Pathological Characteristics and Classification of Unstable Coronary Atherosclerotic Plaques.

Important forensic diagnostic indicators of sudden death in coronary atherosclerotic heart disease, such as acute or chronic myocardial ischemic changes, sometimes make it difficult to locate the ischemic site due to the short death process, the lack of tissue reaction time. In some cases, the deceased died of sudden death on the first-episode, resulting in difficulty for medical examiners to make an accurate diagnosis. However, clinical studies on coronary instability plaque revealed the key role of coronary spasm and thrombosis caused by their lesions in sudden coronary death process. This paper mainly summarizes the pathological characteristics of unstable coronary plaque based on clinical medical research, including plaque rupture, plaque erosion and calcified nodules, as well as the influencing factors leading to plaque instability, and briefly describes the research progress and technique of the atherosclerotic plaques, in order to improve the study on the mechanism of sudden coronary death and improve the accuracy of the forensic diagnosis of sudden coronary death by diagnosing different pathologic states of coronary atherosclerotic plaques.

Novel ratio-expressions of genes enables estimation of wound age in contused skeletal muscle.

Given that combination with multiple biomarkers may well raise the predictive value of wound age, it appears critically essential to identify new features under the limited cost. For this purpose, the present study explored whether the gene expression ratios provide unique time information as an additional indicator for wound age estimation not requiring the detection of new biomarkers and allowing full use of the available data. The expression levels of four wound-healing genes (Arid5a, Ier3, Stom, and Lcp1) were detected by real-time polymerase chain reaction, and a total of six expression ratios were calculated among these four genes. The results showed that the expression levels of four genes and six ratios of expression changed time-dependent during wound repair. The six expression ratios provided additional temporal information, distinct from the four genes analyzed separately by principal component analysis. The overall performance metrics for cross-validation and external validation of four typical prediction models were improved when six ratios of expression were added as additional input variables. Overall, expression ratios among genes provide temporal information and have excellent potential as predictive markers for wound age estimation. Combining the expression levels of genes with ratio-expression of genes may allow for more accurate estimates of the time of injury.

Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods.

OBJECTIVES: To estimate postmortem interval (PMI) by analyzing the protein changes in skeletal muscle tissues with the protein chip technology combined with multivariate analysis methods. METHODS: Rats were sacrificed for cervical dislocation and placed at 16 ℃. Water-soluble proteins in skeletal muscles were extracted at 10 time points (0 d, 1 d, 2 d, 3 d, 4 d, 5 d, 6 d, 7 d, 8 d and 9 d) after death. Protein expression profile data with relative molecular mass of 14 000-230 000 were obtained. Principal component analysis (PCA) and orthogonal partial least squares (OPLS) were used for data analysis. Fisher discriminant model and back propagation (BP) neural network model were constructed to classify and preliminarily estimate the PMI. In addition, the protein expression profiles data of human skeletal muscles at different time points after death were collected, and the relationship between them and PMI was analyzed by heat map and cluster analysis. RESULTS: The protein peak of rat skeletal muscle changed with PMI. The result of PCA combined with OPLS discriminant analysis showed statistical significance in groups with different time points (P<0.05) except 6 d, 7 d and 8 d after death. By Fisher discriminant analysis, the accuracy of internal cross-validation was 71.4% and the accuracy of external validation was 66.7%. The BP neural network model classification and preliminary estimation results showed the accuracy of internal cross-validation was 98.2%, and the accuracy of external validation was 95.8%. There was a significant difference in protein expression between 4 d and 25 h after death by the cluster analysis of the human skeletal muscle samples. CONCLUSIONS: The protein chip technology can quickly, accurately and repeatedly obtain water-soluble protein expression profiles in rats' and human skeletal muscles with the relative molecular mass of 14 000-230 000 at different time points postmortem. The establishment of multiple PMI estimation models based on multivariate analysis can provide a new idea and method for PMI estimation.

Exploring postmortem succession of rat intestinal microbiome for PMI based on machine learning algorithms and potential use for humans.

