Postmortem Interval Estimation Using Protein Chip Technology Combined with Multivariate Analysis Methods / 法医学杂志

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.

Correlation between the Polymorphism of Coagulation-Related Genes and Lower Extremity Deep Venous Thrombosis / 法医学杂志

Objective To investigate the correlation between the polymorphism of 4 coagulation-related genes, rs1799963 （coagulation factor V gene Leiden）, rs6025 （prothrombin gene G20210A）, rs1042579 （thrombomodulin protein gene c.1418C>T） and rs1801131 （methylenetetrahydroflate reductase gene） and lower extremity deep venous thrombosis （LEDVT）. Methods The 4 genotypes mentioned above of 150 LEDVT patients and 153 healthy controls were detected by the kompetitive allele specific polymerase chain reaction （KASP）, then related blood biochemical indicators were collected, binary Logistic regression was established to screen the independent risk factors of LEDVT, and the correlation between polymorphism of 4 coagulation-related genes and LEDVT and its indicators under different genetic modes after adjusting confounding factors were analyzed. Results Five variables, D-dimer, fibrinogen degradation product, homocysteine, sex and age might be the risk factors of LEDVT. These variables were put into 4 genetic inheritance models, and adjusted in binary Logistic regression. The results suggested that the mutations of rs1042579 were correlated with LEDVT under dominant inheritance mode. Conclusion The gene polymorphism of rs1799963, rs6025 and rs1801131 has no significant correlation with the formation of LEDVT. The gene polymorphism of rs1042579 plays a role under dominant inheritance mode, and might be an independent risk factor for formation of LEDVT.

Estimating Postmortem Interval by Protein Chip Detection Technology Combined with Multidimensional Statistics / 法医学杂志

Objective To obtain the protein expression profile of rat liver tissue after death by the 2100 bioanalyzer combined with protein chip, and infer the relationship between protein expression profile and postmortem interval. Methods Rats were killed by abdominal anesthesia and placed at 16 ℃. Water-soluble proteins in liver tissues were extracted at 14 time points after death. The expression profile data of proteins with relative molecular weight of 14 000-230 000 were obtained using protein chip, and principal component analysis （PCA）, partial least squares-discriminant analysis （PLS-DA） and Fisher discriminant were used to analyze the data. Results According to the changes of protein expression profile, the postmortem interval was divided into group A （0 d）, group B （1-9 d）, group C （12-30 d） according to the result of PLS-DA. The prediction accuracy of the training set and test set of the model were all 100.0%, and the internal cross-validation of the training set was 100.0% according to Fisher discriminant. The Fisher discriminant model at each time point of group B and C was established to narrow the time window of postmortem interval estimation. The prediction accuracy of the training set and test set were all 100.0%, and the internal cross-validation accuracy of the training set was 100.0% in group B. The prediction accuracy of the training set and test set were respectively 95.2% and 78.6% in group C, and the internal cross-validation of the training set was 88.1%. Conclusion Protein chip detection technology can quickly and easily obtain the expression profile of water-soluble proteins of rat liver tissue with a relative molecular weight of 14 000-230 000 at different time points after death. PLS-DA and Fisher discriminant models are established to classify and predict the postmortem interval, in order to provide new ideas and methods for postmortem interval estimation.