AIMC Topic: Oxidative Stress

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Artificial Intelligence in Decrypting Cytoprotective Activity under Oxidative Stress from Molecular Structure.

International journal of molecular sciences
Artificial intelligence (AI) is widely explored nowadays, and it gives opportunities to enhance classical approaches in QSAR studies. The aim of this study was to investigate the cytoprotective activity parameter under oxidative stress conditions for...

Principal component analysis-multivariate adaptive regression splines (PCA-MARS) and back propagation-artificial neural network (BP-ANN) methods for predicting the efficiency of oxidative desulfurization systems using ATR-FTIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Oxidative desulfurization (ODS) of diesel fuels has received attention in recent years due to mild working conditions and effective removal of the aromatic sulfur compounds. There is a need for rapid, accurate, and reproducible analytical tools to mo...

Virtual Screening of Nrf2 Dietary-Derived Agonists and Safety by a New Deep-Learning Model and Verified and .

Journal of agricultural and food chemistry
Nuclear factor (erythroid-derived 2)-like 2 (Nrf2) is an essential regulatory target of antioxidants, but the lack of Nrf2 active site information has hindered discovery of new Nrf2 agonists from food-derived compounds by large-scale virtual screenin...

The antidiabetic drug pioglitazone ameliorates betel-nut-induced carcinogenesis in mice by restoring normal lipid metabolism, reducing oxidative stress, and inducing apoptosis.

Journal of cancer research and therapeutics
CONTEXT: Oral administration (2 mg mL-1) of aqueous extract of betel nut (AEBN) for 24 weeks induced oncogenic alterations in the liver of female Swiss Albino mice concomitant with aberrant lipid metabolism, overactivation of Akt/mTOR signaling, and ...

Machine Learning Predicts the Oxidative Stress Subtypes Provide an Innovative Insight into Colorectal Cancer.

Oxidative medicine and cellular longevity
So far, it has been reached the academic consensus that the molecular subtypes are via genomic heterogeneity and immune infiltration patterns. Considering that oxidative stress (OS) is involved in tumorigenesis and prognosis prediction, we propose an...

Influences of Edaravone on Necroptosis-Related Proteins and Oxidative Stress in Rats with Lower Extremity Ischemia/Reperfusion Injury.

Cellular and molecular biology (Noisy-le-Grand, France)
The study aimed to investigate the influences of edaravone on necroptosis-related proteins and oxidative stress in rats with lower extremity ischemia/reperfusion (I/R) injury. The normal group (n=10), model group (lower extremity I/R injury model, n=...

Effect of clove on improving running ability in aging mice.

Journal of food biochemistry
In this study, a D-galactose-induced aging mouse model was established, and Syringa oblata Lindl. extract (SOLE) was administered orally to observe the effect and mechanism of SOLE on the running ability of aging mice. The role of SOLE was evaluated ...

Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network.

Oxidative medicine and cellular longevity
Most multicellular organisms require apoptosis, or programmed cell death, to function properly and survive. On the other hand, morphological and biochemical characteristics of apoptosis have remained remarkably consistent throughout evolution. Apopto...

Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation.

Journal of hazardous materials
Epidemiology studies have indicated that environmental cadmium exposure, even at low levels, will result in chronic cadmium accumulation in the kidney with profound adverse consequences and that the diabetic population is more susceptible. However, t...

Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art.

Chemico-biological interactions
Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such model...