Hiding the evidence: Preliminary investigation of heat-induced alterations to pre-existing saw mark trauma.

Dismemberment and subsequent burning are common methods employed in an attempt to conceal or destroy evidence. While kerf characteristics can be utilised to identify tool(s) used for dismemberment, further research is necessary to assess the effect of burning on these characteristics. In this study, a back (tenon) saw (13 teeth per inch) was used to manually inflict trauma on Ovis aries de-fleshed femur bones (n = 18). Three different cut marks (shallow false start, incomplete cut and complete transection) were made on the mid-shaft of each bone. Subsequently, the bones were burned for 20 minutes in a muffle furnace. Three burn temperatures were assessed: 400 °C, 600 °C and 800 °C. Saw mark characteristics of each cut type were assessed and compared pre- and post-burning. All pre-existing trauma was recognisable post-burning; however, metric and morphological alterations were apparent. An increase in kerf width was observed at 600 °C in false start lesions and 800 °C in incomplete cuts. Breakaway spur thickness decreased post-burning (at 400 °C and 800 °C) but length was not significantly affected. Mean inter-striation distance decreased post burning at all temperature groups. Saw marks were distinguishable from heat-related fractures across all temperature groups. One false start lesion was obliterated at 800 °C. Exit chipping, pull-out striae as well as striation regularity appeared to be more enhanced after heat exposure. These alterations indicate a temperature-dependent impact on these characteristics. Further research is necessary to assess the role of burn duration.

Nonmetric sex estimation in a contemporary Indonesian population: a validation study using clinical pelvic MSCT scans.

Klales et al. (2012) is a popular standard for the estimation of skeletal sex. Since its publication, a number of studies have demonstrated that population-specific applications of Klales improve classification accuracy. However, it has also been shown that age appears to affect the expression of dimorphism in the pelvis across the lifespan. As such, the present study examines the accuracy of Klales, and the modified global standard of Kenyhercz et al. (2017), in a contemporary Indonesian population, including quantifying the effect of age. Pelvic multi-slice CT scans of 378 individuals (213 female; 165 male) were analysed in OsiriX®. Both standards were tested and Indonesian-specific models thereafter derived.When applied to the Indonesian sample, both the Klales and Kenyhercz standards resulted in lower classification accuracy relative to the original studies. In considering the Indonesian-specific models, the ventral arc was the most accurate for the classification of sex, at 93.3% with a - 3.0% sex bias. The accuracy of the three-trait model was 94.4%, with a - 5.5% sex bias. Age was shown to significantly affect the distribution of pelvic trait scores. As such, age-dependent models were also derived, with the standard for individuals between 30 and 49 years the most accurate, at 93.1% and a sex bias of - 4.0%. Accuracy was lower in individuals aged ≥ 50 years, at 91.3% and a sex bias of 4.1%. These findings support the importance of establishing population-specific standards and to facilitate improved accuracy and capabilities for forensic practitioners in Indonesia.

Sex estimation from coxal bones using deep learning in a population balanced by sex and age.

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 (Crecon). The DVAE alone exhibited an accuracy of 97.9%, and the DVAE + Crecon 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.

Effect of hot water maceration, rehydration, and soft tissue presence on 3D geometry of bone.

PURPOSE: In forensic medicine, maceration is often essential for examining bone surfaces, serving purposes such as identifying cut marks, making geometric measurements, and determining the victim's age. While hot water maceration removes soft tissue effectively, it is known to cause bone surface shrinkage. This raises the question of whether this effect is permanent or if it can be partially reversed through rehydration, considering the presence of soft tissue. METHODS: Computed tomography (CT) scans were conducted on the radii of 20 paired human anatomic forearm specimens. Subsequently, the radii were extracted, macerated in 60 °C water, CT-scanned in an air environment, rehydrated, re-implanted into the forearms, and CT-scanned again. RESULTS: Maceration resulted in a mean shrinkage of 0.12 mm on the outer bone surface. This shrinkage was nearly fully recoverable for the diaphysis after rehydration and accounting for soft tissue surrounding the bone. In contrast, the epiphysis showed permanent shrinkage, likely due to the loss of small bone fragments. Analysis of the inner bone surface indicated a smaller effect, but with significant standard deviations, especially for the epiphysis, possibly related to the less well-defined nature of the inner bone surface. CONCLUSION: The epiphyseal surface of hot water-macerated bone will, on average, be approximately 0.15 mm deflated and cannot retain the original surface. On the other hand, the diaphyseal surface is less affected and can be nearly completely restored after rehydration and accounting for soft tissue surrounding the bone.

