Background: Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person’s sex based only on the morphometry of L1–L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine. Method: This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric. Results: The L1–L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model. Conclusions: Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person’s sex based only on the L1–L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1–L5 measurements of a body/cadaver, they can predict the person’s sex.

Congenital heart defects (CHDs) represent the most common category of congenital malformations and constitute a significant cause of infant morbidity and mortality. Despite advances in prenatal imaging, such as fetal echocardiography, early detection remains challenging, particularly in pregnancies without identified risk factors. Recent studies suggest that maternal circulating non-coding RNAs, including microRNAs and long non-coding RNAs (lncRNAs), may serve as promising non-invasive biomarkers for the prenatal diagnosis of CHDs. Following a review of the most relevant clinical and preclinical studies, it was found that maternal circulating RNA, particularly microRNAs and lncRNAs, shows potential as non-invasive biomarkers for detecting fetal congenital heart defects. Among microRNAs, miR-146a-5p demonstrated the highest diagnostic accuracy for ventricular septal defects (VSDs), while panels of lncRNAs, such as LINC00598, LINC01551, and GATA3-AS1, exhibited high performance for atrial septal defects (ASDs). In addition, miR-19b, miR-29c, and miR-375 were associated with both VSDs and ASDs, suggesting a shared role in septal development. However, the studies displayed variability in biomarker selection and analytical methodologies. The findings indicate that maternal circulating microRNAs and lncRNAs hold significant potential as non-invasive biomarkers for the early detection of CHDs. Nonetheless, methodological heterogeneity and small sample sizes highlight the need for standardized protocols and larger multicenter studies prior to clinical implementation. These observations support the future integration of RNA biomarkers with fetal echocardiography to enhance early CHD screening and to inform prenatal counseling.

Identifying skeletal remains has been and will remain a challenge for forensic doctors and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study proposes a machine learning method to determine gender starting from morphometric analysis of L1-L5 lumbar vertebrae in a modern Romanian population. The purpose of the present study was to observe whether by using the ML method there is a good predictability of gender in forensic identification based on parameters obtained from the metric analysis of the lumbar spine specific to the Romanian population. This paper offers two models of ML, RF and XGB, each with its own characteristics, and presenting different performance, random forest having the best. For both, we used two metrics (accuracy and roc_auc), the latter being the most used to highlight model performance. The L1-L5 lumbar vertebrae exhibit sexual dimorphism and can be used in gender estimation. Machine learning is more accurate in determining gender than discriminatory function analysis.

Background: The styloid process is a slender, cylindrical bony projection of the temporal bone, showing marked interindividual variability in length, orientation, and degree of ossification. Its abnormal elongation, defined as exceeding 30 mm, is often associated with Eagle’s syndrome but may also occur as an incidental anatomical variant. Objective: This study reports two rare cases of abnormally long styloid processes without ossification of the stylohyoid ligament, identified in adult dry skulls from the osteological collection of the “Olga Necrasov” Centre of Anthropological Research, Iași, Romania, and provides morphological and CT-based characterization. Materials and Methods: Both skulls were examined macroscopically and by CT, with 3D reconstructions being used for morphometric analysis. Results: In Case 1, the left SP measured 62 mm, corresponding to Langlais type I elongation, with no evidence of pseudoarticulation or ligamentous ossification. In Case 2, the left SP was elongated to 33 mm and fusiform in shape, while the contralateral (right) SP was completely absent, a highly uncommon anatomical variation scarcely reported in the literature. Both findings were confirmed by CT imaging. Conclusions: The coexistence shows that the stylohyoid complex cand vary greatly during development. Such findings expand the spectrum of known anatomical variants of the stylohyoid complex and underscore the importance of detailed morphologic and imaging evaluation of the styloid region in both anatomical and clinical contexts.

Given the increasing complexity of the modern health care environment and the increasing number of patients, reducing medical errors is a priority task for health as well as for policy and medical decision-makers.Forensic psychiatry as subspecialty of clinical psychiatry is guided by principles being used in cases with psychiatric, forensic and legal implications. In both psychiatry and any other medical specialty, malpractice may occur. The paper attempts to identify the risk factors of incorrect psychiatric diagnosis and professional misconduct in the exercise of the medical act generating harm to the patient. Knowing the risk factors inevitably leads to their avoidance.