Application Prospects and Prospect of Metabolomics in the Identification of Sudden Cardiac Death / 法医学杂志

In cases of sudden death, the prevention of sudden cardiac death and the analysis of the cause of death after sudden cardiac death have always been a difficult problem. Therefore, clinical research and forensic pathological identification of sudden cardiac death are of great significance. In recent years, metabolomics has gradually developed into a popular field of life science research. The detection of "metabolic fingerprints" of biological fluids can provide an important basis for early diagnosis of diseases and the discovery of potential biomarkers. This article reviews the current research status of sudden cardiac death and the research on metabolomics of cardiovascular diseases that is closely related to sudden cardiac death and analyzes the application prospects of metabolomics in the identification of the cause of sudden cardiac death.

Estimating Postmortem Interval Using Intestinal Microbiota Diversity Based on 16S rRNA High-throughput Sequencing Technology / 法医学杂志

OBJECTIVES@#To explore the correlation between intestinal microbiota and postmortem interval(PMI) in rats by using 16S rRNA high-throughput sequencing technology.@*METHODS@#Rats were killed by anesthesia and placed at 16 ℃, and DNA was extracted in caecum at 14 time points of 0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27 and 30 d after death. The 16S rRNA high-throughput sequencing technology was used to detect intestinal microbiota in rat cecal contents, and the results were used to analyze the rat intestinal microbiota diversity and differences.@*RESULTS@#The total number of intestinal microbial communities did not change significantly within 30 days after death, but the diversity showed an upward trend. A total of 119 bacterial communities were significantly changed at 13 time points after death. The models for PMI estimation were established by using partial least squares (PLS) regression at all time points, before 9 days and after 12 days, reaching an R2 of 0.795, 0.767 and 0.445, respectively; and the root mean square errors (RMSEs) were 6.57, 1.96 and 5.37 d, respectively.@*CONCLUSIONS@#Using 16S rRNA high-throughput sequencing technology, the composition and structure of intestinal microbiota changed significantly within 30 d after death. In addition, the established PLS regression model suggested that the PMI was highly correlated with intestinal microbiota composition, showing a certain time series change.