A review of the contributions of forensic archaeology and anthropology to the process of disaster victim identification.

Forensic archaeology and anthropology have developed significantly over past decades and now provide considerable assistance to the investigation process of disaster victim recovery and identification. In what are often chaotic death and crime scenes, the formal process of utilizing archaeological methods can bring control, order, and ensure systematic search. Procedures assist in defining scene extent, locating victims and evidence, rule out areas for consideration, and provide standardized recording and quality assurance through dedicated use of standardized forms (pro formas). Combined archaeological and anthropological search methods maximize opportunities to recovery the missing through identifying remains, mapping distributions, and providing accounting of victims at the scene. Anthropological assistance in examinations contributes to individual assessment, resolving commingling and fragmentation issues, and utilizing DNA sampling methods and matching data to reassociate and account for the missing. Utilization of archaeology, anthropology, and DNA matching data provides scope to review crime scene recovery and determine requirements and potential for further survey and retrieval. Adopting the most suitable methods for a particular context can maximize recovery, efficiency, and resource use. Case studies demonstrate the utility of archaeological methods in a range of scenarios. They exemplify the success of multidisciplinary analysis in providing evidence of the sequence of events, the timing of events, the impact of taphonomic processes, the location and accounting of victims, and the demonstration of systematic scene search. The considerations provided in this article, utilizing archaeology and anthropology processes, may assist investigators in planning and implementing responses to mass fatalities.

Disaster victim identification: Stable isotope analysis and the identification of unknown decedents.

Within the complex world of disaster victim identification, or DVI, forensic science practitioners use a variety of investigative techniques to work toward a common goal: identification of the decedents, bringing closure to the affected communities. Identification is a complex undertaking; the event (disaster) also can be extraordinarily complex, as it may be an acute event, or one that spans months or years. Compounding this time issue, remains may be heavily fragmented, dispersed, commingled, or otherwise disrupted by either the perpetrators or the disaster itself. To help solve these complexities, we explore the use of stable isotope analysis (SIA) in DVI events. SIA can be used with a variety of body tissues (hair, nail, bone, and teeth), and each represents different time depths in a decedent's life. Bone collagen and tooth enamel carbonate are useful to reconstruct an individual's diet and source water intakes, respectively, leading to likely population or geographic origin determinations. Additionally, the carbon and nitrogen isotopic signatures of bone collagen have calculated intraperson ranges. These facts allow investigators to determine likely origin of remains using isotopic data and can be used to link skeletal elements (to an individual), or perhaps more importantly, show that remains are not linked. Application of SIA can thus speed remains identification by eliminating individuals from short lists for identification, linking or decoupling remains, and reducing the need for some DNA testing. These strategies and hypothesis tests should commence early in the DVI process to achieve maximum effectiveness.

Exploratory analysis of new craniometric measures for the investigation of biological sex using open-access statistical and machine-learning tools on a cone-beam computed tomography sample.

Investigation of the biological sex of human remains is a crucial aspect of physical anthropology. However, due to varying states of skeletal preservation, multiple approaches and structures of interest need to be explored. This research aims to investigate the potential use of distances between bifrontal breadth (FMB), infraorbital foramina distance (IOD), nasal breadth (NLB), inter-canine width (ICD), and distance between mental foramina (MFD) for combined sex prediction through traditional statistical methods and through open-access machine-learning tools. Ethical approval was obtained from the ethics committee, and out of 100 cone beam computed tomography (CBCT) scans, 54 individuals were selected with all the points visible. Ten extra exams were chosen to test the predictors developed from the learning sample. Descriptive analysis of measurements, standard deviation, and standard error were obtained. T-student and Mann-Whitney tests were utilized to assess the sex differences within the variables. A logistic regression equation was developed and tested for the investigation of the biological sex as well as decision trees, random forest, and artificial neural networks machine-learning models. The results indicate a strong correlation between the measurements and the sex of individuals. When combined, the measurements were able to predict sex using a regression formula or machine learning based models which can be exported and added to software or webpages. Considering the methods, the estimations showed an accuracy rate superior to 80% for males and 82% for females. All skulls in the test sample were accurately predicted by both statistical and machine-learning models. This exploratory study successfully established a correlation between facial measurements and the sex of individuals, validating the prediction potential of machine learning, augmenting the investigative tools available to experts with a high differentiation potential.