The microbial communities may undergo a meaningful successional change during the progress of decay and decomposition that could aid in determining the post-mortem interval (PMI). However, there are still challenges to applying microbiome-based evidence in law enforcement practice. In this study, we attempted to investigate the principles governing microbial community succession during decomposition of rat and human corpse, and explore their potential use for PMI of human cadavers. A controlled experiment was conducted to characterize temporal changes in microbial communities associated with rat corpses as they decomposed for 30 days. Obvious differences of microbial community structures were observed among different stages of decomposition, especially between decomposition of 0-7d and 9-30d. Thus, a two-layer model for PMI prediction was developed based on the succession of bacteria by combining classification and regression models using machine learning algorithms. Our results achieved 90.48% accuracy for discriminating groups of PMI 0-7d and 9-30d, and yielded a mean absolute error of 0.580d within 7d decomposition and 3.165d within 9-30d decomposition. Furthermore, samples from human cadavers were collected to gain the common succession of microbial community between rats and humans. Based on the 44 shared genera of rats and humans, a two-layer model of PMI was rebuilt to be applied for PMI prediction of human cadavers. Accurate estimates indicated a reproducible succession of gut microbes across rats and humans. Together these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating PMI.

Forensic identification of sudden cardiac death: a new approach combining metabolomics and machine learning.

The determination of sudden cardiac death (SCD) is one of the difficult tasks in the forensic practice, especially in the absence of specific morphological changes in the autopsies and histological investigations. In this study, we combined the metabolic characteristics from corpse specimens of cardiac blood and cardiac muscle to predict SCD. Firstly, ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UPLC-HRMS)-based untargeted metabolomics was applied to obtain the metabolomic profiles of the specimens, and 18 and 16 differential metabolites were identified in the cardiac blood and cardiac muscle from the corpses of those who died of SCD, respectively. Several possible metabolic pathways were proposed to explain these metabolic alterations, including the metabolism of energy, amino acids, and lipids. Then, we validated the capability of these combinations of differential metabolites to distinguish between SCD and non-SCD through multiple machine learning algorithms. The results showed that stacking model integrated differential metabolites featured from the specimens showed the best performance with 92.31% accuracy, 93.08% precision, 92.31% recall, 91.96% F1 score, and 0.92 AUC. Our results revealed that the SCD metabolic signature identified by metabolomics and ensemble learning in cardiac blood and cardiac muscle has potential in SCD post-mortem diagnosis and metabolic mechanism investigations.

A novel method for determining postmortem interval based on the metabolomics of multiple organs combined with ensemble learning techniques.

Determining postmortem interval (PMI) is one of the most challenging and essential endeavors in forensic science. Developments in PMI estimation can take advantage of machine learning techniques. Currently, applying an algorithm to obtain information on multiple organs and conducting joint analysis to accurately estimate PMI are still in the early stages. This study aimed to establish a multi-organ stacking model that estimates PMI by analyzing differential compounds of four organs in rats. In a total of 140 rats, skeletal muscle, liver, lung, and kidney tissue samples were collected at each time point after death. Ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to determine the compound profiles of the samples. The original data were preprocessed using multivariate statistical analysis to determine discriminant compounds. In addition, three interrelated and increasingly complex patterns (single organ optimal model, single organ stacking model, multi-organ stacking model) were established to estimate PMI. The accuracy and generalized area under the receiver operating characteristic curve of the multi-organ stacking model were the highest at 93% and 0.96, respectively. Only 1 of the 14 external validation samples was misclassified by the multi-organ stacking model. The results demonstrate that the application of the multi-organ combination to the stacking algorithm is a potential forensic tool for the accurate estimation of PMI.

Application of Linear Regression Model of Gpnmb Gene in Rat Injury Time Estimation.

