From Microplastic Pollution to Disease Emergence: AI-Driven Freshwater Snail Sentinels for Predicting Parasite Risks in a Changing Climate

The Plastisphere Meets Parasitology: Predicting Disease Emergence in Microplastic-Polluted Waters Using Freshwater Snail Sentinels

AI-Integrated Biosensing Using Freshwater Snails to Assess Microplastic Impacts on Aquatic Biodiversity in India

Apple Snails as Bioindicators of Microplastic Pollution in Singapore's Freshwaters

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Researchers used apple snails as bioindicators to measure microplastic pollution in Singapore's freshwater bodies for the first time, suggesting these snails could serve as a practical and cost-effective tool for tracking plastic contamination in urban water systems.

MicroDetect-Net (MDN): Leveraging Deep Learning to Detect Microplastics in Clam Blood, a Step Towards Human Blood Analysis

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Researchers developed MicroDetect-Net, a deep learning model trained to detect microplastics in clam blood, demonstrating a pathway toward automated detection in human blood samples as plastic production exceeds 368 million tons annually and microplastics are found across all environmental and biological matrices.

Predicting microplastic impacts on microalgae: A machine learning approach to understand dynamic interactions in aquatic ecosystems

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Scientists used artificial intelligence to figure out how microplastic pollution harms microalgae, tiny organisms that form the base of the food chain in oceans and lakes and produce much of the oxygen we breathe. They found that temperature and the plastic's chemical properties (like particle size and charge) play the biggest role in how much damage occurs, with water temperature mattering most early on and plastic characteristics mattering more over time. This matters because microalgae support entire aquatic food webs (including the fish we eat), so understanding what makes microplastics most harmful can help guide better pollution control to protect both ecosystems and our food sup