Research Progress on Dental Age Estimation Based on MRI Technology.

Dental age estimation is a crucial aspect and one of the ways to accomplish forensic age estimation, and imaging technology is an important technique for dental age estimation. In recent years, some studies have preliminarily confirmed the feasibility of magnetic resonance imaging (MRI) in evaluating dental development, providing a new perspective and possibility for the evaluation of dental development, suggesting that MRI is expected to be a safer and more accurate tool for dental age estimation. However, further research is essential to verify its accuracy and feasibility. This article reviews the current state, challenges and limitations of MRI in dental development and age estimation, offering reference for the research of dental age assessment based on MRI technology.

Application of Medical Statistical and Machine Learning Methods in the Age Estimation of Living Individuals.

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Age Estimation by Machine Learning and CT-Multiplanar Reformation of Cranial Sutures in Northern Chinese Han Adults.

OBJECTIVES: To establish age estimation models of northern Chinese Han adults using cranial suture images obtained by CT and multiplanar reformation (MPR), and to explore the applicability of cranial suture closure rule in age estimation of northern Chinese Han population. METHODS: The head CT samples of 132 northern Chinese Han adults aged 29-80 years were retrospectively collected. Volume reconstruction (VR) and MPR were performed on the skull, and 160 cranial suture tomography images were generated for each sample. Then the MPR images of cranial sutures were scored according to the closure grading criteria, and the mean closure grades of sagittal suture, coronal sutures (both left and right) and lambdoid sutures (both left and right) were calculated respectively. Finally taking the above grades as independent variables, the linear regression model and four machine learning models for age estimation (gradient boosting regression, support vector regression, decision tree regression and Bayesian ridge regression) were established for northern Chinese Han adults age estimation. The accuracy of each model was evaluated. RESULTS: Each cranial suture closure grade was positively correlated with age and the correlation of sagittal suture was the highest. All four machine learning models had higher age estimation accuracy than linear regression model. The support vector regression model had the highest accuracy among the machine learning models with a mean absolute error of 9.542 years. CONCLUSIONS: The combination of skull CT-MPR and machine learning model can be used for age estimation in northern Chinese Han adults, but it is still necessary to combine with other adult age estimation indicators in forensic practice.

Dental Age Estimation in Northern Chinese Han Children and Adolescents Using Demirjian's Method Combined with Machine Learning Algorithms.

OBJECTIVES: To investigate the application value of combining the Demirjian's method with machine learning algorithms for dental age estimation in northern Chinese Han children and adolescents. METHODS: Oral panoramic images of 10 256 Han individuals aged 5 to 24 years in northern China were collected. The development of eight permanent teeth in the left mandibular was classified into different stages using the Demirjian's method. Various machine learning algorithms, including support vector regression (SVR), gradient boosting regression (GBR), linear regression (LR), random forest regression (RFR), and decision tree regression (DTR) were employed. Age estimation models were constructed based on total, female, and male samples respectively using these algorithms. The fitting performance of different machine learning algorithms in these three groups was evaluated. RESULTS: SVR demonstrated superior estimation efficiency among all machine learning models in both total and female samples, while GBR showed the best performance in male samples. The mean absolute error (MAE) of the optimal age estimation model was 1.246 3, 1.281 8 and 1.153 8 years in the total, female and male samples, respectively. The optimal age estimation model exhibited varying levels of accuracy across different age ranges, which provided relatively accurate age estimations in individuals under 18 years old. CONCLUSIONS: The machine learning model developed in this study exhibits good age estimation efficiency in northern Chinese Han children and adolescents. However, its performance is not ideal when applied to adult population. To improve the accuracy in age estimation, the other variables can be considered.

Adults Ischium Age Estimation Based on Deep Learning and 3D CT Reconstruction.