OBJECTIVES: To investigate the effects of injury time, postmortem interval (PMI) and postmortem storage temperature on mRNA expression of glycoprotein non-metastatic melanoma protein B (Gpnmb), and to establish a linear regression model between Gpnmb mRNA expression and injury time, to provide aimed at providing potential indexes for injury time estimation. METHODS: Test group SD rats were anesthetized and subjected to blunt contusion and randomly divided into 0 h, 4 h, 8 h, 12 h, 16 h, 20 h and 24 h groups after injury, with 18 rats in each group. After cervical dislocation, 6 rats in each group were collected and stored at 0 ℃, 16 ℃ and 26 ℃, respectively. The muscle tissue samples of quadriceps femoris injury were collected at 0 h, 12 h and 24 h postmortem at the same temperature. The grouping method and treatment method of the rats in the validation group were the same as above. The expression of Gpnmb mRNA in rat skeletal muscle was detected by RT-qPCR. The Pearson correlation coefficient was used to evaluate the correlation between Gpnmb mRNA expression and injury time, PMI, and postmortem storage temperature. SPSS 25.0 software was used to construct a linear regression model, and the validation group data was used for the back-substitution test. RESULTS: The expression of Gpnmb mRNA continued to increase with the prolongation of injury time, and the expression level was highly correlated with injury time (P<0.05), but had little correlation with PMI and postmortem storage temperature (P>0.05). The linear regression equation between injury time (y) and Gpnmb mRNA relative expression (x) was y=0.611 x+4.489. The back-substitution test proved that the prediction of the model was accurate. CONCLUSIONS: The expression of Gpnmb mRNA is almost not affected by the PMI and postmortem storage temperature, but is mainly related to the time of injury. Therefore, a linear regression model can be established to infer the time of injury.

Estimating the time of skeletal muscle contusion based on the spatial distribution of neutrophils: a practical approach to forensic problems.

The study aimed to explore the neutrophil's spatial distributions used to estimate the histological age of contused skeletal muscle, and assessed the accuracy of various indicators, such as the proportion of neutrophils, "neutrophil mean distance," and distribution of neutrophils in areas of "contiguous contour lines." Fifty-five Sprague-Dawley rats were divided randomly into a control group and contusion groups at 1, 1.5, 2, 3, 4, and 6 h, as well as 1, 3, 5, and 15 days, post-injury (n = 5 per group). Nuclei and neutrophils were detected by hematoxylin and eosin (HE) staining and immunohistochemical (IHC) staining. At 0-24 h after injury, the distribution of neutrophils at distances of 100, 200, 300, 400, 500, and 600 µm from adjacent blood vessels was determined, and the best samples were screened to estimate wound age. To estimate wound age as accurately as possible, Fisher discriminant analysis (FDA) of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils was performed, and 100.0% and 95.0% of the original and cross-validated cases were correctly classified, respectively. The spatial distribution of neutrophils at different distances from adjacent blood vessels showed a strong correlation with the histological age of contusion skeletal muscle, and the combination of the proportion of neutrophils, neutrophil mean distance, and distribution of neutrophils could be used to accurately estimate wound age.

Combining with lab-on-chip technology and multi-organ fusion strategy to estimate post-mortem interval of rat.

Background: The estimation of post-mortem interval (PMI) is one of the most important problems in forensic pathology all the time. Although many classical methods can be used to estimate time since death, accurate and rapid estimation of PMI is still a difficult task in forensic practice, so the estimation of PMI requires a faster, more accurate, and more convenient method. Materials and methods: In this study, an experimental method, lab-on-chip, is used to analyze the characterizations of polypeptide fragments of the lung, liver, kidney, and skeletal muscle of rats at defined time points after death (0, 1, 2, 3, 5, 7, 9, 12, 15, 18, 21, 24, 27, and 30 days). Then, machine learning algorithms (base model: LR, SVM, RF, GBDT, and MLPC; ensemble model: stacking, soft voting, and soft-weighted voting) are applied to predict PMI with single organ. Multi-organ fusion strategy is designed to predict PMI based on multiple organs. Then, the ensemble pruning algorithm determines the best combination of multi-organ. Results: The kidney is the best single organ for predicting the time of death, and its internal and external accuracy is 0.808 and 0.714, respectively. Multi-organ fusion strategy dramatically improves the performance of PMI estimation, and its internal and external accuracy is 0.962 and 0.893, respectively. Finally, the best organ combination determined by the ensemble pruning algorithm is all organs, such as lung, liver, kidney, and skeletal muscle. Conclusion: Lab-on-chip is feasible to detect polypeptide fragments and multi-organ fusion is more accurate than single organ for PMI estimation.

Insight into molecular profile changes after skeletal muscle contusion using microarray and bioinformatics analyses.

Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. The present study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process. A total of 33 rats were divided randomly into control (n=3), mild contusion (n=15), and severe contusion (n=15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n=3 per subgroup). A total of 2844 and 2298 differentially expressed genes (DEGs) were identified using microarray analyses in the mild and severe contusions, respectively. From the analysis of the 1620 coexpressed genes in mildly and severely contused muscle, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. The functional analyses of genes in the functional modules and temporal clusters were performed, and the hub genes in each module-cluster pair were identified. Interestingly, we found that genes down-regulated at 24-48 h were largely associated with metabolic processes, especially of the oxidative phosphorylation (OXPHOS), which has been rarely reported. These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

Identifying biomarkers for evaluating wound extent and age in the contused muscle of rats using microarray analysis: a pilot study.

Wound age estimation is still one of the most important and significant challenges in forensic practice. The extent of wound damage greatly affects the accuracy and reliability of wound age estimation, so it is important to find effective biomarkers to help diagnose wound degree and wound age. In the present study, the gene expression profiles of both mild and severe injuries in 33 rats were assayed at 0, 1, 3, 24, 48, and 168 hours using the Affymetrix microarray system to provide biomarkers for the evaluation of wound age and the extent of the wound. After obtaining thousands of differentially expressed genes, a principal component analysis, the least absolute shrinkage and selection operator, and a time-series analysis were used to select the most predictive prognostic genes. Finally, 15 genes were screened for evaluating the extent of wound damage, and the top 60 genes were also screened for wound age estimation in mild and severe injury. Selected indicators showed good diagnostic performance for identifying the extent of the wound and wound age in a Fisher discriminant analysis. A function analysis showed that the candidate genes were mainly related to cell proliferation and the inflammatory response, primarily IL-17 and the Hematopoietic cell lineage signalling pathway. The results revealed that these genes play an essential role in wound-healing and yield helpful and valuable potential biomarkers for further targeted studies.

Estimating Postmortem Interval Using Intestinal Microbiota Diversity Based on 16S rRNA High-throughput Sequencing Technology. / 基于16S rRNAé«˜é€šé‡æµ‹åºæŠ€æœ¯çš„è‚ é“èŒç¾¤å¤šæ ·æ€§æŽ¨æ–­æ­»äº¡æ—¶é—´.

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.

Novel insights into wound age estimation: combined with "up, no change, or down" system and cosine similarity in python environment.

Wound age estimation is a complex, multifactorial issue. It is considered to have great practical significance that combining multi-biomarkers and multi-methods for injury time estimation. We optimized our earlier "up, no change, or down" model by adding data on the expression levels of mRNAs encoding ABHD2, MAD2L2, and ARID5A, and we converted the relative quantitative expression levels of seven genes into a vector rather than a color model. We used Python to derive the cosine similarity (CS) between a test set and the vector matrix; the highest similarity most accurately reflected the injury time. For the optimized model, the internal and external verifications were approximately 0.71 and 0.66, respectively. The good double-blinded results indicated that the model was stable and reliable. In summary, we used a vector matrix and cosine similarities derived by Python to mine the levels of genes expressed in contused skeletal muscle. We are the first to combine several biomarkers and methods for wound age estimation.

Temporal expression of wound healing-related genes inform wound age estimation in rats after a skeletal muscle contusion: a multivariate statistical model analysis.

Although many time-dependent parameters involved in wound healing have been exhaustively investigated, establishing an objective and reliable means for estimating wound age remains a challenge. In this study, 78 Sprague-Dawley rats were divided randomly into a control group and contusion groups at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-injury (n = 6 per group). The expression of 35 wound healing-related genes was explored in contused skeletal muscle by real-time polymerase chain reaction. Differences between the groups were assessed by partial least squares discriminant analysis (PLS-DA). The results show that the samples were classified into three groups by wound age (4-12, 16-24, and 28-48 h). A Fisher discriminant analysis model of 14 selected genes was constructed, and 94.9% cross-validated grouped cases were correctly classified. A PLS regression analysis using 14 genes showed reasonable internal predictive validity, with a root mean squared error of cross-validation of approximately 8 h. To examine whether the prediction models were capable of analyzing new (ungrouped) cases, an external validation was carried out using the expression data from an additional 30 rats. Approximately 76.7% of ungrouped cases were correctly classified, which was a lower proportion than that for cross-validation. Similarly, the prediction results of the PLS model showed lower relatively external predictive validity (root mean squared error of prediction = 11 h) than internal predictive validity. Although the prediction results were less accurate than expected, the gene expression modeling and multivariate analyses showed great potential for estimating injury time. These multivariate methods may be valuable when devising future wound time estimation strategies.

