Journal of forensic and legal medicine最新文献

End-of-life (EOL) care for patients with serious mental illness (SMI) in forensic mental health settings remains a critical yet underexplored area of healthcare. Individuals with SMI experience significant health disparities, including reduced life expectancy due to preventable chronic illnesses. These challenges are compounded in forensic settings by legal constraints, systemic neglect, and limited access to palliative care services. This narrative review examines the unique barriers to EOL care in forensic psychiatric institutions. Key issues explored include diagnostic overshadowing, restricted patient autonomy, and the absence of integrated palliative care models. Additionally, ethical and legal dilemmas, such as involuntary treatment and advance care planning (ACP), are analysed in the context of forensic mental health. Best practices for improving EOL care in forensic psychiatric settings include the integration of multidisciplinary palliative care teams, trauma-informed approaches, and the development of hospice and alternative care models. Policy and systemic recommendations highlight the need for early palliative care consultations, legal reforms that balance patient rights with public safety, and enhanced staff training in EOL care competencies. Despite these proposed interventions, significant gaps remain in research, particularly in evaluating the effectiveness of palliative interventions in forensic settings. Addressing these gaps is crucial to ensuring forensic psychiatric patients receive compassionate, dignified, and ethically sound EOL care.

Background: Diphenhydramine (DPH), a widely available over-the-counter antihistamine, has increasingly been implicated in DFCs, including sexual assault, pediatric abuse, and homicide. In several forensic investigations, DPH was detected in up to 13% of DFCs cases, with the highest prevalence reported in South Africa (12.1%), followed by France (10.0%) and the United States (10.2%). The sedative and amnestic effects of this drug, along with how easily it can be obtained, make it a substance of concern in DFCs. Detecting the drug can be challenging, mainly when metabolism occurs quickly, and there are delays in sample collection or case reporting.

Methods: A systematic review was carried out across major scientific databases, such as PubMed, and Scopus, using relevant keywords. Additionally, reports from international bodies such as the United Nations Office on Drugs and Crime (UNODC) and the Society of Hair Testing (SoHT) were also reviewed.

Results: The review compiles case evidence of DPH misuse, details its pharmacokinetics and toxicodynamics, and evaluates detection in biological matrices (blood, urine, hair, oral fluid). Analytical approaches, including LC-MS/MS, UHPLC-MS/MS, and LC-HRMS, are discussed, along with sample extraction methods including liquid-liquid and solid-liquid extraction.

Conclusions: DPH poses unique forensic challenges due to its low detectability and legal status. Hair analysis and high-resolution techniques offer extended detection capabilities in delayed cases. Increased forensic vigilance and validated analytical workflows are essential to address the covert misuse of DPH in DFCs.

The integration of Artificial Intelligence (AI) into healthcare has revolutionized various aspects of clinical practice, including the management of medical malpractice disputes. AI-driven technologies, particularly machine learning (ML) and natural language processing (NLP), enable the automated analysis of electronic health records (EHRs) and other medical documentation, improving the efficiency, accuracy, and transparency of malpractice investigations. By systematically identifying inconsistencies, detecting patterns of errors, and evaluating compliance with clinical guidelines, AI systems offer valuable insights into potential negligence claims. This study examines the impact of AI on medical record management in malpractice disputes, addressing its role in mitigating human biases, enhancing forensic assessments, and supporting legal decision-making. AI-powered algorithms facilitate objective analysis by cross-referencing vast datasets of patient histories, diagnostic reports, and treatment protocols, thus strengthening the evidentiary basis for malpractice claims. However, despite its advantages, the use of AI in forensic and legal medicine raises significant ethical and legal concerns, including issues of accountability, data privacy, and algorithmic bias. Questions regarding liability in AI-assisted medical decision-making and the potential risk of over-reliance on automated assessments must be critically addressed. To maximize AI's benefits while minimizing risks, robust regulatory frameworks, interdisciplinary collaboration, and ethical oversight are essential. Ensuring transparency in AI-driven decision-making and safeguarding patient rights will be crucial in fostering trust in these technologies. The findings suggest that AI-assisted medical record analysis can significantly enhance dispute resolution processes by providing standardized, data-driven evaluations of malpractice claims, ultimately contributing to more equitable and efficient healthcare litigation.