OBJECTIVES: To develop a deep learning model for automated age estimation based on 3D CT reconstructed images of Han population in western China, and evaluate its feasibility and reliability. METHODS: The retrospective pelvic CT imaging data of 1 200 samples (600 males and 600 females) aged 20.0 to 80.0 years in western China were collected and reconstructed into 3D virtual bone models. The images of the ischial tuberosity feature region were extracted to create sex-specific and left/right site-specific sample libraries. Using the ResNet34 model, 500 samples of different sexes were randomly selected as training and verification set, the remaining samples were used as testing set. Initialization and transfer learning were used to train images that distinguish sex and left/right site. Mean absolute error (MAE) and root mean square error (RMSE) were used as primary indicators to evaluate the model. RESULTS: Prediction results varied between sexes, with bilateral models outperformed left/right unilateral ones, and transfer learning models showed superior performance over initial models. In the prediction results of bilateral transfer learning models, the male MAE was 7.74 years and RMSE was 9.73 years, the female MAE was 6.27 years and RMSE was 7.82 years, and the mixed sexes MAE was 6.64 years and RMSE was 8.43 years. CONCLUSIONS: The skeletal age estimation model, utilizing ischial tuberosity images of Han population in western China and employing the ResNet34 combined with transfer learning, can effectively estimate adult ischium age.

Adolescents and Children Age Estimation Using Machine Learning Based on Pulp and Tooth Volumes on CBCT Images.

OBJECTIVES: To estimate adolescents and children age using stepwise regression and machine learning methods based on the pulp and tooth volumes of the left maxillary central incisor and cuspid on cone beam computed tomography (CBCT) images, and to compare and analyze the estimation results. METHODS: A total of 498 Shanghai Han adolescents and children CBCT images of the oral and maxillofacial regions were collected. The pulp and tooth volumes of the left maxillary central incisor and cuspid were measured and calculated. Three machine learning algorithms (K-nearest neighbor, ridge regression, and decision tree) and stepwise regression were used to establish four age estimation models. The coefficient of determination, mean error, root mean square error, mean square error and mean absolute error were computed and compared. A correlation heatmap was drawn to visualize and the monotonic relationship between parameters was visually analyzed. RESULTS: The K-nearest neighbor model (R2=0.779) and the ridge regression model (R2=0.729) outperformed stepwise regression (R2=0.617), while the decision tree model (R2=0.494) showed poor fitting. The correlation heatmap demonstrated a monotonically negative correlation between age and the parameters including pulp volume, the ratio of pulp volume to hard tissue volume, and the ratio of pulp volume to tooth volume. CONCLUSIONS: Pulp volume and pulp volume proportion are closely related to age. The application of CBCT-based machine learning methods can provide more accurate age estimation results, which lays a foundation for further CBCT-based deep learning dental age estimation research.

Determining Whether an Individual is 18 Years or Older Based on the Third Molar Root Pulp Visibility in East China.

OBJECTIVES: To investigate the age-related changes of the mandibular third molar root pulp visibility in individuals in East China, and to explore the feasibility of applying this method to determine whether an individual is 18 years or older. METHODS: A total of 1 280 oral panoramic images were collected from the 15-30 years old East China population, and the mandibular third molar root pulp visibility in all oral panoramic images was evaluated using OLZE 0-3 four-stage method, and the age distribution of the samples at each stage was analyzed using descriptive statistics. RESULTS: Stages 0, 1, 2 and 3 first appeared in 16.88, 19.18, 21.91 and 25.44 years for males and in 17.47, 20.91, 22.01 and 26.01 years for females. In all samples, individuals at stages 1 to 3 were over 18 years old. CONCLUSIONS: It is feasible to determine whether an individual in East China is 18 years or older based on the mandibular third molar root pulp visibility on oral panoramic images.

Análise da performance de graduandos ingressantes e concluintes do curso de odontologia na identificação humana pela análise de fotografias do sorriso / Analysis of the performance of incoming and outgoing dental students in human identification through the analysis of smile photographs