Integrating microRNA and messenger RNA expression profiles in a rat model of deep vein thrombosis.

Deep vein thrombosis (DVT) is a disease involving multiple genes and systems. MicroRNAs (miRNAs) represent a class of non-coding small RNAs that post-transcriptionally suppress their target genes. The expression patterns of miRNA and messenger RNA (mRNA) in DVT remain poorly characterized. The aim of the present study was to evaluate miRNA and mRNA expression profiles in a stasis-induced DVT rat model. Male SD rats were randomly divided into three groups as follows: DVT, sham and control. The inferior vena cava (IVC) of rats was ligated to construct stasis-induced DVT models. Rats were sacrificed three days after ligation, and morphological changes in the vein tissues were observed by hematoxylin and eosin and Masson staining. The miRNA and mRNA expression profiles were evaluated by microarrays, followed by bioinformatics analysis. The microarray analysis identified 22 miRNAs and 487 mRNAs that were significantly differentially expressed between the experimental and control groups, and between the experimental and sham groups, but not between the control and sham groups (P≤0.05; ≥2.0­fold change). By subsequent bioinformatics analysis, a 19 miRNA-98 mRNAs network was constructed in the stasis-induced DVT rat model. Notably, the majority of these miRNAs and mRNAs are reported to be expressed by endothelial cells (ECs) and are associated with the function of ECs. The results provide evidence indicating that the regulatory association of miRNA and mRNA points to key roles played by ECs in thrombosis. These findings advance our understanding of the molecular regulatory mechanisms underlying the pathophysiology of DVT.

An "up, no change, or down" system: Time-dependent expression of mRNAs in contused skeletal muscle of rats used for wound age estimation.

The combined use of multiple markers is considered a promising strategy in estimating the age of wounds. We sought to develop an "up, no change, or down" system and to explore how to combine and use various parameters. In total, 78 Sprague Dawley rats were divided randomly into a control group and contusion groups of 4-, 8-, 12-, 16-, 20-, 24-, 28-, 32-, 36-, 40-, 44-, and 48-h post-injury (n=6 per group). A contusion was produced in the right limb of the rats under diethyl ether anesthesia by a drop-ball technique; the animals were sacrificed at certain time points thereafter, using a lethal dose of pentobarbital. Levels of PUM2, TAB2, GJC1, and CHRNA1 mRNAs were detected in contused muscle using real-time PCR. An up, no change, or down system was developed with the relative quantities of the four mRNAs recorded as black, dark gray, or light gray boxes, representing up-, no change, or down-regulation of the gene of interest during wound repair. The four transcripts were combined and used as a marker cluster for color model analysis of each contusion group. Levels of PUM2, TAB2, and GJC1 mRNAs decreased, whereas that of CHRNA1 increased in wound repair (P<0.05). The up, no change, or down system was adequate to distinguish most time groups with the color model. Thus, the proposed up, no change, or down system provide the means to determine the minimal periods of early wounds.

Comparison of the homogeneity of mRNAs encoding SFRP5, FZD4, and Fosl1 in post-injury intervals: Subcellular localization of markers may influence wound age estimation.

The inter-group heterogeneity and intra-group homogeneity of relative expression are very necessary when the mRNA were used to determine wound age accurately in forensic medicine. The aim of this study was to assess the intra-group homogeneity of SFRP5, FZD4 and Fosl1 mRNAs in post-injury intervals. The corresponding proteins show different subcellular locations. A total of 78 Sprague-Dawley rats were divided into control and contusion groups. At 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, or 48 h (n = 6 per group) after contusion (under anesthesia by chloral hydrate intraperitoneally), the rats were sacrificed using a lethal dose of pentobarbital, and samples of the injured muscles were collected. The raw Ct values of SFRP5, FZD4, and Fosl1 mRNAs were obtained using real-time PCR. After normalized to RPL13 mRNA levels, the coefficient of variation (CV) and the relative average deviation (d%) of each normalized Ct, and their relative expression levels, were calculated in each post-injury interval. Two methods were applied to compare the homogeneity of the three genes. First, each gene was given a score based on its CV value in each post-injury interval. Then, the sum of the 13 scores was calculated; a low sum indicated high homogeneity. Second, the 13 calculated CVs or d%s were used as raw data, which was described as the mean ± SD. Based on this mean ± SD, a CV of the CVs and a d% of the d%s were calculated to represent the variation; a low value indicated high homogeneity. The sum of the variability of FZD4 mRNA was lower than those of the SFRP5 and Fosl1 mRNAs, consistent with the results that the FZD4 mRNA had the lowest mean, the smallest CV of all CVs, and the smallest d% of all d%s, among the three genes. In conclusion, these data indicated that mRNA encoding membranous FZD4 was likely to be more homogeneous than those encoding SFRP5 and Fosl1 within post-injury intervals.

