Environmental Health

G.AI.A: An Integrated Machine-Learning Platform for Predicting Bioaccumulation and Ecotoxicity of Pharmaceuticals.

Pharmaceutical pollution in aquatic environments poses a significant ecological threat due to the accumulation of bioactive compounds from human and veterinary sources. In support of the EU Green Deal's Chemicals Strategy for Sustainability, this study presents a computational framework for predicting two key environmental risk indicators in fish: bioconcentration and ecotoxicity. Bioconcentration...

A Deep Learning Model for Dynamic Prediction of Acute Kidney Injury in Heart Failure Patientss.

BACKGROUND: This study aimed to develop and validate a dynamic prediction model for acute kidney injury (AKI) in heart failure (HF) patients. METHODS: Using data from 7,636 HF patients in the MIMIC-IV v3.1 database, we constructed a Long Short-Term Memory (LSTM) model with dynamic focal loss to handle class imbalance. The study designed two prediction perspectives: short-term prediction utilized d...

A Machine Learning-Based Dynamic SST Index for Long-Lead Malaria Prediction in the Peruvian Amazon.

Malaria imposes a major health burden in the Peruvian Amazon, and its early warning is essential for effective disease prevention. The tropical sea su...

Comment on "Early warning of harmful cyanobacteria blooms based on high frequency in situ monitoring and intelligible machine learning modelling: The case study of Lake Müggelsee (Germany)" by Recknagel et al. (Water Research 287 2025 124,514).

Recknagel et al. (2025) present a timely study leveraging high-frequency in-situ data and three fundamentally different machine learning algorithms to...

Identification of molecular compounds targeting bacterial propionate metabolism with topological machine learning.

This study demonstrates the transformative potential of machine learning in drug discovery by integrating comparative protein and ligand analysis with...

Flux dynamics and scenario simulation of PFAS under extreme hydrological perturbations in urban tributary flowing into the Yangtze River: A focus on floods.

Floods considerably amplify pollutant transfer into the Yangtze River (YZR) via tributaries. We explored flood-induced changes in PFAS pollution patte...

Machine learning frameworks for predicting pulmonary cell toxicities induced by metal ions in the atmosphere.

Metals in PM2.5 are closely associated with cardiopulmonary disease endpoints, potentially attributable to ionic species-induced oxidative stress effe...

Unveiling the fate of heavy metals along the soil-rice-human pathway: Source-sink quantification, rhizospheric processes, and health implications.

Rapid industrial and agricultural intensification is increasing multi-source heavy metals (HMs) inputs into farmland, escalating risks to soil-crop sy...

Medicinal chemistry meets nanotechnology: machine learning assisted colorimetric sensing platform for oxalic acid based on drug mediated copper oxide nanoparticles.

Increased levels of oxalic acid are associated with an increased risk of kidney stone formation, which can lead to renal failure. In addition, its hig...

DCDGNet: Dual-frequency cross-feature diffusion GAN for single fusion sonar image generation in exposed subsea pipeline inspection.

The exposed subsea pipelines are prone to damage and rupture, increasing the risk of marine pollution; therefore, their safety inspection is imperativ...

Stem Cell-Derived Organoids as a Next-Generation Platform for Drug Toxicity and Efficacy Testing: Toward Replacing Animal Models in Regulatory Science.

Three-dimensional, self-organizing structures derived from stem cells, known as organoids, represent a groundbreaking advancement in preclinical drug ...

Molreac-Oxi: An end-to-end deep learning-quantum chemistry platform for •OH reactivity (kOH), pathways, and active-site insight.

To address the long-standing challenge of efficiently evaluating reaction rate constants (kOH) for pollutant-hydroxyl radical (•OH) systems in environ...

Source tracking, pollution load, and risk assessment of microplastics pollution in agricultural soils of Bangladesh using machine learning and multi-matrix approaches.

Microplastics (MPs) contamination in agricultural soils has emerged as a critical environmental challenge, particularly in Bangladesh, where agricultu...

AI-optimized sanguinarine derivatives inhibiting sortase A for combating AmpC β-lactamase resistance in Enterobacter cloacae: An integrated computational approach.

The antimicrobial resistance is a serious health problem worldwide, and one of the causes of multidrug resistance is AmpC β-lactamase-producing Entero...

SiQDs and [Ru(bpy)2(phen-NH2)]2+ based ratiometric fluorescence probe for point-of-care testing of 6PPD-quinone with 3D-printing portable devices.

BACKGROUND: N-phenyl-N'-(1,3-dimethylbutyl)-p-phenylenediamine-quinone (6PPD-Q), an emerging pollutant, is a highly toxic chemical derived from tires,...

Comprehensive and spatially detailed passenger vehicle and truck traffic volume data for the United States estimated by machine learning.

The Highway Performance Monitoring System, managed by the Federal Highway Administration, provides data on average annual daily traffic volume across ...

Mechanistic insights into sulfamethoxazole removal in activated sludge systems through machine learning and microcosm experiments.

Antibiotic residues in wastewater pose serious ecological and public health risks and may even induce the more severe threat of antimicrobial resistan...

Divergent Ozone Predictions in China Under Carbon Neutrality: Why Chemical Mechanisms Disagree.

Uncertainty in air quality models can lead to divergent assessments of emission control policies. Here, we investigate why two widely used chemical me...

Aucklandia lappa, Vladimiria souliei, and Inula helenium: A comprehensive review on the ethnomedicines, phytochemicals, quality control and pharmacology of three confusable Muxiang varieties (2015-2025).

ETHNOPHARMACOLOGICAL RELEVANCE: The dried roots of Aucklandia lappa (Mu-Xiang), Vladimiria souliei (Chuan-Mu-Xiang), and Inula helenium (Tu-Mu-Xiang),...

Precision mapping and driving factors of heavy metal(loid)s in agricultural soils of the Yellow River: An integrated machine learning and Geodetector approach.

Heavy metal (loid) (HM) contamination of agricultural soils threatens global food security and sustainable development. Despite the Yellow River Basin...