Introdução: Os dentes são órgãos de valor inestimável para a identificação humana, uma vez que são as estruturas mais estáveis, duras e resistentes do corpo. Com a existência progressiva para o uso de câmeras digitais para captar fotografias sociais, como as "selfie", em que o foco principal está direcionado no rosto do indivíduo, estas fotografias são capazes de prover material com capacidade para resolver casos de identificação humana. Objetivo: Demonstrar a importância das fotografias do sorriso no processo de identificação humana e comparar e verificar o desempenho de graduandos em Odontologia (ingressantes e concluintes) na identificação humana por meio da análise do sorriso. Material e Métodos: Este estudo utilizou a metodologia e banco de imagens do trabalho de Silva et al. (2012), e o público alvo que avaliou as imagens, modificada. Cada aluno participante da pesquisa analisou um grupo de 4 imagens projetadas, sendo 2 de sorriso (A e B) e 2 intrabucais (C e D). Dentre as 10 fornecidas para cada grupo de imagem, somente uma é compatível com cada fotografia projetada. Resultado: Após análise dos dados, foi possível verificar que 84,6% dos alunos ingressantes (número de 26 alunos) acertaram os testes A e B, e 95,6% dos concluintes (número de 23) acertaram os testes A e B. No teste C podemos observar que 61,5% dos alunos ingressantes acertaram o teste, e 69,6% dos concluintes obtiveram acertos. E, por fim, o teste D mostrou que os ingressantes obtiveram um total de 80,8% de acertos, enquanto todos concluintes (100%) acertaram o teste D. Os parâmetros mais citados foram a coloração dos dentes, a inclinação dental, a morfologia das incisais e o alinhamento incisal. Conclusão: Nesta pesquisa, tanto os alunos ingressantes como os concluintes demostraram capacidade para analisar as fotografias do sorriso na identificação humana. Os alunos ingressantes obtiveram um número maior de erros pelo fato de ainda não terem conhecimento teórico e técnico mais aprofundado em anatomia, tornando isso uma diferença entre ingressantes e concluintes

Introduction: Teeth are invaluable organs for human identification, as they are the most stable, hard and resistant structures in the body. With the progressive use of digital cameras to capture social photographs, such as the "selfie", in which the main focus is on the individual's face, these photographs are able to provide material capable of solving human identification cases. Objective: To demonstrate the importance of smile photographs in the process of human identification and to compare and verify the performance of undergraduate dental students (beginners and graduates) in human identification using smile analysis. Material and Methods: This study used the methodology and image bank of the work by SILVA R.F. et al. (2012), and the target audience that evaluated the images, modified. Each student participating in the study analyzed a group of 4 projected images, 2 of which were smiles (A and B) and 2 intraoral (C and D). Of the 10 provided for each image group, only one was compatible with each projected photograph. Results: After analyzing the data, it was possible to see that 84.6% of the incoming students (26 students) got tests A and B right, and 95.6% of the outgoing students (23) got tests A and B right. In test C we can see that 61.5% of incoming students got the test right, and 69.6% of outgoing students got it right. And finally, test D showed that the entrants got a total of 80.8% right, while all the graduates (100%) got test D right. The most cited parameters were tooth color, tooth inclination, incisal morphology and incisal alignment. Conclusion: In this study, both new students and graduates demonstrated their ability to analyze smile photographs in human identification. The new students had a higher number of errors, due to the fact that they did not yet have in-depth theoretical and technical knowledge of anatomy, making this a difference between new students and graduates

Are computed tomography images of the mandible useful in age and sex determination? A forensic science meta-analysis.

OBJECTIVES: This meta-analysis addresses the use of mandibular computed tomography (CT) scans for age and/or sex determination in forensic science. METHODS: Six databases were searched until June 2023, using the keyword "mandible" combined with keywords related to "multislice computed tomography" (MCT) or "cone-beam computed tomography" (CBCT) and keywords related to "skeletal age determination" or "sex determination analysis." MAIN RESULTS: Among the 23 studies included, 11 used MCT and 12 used CBCT to perform forensic assessments. Age determination was the aim of a single study, sex and agedeterminations were the objective of five studies, and the other studies investigated the determination of sex only. Metaanalysis could be performed only for sex determination. CONCLUSIONS: Mandible measurements are useful in sex determination, as the bicondylar and bigonial breadth are larger in males than in females. For the mandible angle, the meta-analysis results confirm sex dimorphism in CBCT scans but not in MCT scans. For age estimation, further studies are needed to prove that the mandible hole is a reliable parameter for age estimation. PROSPERO registration number: CRD42021260967.

The influence of rehydration on decomposition in the Highveld region of South Africa-Using a pig model.

