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Oxidation-Reduction

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Dry olive leaf extract attenuates DNA damage induced by estradiol and diethylstilbestrol in human peripheral blood cells in vitro.

Mutation research. Genetic toxicology and environmental mutagenesis
Phenolic groups of steroidal or nonsteroidal estrogens can redox cycle, leading to oxidative stress, where creation of reactive oxygen species are recognized as the main mechanism of their DNA damage properties. Dry olive (Olea europaea L.) leaf extr...

Exploring the phenolic profile, antioxidant, antidiabetic and anti-hemolytic potential of Prunus avium vegetal parts.

Food research international (Ottawa, Ont.)
The aim of the present work was to evaluate the phenolic profile of leaves, stems and flowers of P. avium and their biological potential. For this purpose, two extracts of each matrix (hydroethanolic and infusion) were prepared. A total of twenty-six...

Kinetic study of dye removal using TiO supported on polyethylene terephthalate by advanced oxidation processes through neural networks.

Water science and technology : a journal of the International Association on Water Pollution Research
This work investigated the efficiency of polyethylene terephthalate (PET) as support material for TiO films in the photocatalytic degradation of red Bordeaux and yellow tartrazine dyes. The optimum operating conditions were determined by a factorial ...

BP-ANN Model Coupled with Particle Swarm Optimization for the Efficient Prediction of 2-Chlorophenol Removal in an Electro-Oxidation System.

International journal of environmental research and public health
Electro-oxidation is an effective approach for the removal of 2-chlorophenol from wastewater. The modeling of the electrochemical process plays an important role in improving the efficiency of electrochemical treatment and increasing our understandin...

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Scientific reports
Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model is to carefully select the optimal feature encoding for the problem at hand. Biophysical propensity s...

Modeling and optimization of imidacloprid degradation by catalytic percarbonate oxidation using artificial neural network and Box-Behnken experimental design.

Chemosphere
Due to its toxicity and persistence, pesticide pollution poses a serious threat to human health and the environment. Imidacloprid or IMD is an archetypal neonicotinoid insecticide commonly used to protect a variety of crops worldwide. The present stu...

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...

Machine-Learning-Enabled Exploration of Morphology Influence on Wire-Array Electrodes for Electrochemical Nitrogen Fixation.

The journal of physical chemistry letters
Neural networks, trained on data generated by a microkinetic model and finite-element simulations, expand explorable parameter space by significantly accelerating the predictions of electrocatalytic performance. In addition to modeling electrode reac...

A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

Biomolecules
In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature () and burnout index () were selected as ...