[Construction of a Segmented PMI Estimation Model Integrating Intestinal Microbial Signatures and Machine Learning in...

To observe stage-specific changes in the intestinal microbiota of nude mice after death and to develop a postmortem interval (PMI) estimation model based on "rupture points", thereby exploring a new model for PMI estimation.

A total of 108 nude mice were sacrificed, and cecal contents were collected at 18 time points (0, 24, 41, 48, 55, 65, 72, 79, 89, 96, 103, 113, 120, 144, 168, 192, 216, and 240 h postmortem). 16S rRNA gene amplicon sequencing was used to analyze the changes in intestinal microbiota. Based on microbial abundance, a random forest model was employed for cross-validation to identify signature bacterial genera. A segmented regression model was then constructed to estimate PMI and compared with a direct regression model.

Both α- diversity and β-diversity analyses indicated significant changes in the relative abundance of intestinal microbiota during the periods of 0-103 h and 113-240 h postmortem in nude mice. The segmented regression model built using the random forest algorithm achieved an R² of 0.96 and a mean absolute error (MAE) of 9.83 h for PMI estimation. In contrast, the direct regression model yielded an R² of 0.81 and an MAE of 16.91 h.

Microbial succession during cadaver decomposition exhibits clear temporal and stage-specific characteristics. A segmented regression model for PMI estimation using "rupture points" can improve the accuracy of PMI estimation in nude mice.