{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T21:15:10Z","timestamp":1790111710272,"version":"4.0.1"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T00:00:00Z","timestamp":1767830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Provincial Natural Science Foundation Project","award":["2022A1515012090"],"award-info":[{"award-number":["2022A1515012090"]}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Accurate estimation of post-mortem submersion interval (PMSI) is a critical challenge in forensic science. The current research has largely focused on the microbial communities in the skin and gut, which are susceptible to environmental contamination, while the potential of internal tissues remains underexplored. This study aimed to investigate whether the microbiome of skeletal muscle, a relatively closed ecosystem, undergoes a predictable succession following submersion in water PMSI and to evaluate its potential for building a high-precision PMSI prediction model, which is independent of the cause of death (drowning vs. post-mortem submersion).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Using 72 male Sprague\u2013Dawley rats, we established drowning (D group) and post-mortem submersion (PS group) models. After submersion in natural aquatic environment for 14\u202fdays, skeletal muscle samples were collected at six time. The microbial communities were profiled by high-throughput sequencing of the V3\u2013V4 region of the 16S rRNA gene, followed by analyses of alpha and beta diversity. Based on the observed successional patterns, a two-stage prediction model combining classification and regression algorithms (e.g., random forest, RF) was developed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The skeletal muscle microbiome exhibited a significant and predictable successional pattern, clearly partitioning into an early-phase (0\u20133\u202fdays) and a late-phase (5\u201314\u202fdays) (PERMANOVA,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). This succession was characterized by a shift from the dominant community of Proteobacteria to the dominant community of Firmicutes. Importantly, the cause of death did not significantly impact either the alpha or beta diversity of the microbial communities (PERMANOVA,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.251). The resulting two-stage prediction model demonstrated excellent performance: the classification model distinguished the early and late phases with an accuracy of 90.9% (AUC\u202f=\u202f0.9504), and the mean absolute errors (MAE) of regression models was 0.303\u202fdays in the early phase and 1.293\u202fdays in the late phase.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The rat skeletal muscle microbiome undergoes a regular and predictable post-mortem succession unrelated to the cause of death. The stable \u201cmicrobial clock\u201d within the internal tissue allows the construction of a high-precision two-stage machine learning model for PMSI estimation. Our results establish skeletal muscle as a highly promising new target for forensic microbiology, offering a robust theoretical basis and technical approach to resolving challenges in long-term PMSI estimation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2025.1685097","type":"journal-article","created":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T06:35:59Z","timestamp":1767854159000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Postmortem submersion interval prediction model based on the rat muscle microbiome"],"prefix":"10.3389","volume":"16","author":[{"given":"Cheng-Dong","family":"Ma","sequence":"first","affiliation":[{"name":"Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University","place":["Guangzhou, China"]},{"name":"Guangdong Public Security Judicial Appraisal Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Biao","family":"Liao","sequence":"additional","affiliation":[{"name":"Guangdong Public Security Judicial Appraisal Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Cheng","family":"Yue","sequence":"additional","affiliation":[{"name":"Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University","place":["Guangzhou, China"]},{"name":"Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Gao","sequence":"additional","affiliation":[{"name":"Guangzhou Nansha District Material Evidence Identification Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Quan","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University","place":["Guangzhou, China"]},{"name":"Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Er-Wen","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University","place":["Guangzhou, China"]},{"name":"Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University","place":["Guangzhou, China"]},{"name":"Guangdong Province Translational Forensic Medicine Engineering Technology Research Center, Sun Yat-sen University","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,8]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"112072","DOI":"10.1016\/j.forsciint.2024.112072","article-title":"Aquatic conditions & bacterial communities as drivers of the decomposition of submerged remains","volume":"361","author":"Bone","year":"2024","journal-title":"Forensic Sci. 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