All Articles in july, 2025

We report the case of a 29-year-old male referred to our surgical department for evaluation of two progressively enlarging lumbar masses with an eight-month history.

Y-chromosomal DNA (Y-DNA) testing plays a critical role in forensic investigations involving male suspects, especially when traditional autosomal DNA evidence is insufficient or degraded. This review explores how different environmental factors—such as heat, moisture, Ultraviolet (UV) exposure, and microbial activity—impact the ability to recover Y-DNA from fabrics commonly found at crime scenes, including cotton, polyester, and denim. The study found that longer exposure to harsh environments, especially humidity and UV radiation, led to a sharp drop in the amount and quality of recoverable Y-DNA. The type of fabric also influenced results, with cotton generally retaining more DNA than synthetic materials like polyester. These findings reinforce the need for quick evidence collection and proper storage to preserve the integrity of Y-DNA.  Several real-world cases are highlighted where Y-DNA analysis provided clear forensic outcomes, especially when autosomal DNA failed due to issues like allelic dropout—where one or more genetic markers fail to appear during testing—or secondary transfer, which occurs when DNA is unintentionally passed from one surface or person to another. In such cases, Y-DNA profiling was crucial in narrowing down or identifying male suspects, particularly when other forms of DNA were inconclusive. This review underscores the unique value of Y-DNA analysis in situations involving degraded or limited biological material and calls for the development of better recovery techniques to improve success in challenging forensic contexts.

Hydrogen sulphide [H2S] is created when sewage material breaks down. The well-known "rotten egg" odour is linked to H2S at low quantities. It is a colourless and odourless gas that, at greater quantities, can cause cerebral and respiratory depression, fast unconsciousness, and impending death. As a sad occupational accident, worker deaths in septic tanks or sewage systems are not unusual in medico-legal practice.Death is attributed to poisoning with hazardous gases, particularly hydrogen sulphide, but the depletion of oxygen in the air due to an excess of carbon dioxide is not mentioned. Colleague fatalities are frequently linked to deaths in similar situations. Two tragic accidents that resulted in victim deaths are detailed in this paper. Here, the morphological findings—pulmonary diseases, submucosal/sub-serosal congested haemorrhage, and discolouration from postmortem staining—were discovered in both cases. A detailed scene investigation about the potential for life-threatening H2S poisoning for the assistants, the characteristic rotten egg smell that may be detected on the corpses, and the previously described morphological results should be the foundation for any suspicion of a deadly H2S poisoning. A qualitative and, if feasible, quantitative study of H2S should be performed to confirm the diagnosis.

Advanced forensic approaches are necessary to handle digital crimes, as they must provide transparent methods that foster trust and enable interpretable evidence in judicial investigations. The current black-box machine learning models deployed in traditional digital forensics tools accomplish their tasks effectively yet fail to meet legal standards for admission in court because they lack proper explainability.This study creates an Explainable Artificial Intelligence (XAI) system for digital forensics to improve flagging events as legal evidence by establishing high levels of trust and transparency. A digital evidence system employs interpretable machine learning models together with investigative analysis techniques for the detection and classification of computer-based irregularities, which generate clear explanations of the observed anomalies.The system employs three techniques, including SHAP (Shapley Additive Explanations) alongside LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to deliver understandable explanations about forensic findings, thus enhancing investigation clarity for law enforcement agents and attorneys as well as stakeholder professionals.The system performs successfully on actual digital forensic datasets, thus boosting investigation speed while minimizing false alerts and improving forensic decision explanations. The system must demonstrate GDPR and digital evidence admission framework compliance to maintain legal and ethical correctness for usage in court procedures.Forensic digital investigations need explainable Artificial Intelligence as an essential integration for creating reliable and legally sound practices.

Background: This study explored whether emotional distress, coping, and/or resilience contributed to return to driving (RTD) following experienced mild traumatic brain injury, and whether these variables of interest differed among those who had and had not RTD.  Methods: The present study evaluated de-identified archival data of 65 patients with mTBI following an MVA. Patients were either the driver, passenger, /or pedestrian struck by a motor vehicle, and aged 22 to 69 years. The sample consisted of 36 men and 29 women with an average education. The mean months elapsed between the accident and the assessment was 16.82 months. Pearson correlations were used to test for associations between all explanatory and outcome variables. Separate linear and hierarchical regressions were carried out to evaluate whether variables of interest were significant predictors of RTD.  Results: Findings revealed that the presence of depressive symptoms was associated with coping, irrespective of style, resilience, driving-related anxiety, and RTD. Moreover, RTD was related to driving-related anxiety, too, and in fact, anxiety (considering the presence of depressive symptoms) appeared to be an even greater limiting factor when considering RTD in this population. Age, gender, and education did not influence RTD. Conclusion: The present study revealed that depressive symptoms and driving-related anxiety in particular contribute to whether patients with mTBI RTD, irrespective of time since injury, age, and gender. Coping styles and resilience did not predict RTD. Further work is warranted to address the paucity of research investigating RTD parameters that contribute to and/or hinder RTD among mTBI sufferers.