AIMC Topic: Environmental Pollutants

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Machine learning predicts the serum PFOA and PFOS levels in pregnant women: Enhancement of fatty acid status on model performance.

Environment international
Human exposure to per- and polyfluoroalkyl substances (PFASs) has received considerable attention, particularly in pregnant women because of their dramatic changes in physiological status and dietary patterns. Predicting internal PFAS exposure in pre...

ARKA: a framework of dimensionality reduction for machine-learning classification modeling, risk assessment, and data gap-filling of sparse environmental toxicity data.

Environmental science. Processes & impacts
Due to the lack of experimental toxicity data for environmental chemicals, there arises a need to fill data gaps by approaches. One of the most commonly used approaches for toxicity assessment of small datasets is the Quantitative Structure-Activit...

Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.

The American journal of clinical nutrition
BACKGROUND: Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of c...

Identification of biological indicators for human exposure toxicology in smart cities based on public health data and deep learning.

Frontiers in public health
With the acceleration of urbanization, the risk of urban population exposure to environmental pollutants is increasing. Protecting public health is the top priority in the construction of smart cities. The purpose of this study is to propose a method...

Construction of an antidepressant priority list based on functional, environmental, and health risks using an interpretable mixup-transformer deep learning model.

Journal of hazardous materials
As emerging pollutants, antidepressants (AD) must be urgently investigated for risk identification and assessment. This study constructed a comprehensive-effect risk-priority screening system (ADRank) for ADs by characterizing AD functionality, occur...

Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods.

Environmental science & technology
Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on related immunological mechanisms, which play an important role in the occurrence and development of canc...

Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning.

Environmental pollution (Barking, Essex : 1987)
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluat...

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...

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...

Ocean Stratification Impacts on Dissolved Polycyclic Aromatic Hydrocarbons (PAHs): From Global Observation to Deep Learning.

Environmental science & technology
Ocean stratification plays a crucial role in many biogeochemical processes of dissolved matter, but our understanding of its impact on widespread organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), remains limited. By analyzing disso...