensive sample preparation. To address this analytical bott...