Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/s0166-6851(00)00186-9 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The TOXIN knowledge graph: supporting animal-free risk assessment of cosmetics.
Database (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
View Article and Find Full Text PDFNear-infrared spectroscopy discriminates mass-reared sterile and wild tsetse flies.
PLoS Negl Trop Dis
January 2025
Institut de Recherche en Sciences de la Santé, Direction Régionale, Bobo-Dioulasso, Burkina Faso.
Background: Monitoring the efficacy of the sterile insect technique (SIT) programs, it is desirable to discriminate between wild and sterile tsetse males captured in monitoring traps. Currently, this is primarily achieved by marking sterile males with fluorescent dye powder before release, and identifying them using a fluorescence camera and/or microscope. However, the accuracy of this method is limited due to defective marking and wild flies contaminated with a few dye particles in the monitoring traps.
View Article and Find Full Text PDFPredicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.
Environ Sci Technol
January 2025
Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China.
Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO, PM, and PM, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m).
View Article and Find Full Text PDFUrgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With Echocardiograms.
JMIR AI
January 2025
Department of Information Systems and Business Analytics, Iowa State University, Ames, IA, United States.
Background: In the contemporary realm of health care, laboratory tests stand as cornerstone components, driving the advancement of precision medicine. These tests offer intricate insights into a variety of medical conditions, thereby facilitating diagnosis, prognosis, and treatments. However, the accessibility of certain tests is hindered by factors such as high costs, a shortage of specialized personnel, or geographic disparities, posing obstacles to achieving equitable health care.
View Article and Find Full Text PDFMining versatile feruloyl esterases: phylogenetic classification, structural features, and deep learning model.
Bioresour Bioprocess
January 2025
Key Laboratory of Pollution Exposure and Health Intervention of Zhejiang Province, College of Biological and Environment Engineering, Zhejiang Shuren University, Hangzhou, 310015, China.
Feruloyl esterases (FEs, EC 3.1.1.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!