International Journal of Legal Medicine最新文献

Analysing genetic material from skeletonised human remains has become valuable in forensic and archaeological contexts. While the petrous bone is often preferred for DNA extraction, its availability is not guaranteed, and because of destructive sampling, it is not frequently used in forensic cases. This study explores the potential of patellae as an alternative source of bone material for genetic investigations. Forty-five patellae were sampled from a post-World War II mass grave and an archaeological Christian cemetery dated from the 13th to 19th centuries. A full demineralisation extraction method was used to obtain the DNA, and real-time PCR quantification was used to determine the quantity and quality of DNA. To evaluate the suitability of patellae for forensic and archaeological analyses, short tandem repeat (STR) typing was performed using the ESI17 Fast PCR amplification kit (Promega). To explore the difference in DNA yield, DNA degradation and STR typing success between the post-World War II and archaeological patellae, statistical analysis was performed. The results revealed significantly higher DNA yield and STR typing success in WWII patellae and higher degradation of DNA in archaeological patellae, highlighting the impact of environmental exposure time on genetic material preservation. Almost all WWII patellae achieved a high success rate in STR typing with full profiles generated. More than half of the archaeological patellae showed high STR typing performance and highly informative partial profiles were obtained, indicating the suitability of patellae not only for forensic purposes but also for archaeological genetic analyses.

The Union Territory of Jammu and Kashmir has always been a point of contention among India, Pakistan, and China for various reasons. This region has great significance historically due to its geography and ethnic diversity. However, a comprehensive genetic study providing the genetic landscape of Jammu and Kashmir was lacking until now. In this study, we analysed 21 hyper-variable autosomal STR loci of 694 individuals from Jammu and Kashmir and compared them with neighbouring populations to explore the genetic implications of demographic dynamics that have taken place in this region. Our findings suggest that the population of Jammu and Kashmir is a genetic mixture of two major clines, northern and southern, that correspond with the variation in geography, linguistics, and demography of the region. Broadly, the gene flow into the southern cline appears to be in historical times perhaps as a result of the spread of Gurjars, Dogri language and invasions of Mongols. However, the arrival of ancient nomadic tribes into this region may belong to the time depth of the arrival of pastoralist communities in this region. As a future perspective, a comprehensive study including samples from individual tribes will provide fine-scale structure of the gene pool of Jammu and Kashmir.

"Shedder status" describes the inherent variation between individuals to leave touch DNA on a surface through direct contact. Depending on the amount and quality of DNA or cellular deposition, individuals are typically deemed high, intermediate, or low shedders. Although many shedder tests have been described, variability in study design and categorisation criteria has limited the ability of researchers to accurately compare results, as well as accrue the necessary population data. As activity level reporting becomes more common, the need for reliable and standardised testing increases. To assess reproducibility, this study compared shedder status data generated by six participants using three different shedder tests, as modified from the literature. This involved DNA quantification and profiling of a handprint made on a glass plate, DNA quantification and profiling of a grip mark made on a plastic conical tube, and cell scoring of a Diamond™ Dye-stained fingermark. All participants washed and dried their hands fifteen minutes before each deposit. To assess the impact of behaviour on shedder designation, participants either refrained from activity or went about their daily tasks during this wait. The shedder status of participants changed between tests, as DNA-based testing often generated lower shedder statuses than cell scores. Further, when different categorisation methods were applied to a single test, intra-person variability increased as the number of shedder designations increased from two (low/high) to five (low/low-intermediate/intermediate/intermediate-high/high). Moving forward, the utilisation of a single shedder test and standardised categorisation criteria is needed to employ shedder testing in forensic casework.

In this paper, we present a forensic perspective on classifying gunshot wound patterns using Deep Learning (DL). Although DL has revolutionized various medical specialties, such as automating tasks like medical image classification, its applications in forensic contexts have been limited despite the inherently visual nature of the field. This study investigates the application of DL techniques (59 architectures) to classify gunshot wounds in a forensic context, focusing on distinguishing between entry and exit wounds and determining the Medical-Legal Shooting Distance (MLSD), which classifies wounds as contact, close range, or distant, based on digital images from real crime scene cases. A comprehensive database was constructed with 2,551 images, including 1,883 entries and 668 exit wounds. The ResNet152 architecture demonstrated superior performance in both entry and exit wound classification and MLSD categorization. For the first task, achieved accuracy of 86.90% and an AUC of 82.09%. For MLSD, the ResNet152 showed an accuracy of 92.48% and AUC up to 94.36%, though sample imbalance affected the metrics. Our findings underscore the challenges of standardizing wound images due to varying capture conditions but reflect the practical realities of forensic work. This research highlights the significant potential of DL in enhancing forensic pathology practices, advocating for Artificial Intelligence (AI) as a supportive tool to complement human expertise in forensic investigations.

