AI Medical Compendium Journal:
Journal of hazardous materials

Showing 61 to 70 of 97 articles

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.

Journal of hazardous materials
Developing mechanistic non-animal testing methods based on the adverse outcome pathway (AOP) framework must incorporate molecular and cellular key events associated with target toxicity. Using data from an in vitro assay and chemical structures, we a...

Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay.

Journal of hazardous materials
Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complex...

Integrating transformer-based machine learning with SERS technology for the analysis of hazardous pesticides in spinach.

Journal of hazardous materials
This study introduces an innovative strategy for the rapid and accurate identification of pesticide residues in agricultural products by combining surface-enhanced Raman spectroscopy (SERS) with a state-of-the-art transformer model, termed SERSFormer...

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms.

Journal of hazardous materials
Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, emphysema, and chronic bronchitis. Despite recent attention from researchers, the link between exhaled breath and ozone-induced injury remains poorly un...

Biodegradation of ciprofloxacin using machine learning tools: Kinetics and modelling.

Journal of hazardous materials
Recently, the rampant administration of antibiotics and their synthetic organic constitutes have exacerbated adverse effects on ecosystems, affecting the health of animals, plants, and humans by promoting the emergence of extreme multidrug-resistant ...

A refined deep-learning-based algorithm for harmful-algal-bloom remote-sensing recognition using Noctiluca scintillans algal bloom as an example.

Journal of hazardous materials
Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep-learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of...

Deciphering exogenous chemical carcinogenicity through interpretable deep learning: A novel approach for evaluating atmospheric pollutant hazards.

Journal of hazardous materials
Cancer remains a significant global health concern, with millions of deaths attributed to it annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the growing prevalence of this disease. The carcinogenic assessme...

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals.

Journal of hazardous materials
Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing res...

Screening the phytotoxicity of micro/nanoplastics through non-targeted metallomics with synchrotron radiation X-ray fluorescence and deep learning: Taking micro/nano polyethylene terephthalate as an example.

Journal of hazardous materials
Microplastics (MPs) and nanoplastics (NPs) are global pollutants with emerging concerns. Methods to predict and screen their toxicity are crucial. Elemental dyshomeostasis can be used to assess toxicity of environmental pollutants. Non-targeted metal...

Transformer-based deep learning models for adsorption capacity prediction of heavy metal ions toward biochar-based adsorbents.

Journal of hazardous materials
Biochar adsorbents synthesized from food and agricultural wastes are commonly applied to eliminate heavy metal (HM) ions from wastewater. However, biochar's diverse characteristics and varied experimental conditions make the accurate estimation of th...