[Relation between Injury Time and the Expression of COX6C mRNA in Skeletal Muscle of Rats after Contusion].

OBJECTIVE: To investigate the relation between injury time and the expression of cytochrome c oxidase subunit VIc (COX6C) mRNA in skeletal muscle of rat after contusion. METHODS: A total of fifty-four SD rats were divided into the control group and the contusion groups (0.5, 1, 6, 12, 18, 24, 30, and 36 h after contusion), randomly. The contusion model was established by free fall drop of gravity hammer. At corresponding time point after contusion, the regular histology was examined and expression level of COX6C mRNA was tested by real-time PCR after extraction of total RNA from the tissues. RESULTS: The main pathological features of 6 h after injury included edema and hemorrhage in myocytes with no inflammatory cells found. After 6 hours, the findings included myocyte degeneration and necrosis, inflammatory cells infiltration, and fibrous connective tissue proliferation in the contused zone. The expression level of COX6C mRNA was higher than that of the control group within 6 h after contusion. The expression level was lower than that of the control group from 6-36 h after contusion. CONCLUSION: The level of COX6C mRNA expresses in a regular way after contusion. It may be useful for estimating wound age in combination with the results of pathological features.

Relative Expression of Indicators for Wound Age Estimation in Forensic Pathology.

OBJECTIVE: In order to understand which kind of function genes play an important role for estimating wound age, the variation of difference genes' mRNA expression were compared after injury. METHODS: The mRNA expression levels of seven candidate genes (ICAM-1, NF-κB, MX2, MT1, MT2, sTnI, and Cox6c) were analyzed in contused rat skeletal muscle at different time points using real-time fluorescent quantitative PCR (RT-qPCR). The raw Ct values were normalized relative to that of RPL32 mRNA, and converted to standard Ct values. At each time point after injury, the standard deviations (SD) of the standard Ct values were calculated by SPSS. RESULTS: The expression trends of the seven genes were all found to be related to wound age, but there were lower variation coefficients and greater reliability of s TnI and Cox6c when compared with other genes. CONCLUSION: The genes encoding structural proteins or proteins that perform basic functions can be suitable for wound age estimation.

[Expression of SFRP5 mRNA in Rat Skeletal Muscle after Contusion].

OBJECTIVE: To investigate the relationship between the expression of secreted frizzled-related protein 5 (SFRP5) mRNA and the time interval after skeletal muscle injury in rats by real-time PCR. METHODS: A total of ninety SD rats were randomly divided into the contusion groups at different times including 4h, 8h, 12h, 16h, 20h, 24h, 28h, 32h, 36h, 40h, 44h, 48h after contusion, incision groups at different times including 4h and 8h after incision and the control group. The samples were taken from the contused zone at different time points. The total RNA was isolated from the samples and reversely transcribed to analyze the expression levels of SFRP5 mRNA. RESULTS: Compared to the control group, the expression of SFRP5 mRNA in contusion groups were down-regulated within 48 h after contusion and reached the lowest level at 20 h, and the expression of SFRP5 mRNA gradually increased from 20 h to 48 h after contusion. The expression of SFRP5 mRNA in the incised groups were significantly lower than that of the contusion groups at 4 h after injury. At the time of 8 h, the expression levels between the contusion and incision groups showed no statistically significant difference. CONCLUSION: It is suggested that SFRP5 mRNA analysis may show regular expression and can be a marker for estimation of skeletal muscle injury age.