Sexual violence against women remains a global challenge, with Brazil exemplifying persistent issues despite legislative advancements. This study examined sexual violence within São Paulo state, Brazil's largest and economically affluent state, focusing on prevalence, patterns, forensic evidence, and health consequences. We analyzed 40,757 medical reports of alleged cases of rape against women available from the São Paulo Medical Legal Institute from 2014 to 2017. An increase in female rape cases was observed, reflecting broader societal trends, but underreporting, prevalent due to fear and stigma, underscored the need for multifaceted interventions. We also explored correlations between rape prevalence and the Human Development Index, highlighting disparities between urban and rural areas. Additionally, the study revealed concerning proportions of cases involving vulnerable victims, emphasizing the imperative for tailored interventions and legislative reforms. Furthermore, we demonstrated that only a small portion of medicolegal examinations were able to find evidence of sexual violence or recent sexual acts, necessary for conducting the criminal investigation of the alleged act. Therefore, despite challenges in forensic evidence collection, efforts to enhance medical examination protocols and awareness among professionals are crucial. Moreover, findings underscored the significant health consequences of rape, including unwanted pregnancies and sexually transmitted infections, necessitating comprehensive healthcare services for survivors. In conclusion, we reinforced that addressing sexual violence demands coordinated action, including legislative reforms, community engagement, and survivor-centered approaches, to create safer and more resilient communities.

Biological sex estimation in forensic anthropology is a crucial topic, and the patella has shown promise in this regard due to its sexual dimorphism. This study uses 12 machine learning models for sex estimation based on three patellar measurements (maximum height, breadth, and thickness). Data was collected from 180 skeletons of a contemporary Italian population (83 males and 97 females) as well as from an independent sample of 21 forensic cases (13 males and 8 females). Statistical analyses indicated that each of the variables exhibited significant sexual dimorphism. To predict biological sex, the classifiers were built using 70% of a reference sample, then tested on the remaining 30% of the original sample and then tested again on the independent sample. The different classifiers generated accuracies varied between 0.85 and 0.91 on the reference sample and between 0.71 and 0.95 for the validation sample. SVM classifier stood out with the highest accuracy and seemed the best model for our study.This study contributes to the growing application of machine learning in forensic anthropology by being the first to apply such techniques to patellar measurements in an Italian population. It aims to enhance the accuracy and efficiency of biological sex estimation from the patella, building on promising results observed with other skeletal elements.

Due to their unique anatomy, paranasal sinuses have been used for comparative identification between post-mortem CT (PMCT) and ante-mortem CT (AMCT). However, data security issues arise when transferring raw AMCT images of a suspected identity. The aim of this study was to derive and validate an identification score based on CT slices extracted from successive CTs for the identification of subjects. For derivation procedure, we included patients who underwent two successive AMCTs at ≥ 1-year interval (n = 98), and 4 radiologists individually assessed similarity of prespecified CT slices (centered on ethmoid, frontal sinus and Left Semi-Circular Canal). Predictive values were calculated for all combinations of number of readers and slices, and the optimal compromise, termed IDScore, was selected. For validation, we included PMCTs performed between 2018 and 2022 with available comparative head AMCTs (n = 27). For each PMCT, 5 comparison procedures were performed: 1 concordant (with corresponding AMCT) and 4 discordant (with randomly selected AMCTs). Two radiologists evaluated similarity of ethmoid and frontal CT slices with a score ranging from -2 to + 2. IDScore was defined as the sum of these slice scores, averaged between the two readers. In the 135 comparison procedures, IDScore using predetermined thresholds (positive identification for IDScore >  + 2, negative identification for IDScore < -1) allowed a perfect discrimination between identical subjects (Sensitivity = 100%, Specificity = 100%). IDScore could be used for remote identification of a subject with no need to access to the complete raw AMCT images, hence helping to overcome ethical and regulatory issues to access AMCT of a suspected identity.Trial registration: F20220729161623 on Health Data Hub, registered on 29 July 2022.

