Following skeletal muscle injury (SMI), from post-injury reaction to repair consists of a complex series of dynamic changes. However, there is a paucity of research on detailed transcriptional dynamics and time-dependent marker gene expression in the early stages after SMI. In this study, skeletal muscle tissue in rats was taken at 4 to 48 h after injury for next-generation sequencing. We examined the transcriptional kinetics characteristics during above time periods after injury. STEM and maSigPro were used to screen time-correlated genes. Integrating 188 time-correlated genes with 161 genes in each time-related gene module by WGCNA, we finally identified 18 network-node regulatory genes after SMI. Histological staining analyses confirmed the mechanisms underlying changes in the tissue damage to repair process. Our research linked a variety of dynamic biological processes with specific time periods and provided insight into the characteristics of transcriptional dynamics, as well as screened time-related biological indicators with biological significance in the early stages after SMI.

2020): Analysis of sensitivity and specificity: precise recognition of neutrophils during regeneration of contused skeletal muscle in rats, Forensic Sciences Research, ABSTRACT In this report, we applied the TissueFAXS 200 digital pathological analysis system to rapidly and accurately identify neutrophils during regeneration of contused skeletal muscle, and to provide information for follow-up studies on neutrophils to estimate wound age. Rat injury model was established, and skeletal muscle samples were obtained from the control group and contusion groups at 1, 1.5, 2, 3, 4, and 6 h, as well as at 1, 3, 5, and 15 d post-injury (n ¼ 5 per group). The expression of nuclei and neutrophils was detected by hematoxylin and eosin (HE) staining and immunohistochemical (IHC) staining. A total of 20 injury site areas of 0.25 mm 2 (0.5 mm  0.5 mm) were then randomly selected at all time points. A TissueFAXS 200 digital pathological analysis system was used to identify the positive and negative numbers. Knowledge of five professional medical workers were considered the gold standard to measure the false positive rate (FPR), false negative rate (FNR), sensitivity, specificity, and area under the curve (AUC) of receiver operating characteristic (ROC) curves. As a result, with a staining area of neutrophils from 8 mm 2 to 15 mm 2 , the FPR was 4.28%-12.14%, the FNR was 12.42%-64.08%, the sensitivity was 35.92%-87.58%, the specificity was 87.86%-95.72%, the Youden index was 0.316-0.754, accuracy was 82.80%-88.30%, and the AUC was 0.771-0.826. The AUC was largest when the cut-off value of the staining area was 12 mm 2 . Our results show that this software-based method is more accurate than the human eye in evaluating neutrophil infiltration. Based on the sensitivity and specificity, neutrophils can be accurately identified during regeneration of contused skeletal muscle. The TissueFAXS 200 digital pathological analysis system can also be used to optimize conditions for different cell types under various injury conditions to determine the optimal cut-off value of the staining area and provide optimal conditions for further study. Furthermore, it will provide evidence for forensic pathology cases. ARTICLE HISTORY

Background: Acute myocardial ischemia (AMI) remains the leading cause of death worldwide. In particular, when death occurs within a short time, it is hard to nd post-mortem speci c structural anomalies of the heart at autopsy with standard methods. Therefore, the post-mortem diagnosis of AMI represents a current challenge for both clinical and forensic pathologists. Metabolomics technology plays an important role in searching for new diagnostic biomarkers. Here, we characterize metabolic pro les of AMI and attempted to interpret the role of metabolic changes in sudden cardiac death (SCD).Methods: The untargeted metabolomics was applied to analyze serum metabolic signatures from AMI experimental group (ligation of left coronary artery at 5mm below the left atrial appendage in rats), along with the control and sham groups (n = 10 per group). The analytical strategy based on ultra performance liquid chromatography combined with high-resolution mass spectrometry. The resulting data was preprocessed to discriminant metabolites, and a set of machine learning algorithms were used to construct predictable models. Seventeen blood samples from autopsy cases were applied to validate the classi cation model's value in human samples.Results: A total of 28 endogenous metabolites in serum were signi cantly altered in AMI group relative to control and sham groups. Gradient tree boosting, support vector machines, random forests, logistic regression, and multilayer perceptron models were used to further screen the more valuable metabolites from 28 metabolites to optimize the biomarker panel. The results showed that classi cation accuracy and performance of multilayer perceptron (MLP) models were better than other algorithms when the metabolites consisting of L-threonic acid, N-acetyl-L-cysteine, CMPF, glycocholic acid, L-tyrosine, cholic acid, and glycoursodeoxycholic acid. In autopsy cases, the MLP model constructed based on rat dataset achieved an accuracy of 88.23, and ROC of 0.89 for predicting AMI-SCD.Conclusions: A panel of 7 molecular biomarkers was identi ed by assessment the accuracy and e cacy of different metabolite combinations in inferring AMI using machine learning algorithms. The constructed MLP model has a high diagnostic performance for both AMI rats and autopsies-based blood samples. Thus, the combination of metabolomics and machine learning algorithms provides a novel strategy for AMI diagnosis.

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