联合肠道特征菌与机器学习的裸鼠死亡时间分段推断模型构建OACHSSCD

Objective 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.Methods A total of 108 nude mice were sacri-ficed,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.Results 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 re-gression 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.Conclusion 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.