AIMC Topic: Pesticides
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Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.
PloS one
Jun 18, 2025
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision bee...
A predictive framework using advanced machine learning approaches for measuring and analyzing the impact of synthetic agrochemicals on human health.
Scientific reports
May 3, 2025
Pesticides and other synthetic agrochemicals play a critical role in emerging agricultural practices by enhancing crop productivity and protecting against pests and diseases. However, their widespread application has raised significant concerns about...
Machine learning model for age related macular degeneration based on pesticides: the National Health and Nutrition Examination Survey 2007-2008.
Frontiers in public health
Apr 16, 2025
Age-related macular degeneration (AMD) is the most common cause of irreversible deterioration of vision in older adults. Previous studies have found that exposure to pesticides can lead to a worsening of AMD. In this paper, information on pesticide e...
Leveraging machine learning in precision medicine to unveil organochlorine pesticides as predictive biomarkers for thyroid dysfunction.
Scientific reports
Apr 11, 2025
Exposure to organochlorine pesticides (OCPs) poses significant health risks, including cancer, endocrine dysregulation, neurological disorders, and reproductive disruption. This study investigates the association between OCP exposure and thyroid dist...
Unraveling the complexity of organophosphorus pesticides: Ecological risks, biochemical pathways and the promise of machine learning.
The Science of the total environment
Mar 27, 2025
Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in...
Advancing non-target analysis of emerging environmental contaminants with machine learning: Current status and future implications.
Environment international
Mar 22, 2025
Emerging environmental contaminants (EECs) such as pharmaceuticals, pesticides, and industrial chemicals pose significant challenges for detection and identification due to their structural diversity and lack of analytical standards. Traditional targ...
Unlocking the potential of Eudrilus eugeniae in mitigating the pollution risk of pesticides and heavy metals: Fostering machine learning tactics to optimize environmental health.
The Science of the total environment
Mar 8, 2025
Agro-industrial waste management remains a critical challenge in sustainable development, particularly due to contamination with heterogeneous micropollutants such as heavy metals (HMs), pesticides, and polyphenols. This study explores an innovative ...
Comprehensive Raman Fingerprinting and Machine Learning-Based Classification of 14 Pesticides Using a 785 nm Custom Raman Instrument.
Biosensors
Mar 5, 2025
Raman spectroscopy enables fast, label-free, qualitative, and quantitative observation of the physical and chemical properties of various substances. Here, we present a 785 nm custom-built Raman spectroscopy instrument designed for sensing applicatio...
Interpretable machine learning unveils key predictors and default values in an expanded database of human in vitro dermal absorption studies with pesticides.
Regulatory toxicology and pharmacology : RTP
Mar 4, 2025
The skin is the main route of exposure to plant protection products for operators, workers, residents, and bystanders. Assessing dermal absorption is key for evaluating pesticide exposure. The initial approach to risk assessment involves using defaul...
PE-GCL: Advancing pesticide ecotoxicity prediction with graph contrastive learning.
Journal of hazardous materials
Jan 16, 2025
Ecotoxicity assessments, which rely on animal testing, face serious challenges, including high costs and ethical concerns. Computational toxicology presents a promising alternative; nevertheless, existing predictive models encounter difficulties such...
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