Background: Stress hormones like catecholamine and cortisol are thought to reflect the magnitude of physical stress in adults and were studied in relationship to the cause of death and agony time. Intrauterine distress, intrapartum events, and modes of delivery can affect the fetal endocrine stress response, as reflected by biochemical analyses. The aim of the present study was to evaluate the role of catecholamines and cortisol as markers of ante-mortem fetal distress. The role of cortisol as a marker of circadian timing of delivery was also assessed.

Methods: A 2-year prospective cohort-comparison inclusion of stillbirths and newborns took place with collection of antemortem data, labor parameters, neonatal outcome, post-mortem data and blood samples. Stillbirths were classified as acute or chronic on the basis of a multidisciplinary evaluation. Heart blood of stillbirths and cord blood of newborns were analyzed by high pressure liquid chromatography (HPLC) for adrenaline and noradrenaline and by immunoassay for cortisol determination.

Results: Fifteen stillbirths and 46 newborns, as a comparison group, delivered by spontaneous vaginal birth, elective, and emergency cesarean sections were included. Stillbirths' main cause of death was cord thrombosis. Levels of adrenaline and noradrenaline (median: 14,188 pg/ml and 230.5 pg/ml, respectively) were significantly higher (p < 0.001) in stillbirths than in newborns and were also higher in acute compared to chronic distress. Cortisol levels were significantly higher (p < 0.05) in spontaneous vaginal delivery (median: 18.2 μg/dl) compared to elective cesarean sections (median: 3.8 μg/dl). No difference in cortisol concentrations was detected between newborns delivered at morning and at afternoon/evening.

Conclusion: Our results suggest that the biochemical measurement of adrenaline and noradrenaline levels might reflect a marked physical stress response during the process of death in stillbirths. On the contrary, the elevation of cortisol levels could mirror the elevation in maternal cortisol level during vaginal delivery. For the post-mortem evaluation of stillbirths, the analysis of CA levels could provide additional data on the duration of distress, useful to integrate the forensic diagnosis.

The authors present the case of a 58-year-old man found hanging from a radiator by his shoelaces. The time of death was approximately 6 h before the body was discovered. An autopsy was performed approximately 24 h after the body was found, which revealed hemorrhages in the thoracic aorta at the junctions of the posterior intercostal arteries. Before autopsy, a routine whole-body CT scan was performed. Histologic examination of the aorta and the posterior intercostal arteries revealed a fresh hemorrhage into the tunica adventitia of the aorta. To our knowledge, there is no case description of such findings in hanged persons in the literature. Conclusion: Hemorrhages into the tunica adventitia of the junction of the posterior costal arteries may occur in association with suicidal hanging. The significance of these hemorrhages as a sign of vitality may be debated.

In the field of forensic anthropology, researchers aim to identify anonymous human remains and determine the cause and circumstances of death from skeletonized human remains. Sex determination is a fundamental step of this procedure because it influences the estimation of other traits, such as age and stature. Pelvic bones are especially dimorphic, and are thus the most useful bones for sex identification. Sex estimation methods are usually based on morphologic traits, measurements, or landmarks on the bones. However, these methods are time-consuming and can be subject to inter- or intra-observer bias. Sex determination can be done using dry bones or CT scans. Recently, artificial neural networks (ANN) have attracted attention in forensic anthropology. Here we tested a fully automated and data-driven machine learning method for sex estimation using CT-scan reconstructions of coxal bones. We studied 580 CT scans of living individuals. Sex was predicted by two networks trained on an independent sample: a disentangled variational auto-encoder (DVAE) alone, and the same DVAE associated with another classifier (C_recon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + C_recon showed an accuracy of 99.8%. Sensibility and precision were also high for both sexes. These results are better than those reported from previous studies. These data-driven algorithms are easy to implement, since the pre-processing step is also entirely automatic. Fully automated methods save time, as it only takes a few minutes to pre-process the images and predict sex, and does not require strong experience in forensic anthropology.