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

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In the present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague-Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h postcontusion (n = 9). Then, the samples were analysed using ultraperformance liquid chromatography coupled with high-resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4, 8, 12, 16-20, 24-32, 36-40, and 44-48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6%, which was much higher than that of the single model. In summary, the multilayer perceptron-multilayer perceptron tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework.

Key Points

The changes of metabolite profile were correlated with the time interval after injury in contused skeletal muscle.A panel of 43 endogenous metabolites screened by ultraperformance liquid chromatography coupled with high-resolution mass spectrometry could distinguish the wound ages.The multilayer perceptron (MLP) algorithm exhibited a robust performance in wound age estimation using metabolites.The combination of matabolomics and MLP-MLP tandem model could improve the accuracy of inferring the time interval after injury.

Screening criteria of mRNA indicators for wound age estimation

Wound age estimation is a crucial and challenging problem in forensic pathology. Although mRNA is the most commonly used indicator for wound age estimation, screening criteria are lacking. In the present study, the feasibility of screening criteria using mRNA to determine injury time based on the adenylate-uridylate-rich element (ARE) structure and gene ontology (GO) categories were evaluated. A total of 78 Sprague-Dawley male rats were contused and sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h after inflicting injury. The candidate mRNAs were classified based on with or without ARE structure and GO category function. The mRNA expression levels were detected using qRT-PCR. In addition, the standard deviation (STD), mean deviation (MD), relative average deviation (d%), and coefficient of variation (CV) were calculated based on mRNA expression levels. The CV score (CVs) and the CV of CV (CV'CV) were calculated to measure heterogeneity. Finally, based on classic principles, the accuracy of combination of candidate mRNAs was assessed using discriminant analysis to construct a multivariate model for inferring wound age. The results of homogeneity evaluation of each group based on CVs were consistent with the MD, STD, d%, and CV results, indicating the credibility of the evaluation results based on CVs. The candidate mRNAs without ARE structure and classified as cellular component (CC) GO category (ARE-CC) had the highest CVs, showing the mRNAs with these characteristics are the most homogenous mRNAs and best suited for wound age estimation. The highest accuracy was 91.0% when the mRNAs without ARE structure were used to infer the wound age based on the discrimination model. The accuracy of mRNAs classified into CC or multiple function (MF) GO category was higher than mRNAs in the biological process (BP) category. In all subgroups, the accuracy of the composite identification model of mRNA composition without ARE structure and classified as CC was higher than other subgroups. The mRNAs without ARE structure and belonging to the CC GO category were more homogenous, showed higher accuracy for estimating wound age, and were appropriate for rat skeletal muscle wound age estimation. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1986770 .

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA

(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.

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 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.