#postmorteminterval

Metagenomic Profiling of Lung Microbes in Aquatic Forensics #worldresearchawards#researchawards

Metagenomic sequencing revealed that multi-kingdom lung microbes—particularly bacteria and eukaryotes—serve as reliable forensic markers in aquatic death investigations. Seventeen bacterial and nine eukaryotic species were identified as potential biomarkers for drowning diagnosis, with Aeromonas hydrophila showing strong diagnostic significance. Robust random forest models achieved high accuracy for drowning identification (89.29%), drowning site inference (100%), and postmortem submersion interval (PMSI) estimation (MAE 0.66 days). These findings enhance precision in forensic investigations of aquatic corpses.

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Forensic AI Explained: Deep Learning for PMI Using Sarcophaga peregrina #researchawards

This video explores a cutting-edge deep learning method based on image recognition for accurately estimating intra-puparial age and postmortem interval (PMI) in the forensically important flesh fly Sarcophaga peregrina (Diptera: Sarcophagidae).

By leveraging advanced convolutional neural networks (CNNs) and AI-powered image analysis, researchers can now determine puparial developmental stages with improved precision—offering significant value for forensic investigations, death-time estimation, and forensic entomology casework.

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Digital Forensic Estimation of Time Since Death #researchawards #sciencefather #fosawards

This study emphasizes the shift from traditional postmortem interval (PMI) estimation techniques to advanced digital approaches. It highlights the need for a multi-parameter forensic software system that integrates physiological, biochemical, and environmental data to improve the accuracy and reliability of time since death estimation. By combining artificial intelligence, data analytics, and forensic science, the proposed digital framework aims to modernize medico-legal investigations and enhance evidence-based death analysis.

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