AI-driven cardiovascular toxicity assessment of emerging contaminants in water: from deep learning phenotyping to LLM-orchestrated risk evaluation.
- DOI
- 10.1016/j.watres.2026.126790
- Published
- 2026 Aug 24
- Container
- Water research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.126790,
title = {AI-driven cardiovascular toxicity assessment of emerging contaminants in water: from deep learning phenotyping to LLM-orchestrated risk evaluation.},
author = {Zhang L and Zhu Z and Chen L and Zhong Y and Jin LN and Su Q and Ng HY},
year = {2026},
journal = {Water research},
doi = {10.1016/j.watres.2026.126790},
url = {https://doi.org/10.1016/j.watres.2026.126790}
}RIS
TY - JOUR TI - AI-driven cardiovascular toxicity assessment of emerging contaminants in water: from deep learning phenotyping to LLM-orchestrated risk evaluation. AU - Zhang L AU - Zhu Z AU - Chen L AU - Zhong Y AU - Jin LN AU - Su Q AU - Ng HY PY - 2026 JO - Water research DO - 10.1016/j.watres.2026.126790 UR - https://doi.org/10.1016/j.watres.2026.126790 ER -
APA
L, Z., Z, Z., L, C., Y, Z., LN, J., Q, S., & HY, N. (2026). AI-driven cardiovascular toxicity assessment of emerging contaminants in water: from deep learning phenotyping to LLM-orchestrated risk evaluation.. Water research. https://doi.org/10.1016/j.watres.2026.126790
Source records
- pubmed · retrieved 2026-09-27T01:14:18.714Z