AI Medical Compendium Journal:
Toxicology letters

Showing 1 to 7 of 7 articles

Study on the effect and mechanism of PM on the expression of alzheimer's-like lesions related proteins in SH-SY5Y cells.

Toxicology letters
Fine particulate matter (PM) is recognized as one of the most harmful environmental pollutants to human health. Current research indicates that PM exhibits neurotoxic effects, though the specific mechanisms remain unclear. In this study, SH-SY5Y cell...

Using explainable machine learning to predict the irritation and corrosivity of chemicals on eyes and skin.

Toxicology letters
Contact with specific chemicals often results in corrosive and irritative responses in the eyes and skin, playing a pivotal role in assessing the potential hazards of personal care products, cosmetics, and industrial chemicals to human health. While ...

Establishment of a 13 genes-based molecular prediction score model to discriminate the neurotoxic potential of food relevant-chemicals.

Toxicology letters
Although many neurotoxicity prediction studies of food additives have been developed, they are applicable in a qualitative way. We aimed to develop a novel prediction score that is described quantitatively and precisely. We examined cell viability, r...

Predicting the reproductive toxicity of chemicals using ensemble learning methods and molecular fingerprints.

Toxicology letters
Reproductive toxicity endpoints are a significant safety concern in the assessment of the adverse effects of chemicals in drug discovery. Computational models that can accurately predict a chemical's toxic potential are increasingly pursued to replac...

Prediction of hERG potassium channel blockage using ensemble learning methods and molecular fingerprints.

Toxicology letters
The human ether-a-go-go-related gene (hERG) encodes a tetrameric potassium channel called Kv11.1. This channel can be blocked by certain drugs, which leads to long QT syndrome, causing cardiotoxicity. This is a significant problem during drug develop...

Machine learning prediction of nanoparticle in vitro toxicity: A comparative study of classifiers and ensemble-classifiers using the Copeland Index.

Toxicology letters
Nano-Particles (NPs) are well established as important components across a broad range of products from cosmetics to electronics. Their utilization is increasing with their significant economic and societal potential yet to be fully realized. Inroads...

The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks.

Toxicology letters
Protein kinases (PKs) play a role in many pivotal aspects of cellular function. Dysregulation and mutations of protein kinases are involved in the development of different diseases, which might be treated by inhibition of the corresponding kinase. Pr...