Integrated in-Vitro/in-Silico Wildfire Forecasting and Health Risk Assessment

Overview

Climate change has significantly increased global wildfire frequency and impact, making it a critical humanitarian crisis, particularly in dry climates. To address this challenge, we propose PyroShield—a novel, multi-stage framework for early wildfire detection, toxicological assessment, early-stage lung cancer detection, and rapid drug development. Our machine-learning wildfire detection model, trained on diverse environmental data, achieves 98% accuracy while incorporating dropout layers to prevent overfitting - which ensures that accuracy is translatable for real-world applications. Toxicological analysis of wildfire particles on A549 alveolar lung cells in vitro revealed cytotoxic effects from prominent wildfire-derived particulate matter, particularly phenanthrene and anthracene, highlighting long-term respiratory health risks. However, predicting wildfires and identifying health hazards alone are insufficient; some individuals may still be unable to evacuate in time. To address this, we developed PyroScan, a deep learning model utilizing convolutional neural networks and transfer learning to detect early-stage lung cancer from histopathological scans with 95% accuracy, aiding victims of wildfire-induced respiratory complications. Furthermore, leveraging in silico lead optimization through molecular docking simulations, we designed novel C1632-based inhibitors targeting the oncogenic Lin28B protein and the EGFR pathway, followed by in vitro validation via Differential Scanning Fluorimetry (DSF) for multi-target polypharmacology. Our integrated framework ensures timely intervention at every critical wildfire stage, ultimately saving lives.

Integrated in-Vitro/in-Silico Wildfire Forecasting and Health Risk Assessment

Overview

Competition history

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