Researchers have observed that rainfall may re-initiate decomposition in desiccated tissue; however, no conclusive research-based evidence exists on the specific effects of rehydration on decomposition. Therefore, this study aimed to assess the effects of artificial rehydration on the progression of decomposition following the advanced stage of decomposition. Twelve adult pig cadavers (8 experimental; 4 controls) were placed in the central Highveld of South Africa during cooler (April-July 2021) and warmer (August-November 2021) months. Decomposition was scored approximately biweekly to obtain the total body score, and accumulated degree days (ADD) were calculated for each pig. All pig cadavers were covered by chicken wire cages with transparent tarps to control for natural rehydration and scavenging. Once the experimental pig cadavers reached a three-visit stasis in the advanced phase of decomposition, they were artificially rehydrated, and changes in the progression of decomposition between the control and experimental groups were plotted (ADD against TBS) for observation. The rehydrated experimental pig cadavers showed re-initiation of decay and insect re-colonization, while the control cadavers mainly remained in a state of stasis with insect activity ceased altogether. Greater cadaver decomposition islands and a color change post-rehydration were also noted in some experimental cadavers. This supports the need for future research on the impact of rehydration, including associated soil moisture on decomposition rates, progression, and invertebrate colonization, which will enhance our understanding of the effects these environmental factors have on the accuracy of post-mortem interval estimation.

Cranial trauma in handgun executions: Circumferential delamination defect and its relationship with contact/close-range shooting.

Skeletal evidence usually constitutes the only source of information to interpret lesion patterns that help to clarify the circumstances surrounding death. The examination and interpretation of bone trauma are essential to the application and utility of anthropology as a forensic science. When discussing the effect of gunshot wounds in bone, it becomes imperative to differentiate between short and long-distance injuries based on clear, distinct, and observable signs. To contribute to the debate, our focus is directed toward the external analysis of the so-called circumferential delamination defect (CDD) as an observable proxy for close-range shooting (≤30 cm) and contact gunshot wounds in the skull. In the context of known extrajudicial killings, in which the perpetrators used short 9 × 19 FMJ ammunition in a close-range shooting, instances of CDD have been documented. Empirical evidence reinforcing the causal relationship between CDD and close-range shootings is presented. Elements' characteristics of firearm residues were also found in remains buried for up to 30 years. Primarily, this work shows that the concentrations of gunshot residues (Pb, Ba, and Sb) resemble those observed in fresh corpses with the same gunshot wound (GSW). Moreover, the correlation observed between CDD and gunshot residues, where the likelihood of CDD increases the closer to the head and the more perpendicular the shot angle is, reinforces CDD as a pivotal discriminatory factor in the skeletal evidence of short-range or contact shot. This research contributes to the field of forensic anthropology by providing fundamental insights into the etiology of CDD and its practical application.

Sexual dimorphism of the twelfth thoracic vertebra for sex determination in the Central Thai population.

Analyzing skeletal remains is crucial for identifying individuals, and forensic anthropologists use this analysis to determine biological characteristics, particularly sex, aiding criminal investigations. Among thoracic vertebrae, the twelfth thoracic vertebra (T12) is highly sexually dimorphic in various populations. This study aims to establish a discriminant function equation (DFE) for sex determination based on T12 in the Central Thai population. A total of 15 parameters of T12 were examined in 69 bone samples (43 males and 26 females). Among the 15 parameters, 14 were significantly different between males and females. The discriminant function equation (DFE) was generated as DFE = -19.578 + 0.376(i) BDsm + 0.254(l) PW + 0.081TDm, with a cutoff value of -0.296 for males and females, showing 92.8 % accuracy. The evaluation of the DFE using 10 blind samples showed 90 % accuracy. These findings may offer an additional method for sex determination through T12, complementing the examination of other skeletal elements.

Correlations between Dental Age, Skeletal Age, and Mandibular Morphologic Index Changes in Turkish Children in Eastern Anatolia and Their Chronological Age during the Pubertal Growth Spurt Period: A Cross-Sectional Study.

In age determination, different methods aiming to obtain the closest result to chronological age have been investigated so far. The most commonly used one among these is the radiological method, which is usually used to evaluate the developmental stages of wrist bones or teeth. In our study, we assessed bone age estimations using the Gilsanz-Ratib atlas (GRA), which has recently become commonly used for children aged 9 to 15 years; evaluated the dental age, determined with Cameriere's European method; conducted morphometric measurements of the mandibular bone; and then examined their relationships with chronological age. The results of our study reveal that, in children during the puberty growth spurt, Cameriere's EU formula might have higher accuracy in estimating chronological age in younger age groups, while the GRA might be more accurate for older ages. Additionally, we conclude that of the mandibular morphometric measurements, condylar height and tangential ramus height show strong positive correlations with age. As a result, we conclude that the morphometric measurements evaluated in the present study can be used as auxiliary methods in forensic anthropology and forensic dentistry.