Juvenile delinquency is a significant global issue that impacts individuals, families, and communities, necessitating an understanding of its underlying causes for effective intervention. This study aims to identify the demographic, educational, familial, and social factors contributing to juvenile delinquency among adolescents in Turkey, with the goal of informing comprehensive prevention and rehabilitation strategies. A prospective, interview-based study was conducted involving 225 adolescents aged 12-15 years who were referred for mental evaluation due to criminal activities. Data were collected through structured interviews by forensic medicine specialists, covering aspects such as the type of crime, educational status, family structure, prior criminal behaviors, substance use, and mental maturity examination results. The findings reveal a significant gender disparity in delinquent behaviors, with males predominantly involved in theft, physical assault, and drug trafficking, while females were more engaged in theft and drug use. Lower educational attainment was strongly linked to higher rates of delinquency, underscoring the need for improved educational support and vocational training as preventive measures. Family dynamics also played a crucial role, with children from single-parent families or those with poor parental supervision being more prone to criminal activities. Additionally, a significant association was found between the criminal history of parents and the likelihood of juveniles committing crimes, highlighting the importance of comprehensive family support programs. Substance use, particularly smoking and drug addiction, was associated with higher rates of theft, drug trafficking, and physical assault, indicating the necessity of psychological support and substance abuse treatment in addressing juvenile delinquency. The study emphasizes the importance of rehabilitative measures, including family support, education, vocational training, psychosocial support, and community involvement, to reduce offending tendencies and facilitate the reintegration of juveniles into society. This study highlights the complex interplay of factors influencing juvenile delinquency and advocates for targeted, multifaceted interventions to promote the well-being and rehabilitation of young offenders.

Determining the time passing since death, also known as post-mortem interval (PMI), is one of the primary duties of forensic medicine. However, standard methods of PMI estimation only sometimes allow for accurate determination of its value because many internal and external factors influence the estimate's accuracy. In recent years, several studies have reported using molecular biology and genetics methods in PMI estimation. Due to the promising results obtained in both animal and human models, these methods can replace existing techniques in the future and allow PMI to be determined more precisely. The presented narrative review analyzes methods of molecular biology already available and used to estimate PMI, outlines the latest reports on determining PMI using molecular methods, and summarizes research on using RNA, DNA, and other biochemical molecules for PMI estimation. Moreover, the presented review indicates limitations of using molecular methods for molecular PMI estimation that need to be solved before entering forensic practice and underlines prospects for further research in this area.

Background and objectives: In recent years, Artificial Intelligence (AI) has gained prominence as a robust tool for clinical decision-making and diagnostics, owing to its capacity to process and analyze large datasets with high accuracy. More specifically, Deep Learning, and its subclasses, have shown significant potential in image processing, including medical imaging and histological analysis. In forensic pathology, AI has been employed for the interpretation of histopathological data, identifying conditions such as myocardial infarction, traumatic injuries, and heart rhythm abnormalities. This review aims to highlight key advances in AI's role, particularly machine learning (ML) and deep learning (DL) techniques, in forensic neuropathology, with a focus on its ability to interpret instrumental and histopathological data to support professional diagnostics.

Materials and methods: A systematic review of the literature regarding applications of Artificial Intelligence in forensic neuropathology was carried out according to the Preferred Reporting Item for Systematic Review (PRISMA) standards. We selected 34 articles regarding the main applications of AI in this field, dividing them into two categories: those addressing traumatic brain injury (TBI), including intracranial hemorrhage or cerebral microbleeds, and those focusing on epilepsy and SUDEP, including brain disorders and central nervous system neoplasms capable of inducing seizure activity.

Results: In both cases, the application of AI techniques demonstrated promising results in the forensic investigation of cerebral pathology, providing a valuable computer-assisted diagnostic tool to aid in post-mortem computed tomography (PMCT) assessments of cause of death and histopathological analyses.

Conclusions: In conclusion, this paper presents a comprehensive overview of the key neuropathology areas where the application of artificial intelligence can be valuable in investigating causes of death.

In suspected sex-related homicides, special attention is paid to morphological and trace evidence suggesting a sexual assault. As far as anogenital lesions are concerned, injuries may be located externally (affecting the vulva, perineum and anus) or internally. In the latter case, the vagina, the cervix and the rectal wall can be involved. Among the findings associated with sexual homicides, mainly striking injuries such as perforations, lacerations and deep abrasions of the vagina and/or rectum are mentioned in textbooks and autopsy reports. In contrast, the presence of non-penetrating mucosal lesions such as petechiae, ecchymoses and more extensive blood extravasation is rarely noticed. Based on two exemplary cases, the macroscopic and histological appearance of haemorrhages within the vaginal and rectal mucosa are demonstrated in synopsis with concomitant signs of anogenital trauma. Locally acting shear forces followed by ruptures of small vessels are considered to be the most important injury mechanism. Contributing factors may result from special circumstances of the individual case such as the offender's modus operandi.