Pathological and ATR-FTIR spectral changes of delayed splenic rupture and medical significance.

Traumatic delayed splenic rupture often follows by a "latent period" without typical symptoms after injury. During this period, though there are no obvious symptoms, the injury is still present and changing. In this study, we constructed an SD rat model of delayed splenic rupture; evaluated the model by HE staining, Perl's staining, Masson trichrome staining and immunohistochemical staining; observed the pathological changes of spleen tissue in delayed splenic rupture at different times after splenic injury; we found that pathological change of injured tissues were different from non-injured, and has phases-change patterns, it can be roughly divided into three phases: 2-7 d, 10-14 d, and 18-28.We then investigated the relationship between the pathological changes and FTIR spectroscopy by chemometric methods. The main distinction of injured and non-injured tissue was the protein secondary structure of amide I, and the main distinctions of different phases of delayed splenic rupture were protein secondary structures and content of amide I and amide II.A classification model developed by SVM-DA was used to infer three phases (2-7 days, 10-12 days and 14-28 days). According to the most probable class, the accuracy of external validation is 96.7%. The results indicate that FTIR spectroscopy combined with various types of pathological staining has a potential for forensic identification and can provide theoretical support and diagnostic reference on clinical persistent injury.

Identifying traumatic brain injury (TBI) by ATR-FTIR spectroscopy in a mouse model.

Traumatic brain injury (TBI) is one of the most common mechanical injuries and plays a significant role in forensic practice. For cadavers, however, accurate diagnosis of TBI becomes a more and more challenging task as the level of decomposition increases. Our main purpose was to investigate whether TBI in putrefied mouse cadavers can be identified by Fourier Transform Infrared (FT-IR). The method proposed by Feeney et al. was used to establish the mouse TBI model. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modeling were used to distinguish fresh and putrefied brain tissues. Then, we established two PLS-DA models to identify injured area samples in fresh and putrefied brain tissue samples. The accuracy of the two models were 100% and 92.5%. Our preliminary research has proved that the use of FT-IR spectroscopy combined with chemometrics can identify TBI more quickly and accurately in cadavers, providing crucial evidence for judicial proceedings.

Analysis of Postmortem Intestinal Microbiota Successional Patterns with Application in Postmortem Interval Estimation.

Microorganisms play a vital role in the decomposition of vertebrate remains in natural nutrient cycling, and the postmortem microbial succession patterns during decomposition remain unclear. The present study used hierarchical clustering based on Manhattan distances to analyze the similarities and differences among postmortem intestinal microbial succession patterns based on microbial 16S rDNA sequences in a mouse decomposition model. Based on the similarity, seven different classes of succession patterns were obtained. Generally, the normal intestinal flora in the cecum was gradually decreased with changes in the living conditions after death, while some facultative anaerobes and obligate anaerobes grew and multiplied upon oxygen consumption. Furthermore, a random forest regression model was developed to predict the postmortem interval based on the microbial succession trend dataset. The model demonstrated a mean absolute error of 20.01 h and a squared correlation coefficient of 0.95 during 15-day decomposition. Lactobacillus, Dubosiella, Enterococcus, and the Lachnospiraceae NK4A136 group were considered significant biomarkers for this model according to the ranked list. The present study explored microbial succession patterns in terms of relative abundances and variety, aiding in the prediction of postmortem intervals and offering some information on microbial behaviors in decomposition ecology.

Identification of antemortem and postmortem fractures in a complex environment by FTIR spectroscopy based on a rabbit tibial fracture self-control model.

The identification of antemortem and postmortem fractures is a critical and challenging task for forensic researchers. Based on our preliminary studies, we explored whether the combination of Fourier transform infrared spectroscopy (FTIR) and chemometrics can identify antemortem and postmortem fractures in complex environments. The impacts of the four environments on the bone spectrum were analyzed by principal component analysis (PCA). It was found that the bone degradation rate in the submerged and ground surface (GS) environments was higher than that in the buried and constant temperature and moisture (CTM) environments. Additionally, the bone degradation rate in buried environment higher than that in the CTM environment. The average spectrum, PCA and partial least squares discriminant analysis (PLS-DA) results all revealed that there were significant differences between the antemortem fracture and the remaining three groups in a complex environment. Compared with the antemortem fracture, the antemortem fracture control (AFC) and postmortem fracture control (PFC) tended to be more similar to the postmortem fracture. According to the loading plot, amide I and amide II were the main components that contributed to the identification of the antemortem fracture, AFC, postmortem fracture, and PFC. Finally, we established a differential model for the antemortem and postmortem fractures (an accuracy of 96.9%), and a differentiation model for the antemortem fracture, AFC, postmortem fracture, and PFC (an accuracy of 87.5%). In conclusion, FTIR spectroscopy is a reliable tool for the identification of antemortem and postmortem fractures in complex environments.

Identification of antemortem, perimortem and postmortem fractures by FTIR spectroscopy based on a rabbit tibial fracture model.

The identification of antemortem, perimortem and postmortem fractures is very important for forensic pathologists and anthropologists. However, traditional methods are subjective, time-consuming, and have low accuracy, which do not fundamentally solve the problem. In this study, we utilized Fourier transform infrared (FTIR) spectroscopy and chemometrics to identify antemortem, perimortem and postmortem fractures in a rabbit tibial fracture model. Based on the results of the principal component analysis (PCA), changes in the ante-perimortem fracture repair process are mainly associated with protein variations, while postmortem fractures are more likely to result in lipid changes during degradation. Then, a partial least squares discriminant analysis (PLS-DA) was performed to assess the classification ability of the training and predictive datasets, with classification accuracies of 88.9% and 86.7%, respectively. According to the latent variable 1 (LV1) loading plot, amide I and amide II (proteins) are mostly classified as ante-perimortem and postmortem fractures. In conclusion, FTIR spectroscopy is a reliable tool to identify antemortem, perimortem and postmortem fractures. FTIR has the advantages of rapid, objective and strong discrimination. and shows great potential for analyzing forensic cases under actual natural conditions.

Predicting postmortem interval based on microbial community sequences and machine learning algorithms.

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.