In this episode, we explore a high-tech twist on developmental toxicology. Researchers have combined microfluidic engineering with machine learning to automate the analysis of thousands of C. elegans for chemical toxicity testing — no anaesthetics or low-res imaging required.

Using the vivoChip device and a custom ML model called vivoBodySeg, the team:

Captures 3D images of ~1000 worms from 24 populations at once Achieves near-human segmentation accuracy (Dice score: 97.8%) Measures subtle toxicity effects like changes in body size and gut autofluorescence Identifies EC10 and LOAEL values with high precision Uses few-shot learning to adapt the model to new worm shapes and sizes

This platform slashes analysis time by 140× and sets a new benchmark for high-throughput New Approach Methodologies (NAMs) in toxicology.

📖 Based on the research article: “Machine learning-based analysis of microfluidic device immobilised C. elegans for automated developmental toxicity testing” Andrew DuPlissis, Abhishri Medewar, Evan Hegarty, et al. Published in Scientific Reports (2025) 🔗 https://doi.org/10.1038/s41598-024-84842-x

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This podcast is generated with artificial intelligence and curated by Veeren. If you’d like your publication featured on the show, please get in touch.

📩 More info: 🔗 ⁠⁠www.veerenchauhan.com⁠⁠ 📧 [email protected]