Environmental Health

Artificial intelligence performance in image-based biliary atresia identification: a systematic review and meta-analysis.

BACKGROUND: Biliary atresia (BA) is a rare condition that can lead to serious health complications. Artificial Intelligence (AI)- based medical imaging has shown potential to improve the detection of BA diagnosis, offering increased accuracy over traditional imaging methods. This meta-analysis aimed to evaluate the diagnostic accuracy of imaging-based AI for detecting BA. METHODS: The study follow...

Atmospheric pollution is associated with microbial community stability and functional diversity in river ecosystems.

Air pollution, which poses a global threat to human and ecosystem health, has been extensively monitored by governments and researchers worldwide. However, the effects of air pollution on river ecosystems, particularly riverine microorganisms, have received limited attention. Here, we present the first national-scale investigation (105 rivers across China) to examine how spatial variation in atmos...

Unraveling the molecular mechanisms of air pollution-induced lung adenocarcinoma through machine learning, network toxicology, and molecular docking.

OBJECTIVE: Airborne environmental contaminants are established carcinogens. This investigation elucidates the mechanistic contributions to pulmonary a...

Smart three-in-one detection platform: Chemical sensing, image recognition, and machine learning for rapid identification of tetracycline antibiotics.

The hidden allergic reactions and cumulative toxic effects caused by antibiotic residues in aquatic products have become significant public health con...

TNFAIP3, PADI4, and CXCR4 as biomarkers of neutrophil extracellular traps in severe asthma.

Asthma is a heterogeneous condition impacting an estimated 300 million individuals globally. Although inhaled corticosteroids are effective in allevia...

Rapid characterization of heavy metals in soil using a novel integrated strategy for near-infrared spectroscopy models.

As one of the critical natural resources sustaining human survival and sustainable development, soil quality directly underpins ecological security an...

Pectin from Phyllanthus emblica L.: Structure elucidation and synergistic in vitro anti-stomach adenocarcinoma effects with 5-fluorouracil.

In recent years, polysaccharides with potential anticancer activity have attracted widespread attention. In this study, a homogeneous polysaccharide f...

Spatiotemporal prediction of aeropollen concentration using tree-based machine learning.

Pollen is a major aeroallergen and an important environmental health concern, with its concentrations strongly modulated by climate, air pollution, an...

River plastic hotspot detection from space.

Plastic pollution threatens terrestrial and aquatic ecosystems, and rivers play a central role in transporting and retaining plastics across landscape...

Micro- and nanoplastics (MNPs) in liquid food: From occurrence, health risks and migration mechanisms to AI-enabled analytical and circular solutions.

Microplastics (MPs, 1 μm-5 mm) and nanoplastics (NPs, <1 μm) are routinely detected in wide array of liquid food, including drinking water, milk, beve...

Predicting Nanotoxicity across Materials Using Interpretable Machine Learning and Descriptors Based on the Periodic Table.

The biological effects of engineered nanomaterials are largely determined by their intrinsic physicochemical properties; however, predicting these eff...

Integrating landscape "source-transport-sink" mechanisms into GeoAI to enhance surrogate modeling of watershed nutrient loads.

Accurate prediction and effective management of watershed nitrogen (N) and phosphorus (P) loads remain critical challenges in controlling non-point so...

Enhanced prediction of total organic carbon in a large complex watershed (the Han River, South Korea) by integrating machine learning with real-time in-situ water quality parameters and fluorescence intensities.

Effective water quality monitoring is critical for protecting aquatic ecosystems and supporting regulatory management. Total organic carbon (TOC), a k...

Automatic and precise segmentation of gas chromatographic peaks by temporal semantic segmentation for prolonged monitoring of volatile organic compounds.

Long-term continuous monitoring of volatile organic compounds (VOCs) is essential for atmospheric pollution control, environmental health assessment, ...

Hybrid TCN-BiGRU model for accurate air quality index prediction.

Accurate Air Quality Index (AQI) forecasting is critical for pollution control and public health protection, yet traditional methods struggle to captu...

The scope of cardiology research: More than 300 topics covered through bibliometric evaluation.

This study provides a bibliometric overview of cardiovascular-related bibliometric research identified in the Scopus database using a title-abstract-k...

Quantitative analysis of 3-monochloropropane-1,2-diol in fried oil using convolutional neural networks optimizing with a hybrid feature selection based on Fourier transform infrared spectroscopy.

To address challenges in quantifying 3-monochloropropane-1,2-diol (3-MCPD), a toxic compound formed during oil refining with nephrotoxic, reproductive...

Decoding the urban-suburban ozone disparities in Shandong, China: A machine learning-driven attribution analysis (2019-2023).

Urban-suburban disparities in ozone (O3) pollution have received growing attention due to their implications for environmental equity and public healt...

New spectral indices for identifying large plastic accumulations in coastal waters with sentinel-2 imagery.

Plastic pollution in aquatic environments has become a growing global concern, with floating macroplastics posing serious ecological, economic, and he...

Global mapping of pharmaceutical ecological risk in rivers using machine learning: drivers, hotspots, and compounded water stress.

Pharmaceuticals serve as a double-edged sword: while essential for health care, they are also ubiquitous pollutants that threaten ecosystem. The vast ...