Forensic Science, Medicine and Pathology最新文献

{"title":"Clinicopathological features of fatal mushroom poisoning: a 10-year retrospective autopsy-based study.","authors":"Mehmet Dogan, Omer Faruk Simseker, Ferah Karayel, Ibrahim Uzun","doi":"10.1007/s12024-025-01050-3","DOIUrl":"https://doi.org/10.1007/s12024-025-01050-3","url":null,"abstract":"<p><strong>Purpose: </strong>Mushroom poisoning is a persistent public health concern with significant mortality, particularly in regions where wild mushroom foraging is a cultural practice. This study aimed to characterize the clinicopathological features of fatal mushroom poisonings through a 10-year retrospective autopsy-based review from Türkiye.</p><p><strong>Methods: </strong>We reviewed 32 fatal cases of mushroom poisoning investigated by the Council of Forensic Medicine between 2013 and 2022. Cases were analyzed for demographic patterns, seasonal and geographic distribution, clinical presentation, autopsy findings, and histopathological features. Data were extracted from forensic reports, toxicology, and histology records, and evaluated statistically.</p><p><strong>Results: </strong>Victims ranged from 2 to 82 years, with a bimodal age distribution affecting children and elderly adults. The majority (81%) occurred in rural areas and during autumn (53%). Most patients presented with gastrointestinal symptoms; 47% required mechanical ventilation and 19% were evaluated for liver transplantation. Histologically, 53% showed hepatic necrosis, with massive or submassive patterns observed in 34% of cases, consistent with amatoxin-induced injury. Acute tubular necrosis was present in 25%, and disseminated intravascular coagulation was noted in 19%. Autolysis occasionally limited interpretation, characteristic features of amatoxin toxicity were consistently documented.</p><p><strong>Conclusions: </strong>This study provides one of the most comprehensive autopsy-based analyses of fatal mushroom poisonings in Türkiye. The findings highlight the importance of early recognition, preventive interventions targeting high-risk populations and seasons, and the critical role of forensic investigation in establishing cause of death. Multidisciplinary strategies integrating clinical, forensic, and toxicological data are essential to reducing mushroom-related mortality.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144706934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advances in postmortem interval estimation: A systematic review of machine learning and metabolomics across various tissue types.","authors":"Abdulkreem Abdullah AlJuhani, Rodan Mahmoud Desoky, Abdulaziz A Binshalhoub, Mohammed Jamaan Alzahrani, Mofareh Shubban Alraythi, Farouq Faisal Alzahrani","doi":"10.1007/s12024-025-01026-3","DOIUrl":"https://doi.org/10.1007/s12024-025-01026-3","url":null,"abstract":"<p><strong>Background: </strong>Traditional postmortem interval (PMI) estimation methods rely on observable changes such as rigor mortis, livor mortis, and algor mortis but are often affected by environmental factors. Metabolomics, combined with techniques like nuclear magnetic resonance (NMR) and mass spectrometry, improves accuracy by identifying biochemical changes postmortem. Machine learning methods such as Principal Component Analysis (PCA), Partial Least Squares (PLS), and Support Vector Machines (SVMs), enhance PMI predictions by analyzing metabolite data. This review aims to summarize advances in using machine learning for PMI estimation and identify the optimal combination of tissue samples and algorithms for accurate predictions.</p><p><strong>Methods: </strong>We retrieved relevant articles up to September 2024 from PubMed, Scopus, Web of Science, IEEE, and Cochrane Library. Data were extracted from eligible studies by two independent reviewers. This included the number and species of subjects, tissue sample used, PMI range in the study, metabolic profiling technique, machine learning algorithms, potential PMI markers, and model performance.</p><p><strong>Results: </strong>We compared machine learning models for PMI estimation across various tissues. Zhang et al. (2022) had the best performance with a random forest (RF) model using cardiac blood, achieving a mean absolute error (MAE) of 1.067 h by selecting key metabolites. Wu et al. (2017) followed with an orthogonal signal-corrected PLS model (R² > 0.99, MAE 1.18-2.37 h). Lu et al. (2022) achieved 93% accuracy with a multi-organ stacking model. Other promising models include Zhang et al.'s (2017) nu-SVM on pericardial fluid (RMSE = 2.38 h) and Sato et al.'s (2015) PLS model on cardiac blood (MAE = 5.73 h).</p><p><strong>Conclusion: </strong>Cardiac blood is best for short PMIs with random forest models, while skeletal muscle and stacking models excel for longer PMIs. Future studies should refine and validate these findings as well as extend the findings to human subjects.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Black esophagus in forensic autopsies: impressive finding and cause of death?","authors":"Bianca Beltrame, Stefan Pittner, Thomas Keller, Fabio C Monticelli","doi":"10.1007/s12024-025-01048-x","DOIUrl":"https://doi.org/10.1007/s12024-025-01048-x","url":null,"abstract":"<p><p>Black esophagus (BE) is characterized by a discoloration of the esophageal mucosa, commonly arising from acute esophageal necrosis. The underlying pathogenesis of BE is poorly understood though it is frequently associated with comorbidities, such as diabetes mellitus, alcohol abuse, infections. Determining the cause of death in cases involving BE at autopsy can be particularly challenging. The report presents the case of a 45-year-old man with a history of alcohol abuse. Autopsy revealed extensive BE along with bilateral pneumonia. Cause of death was determined to be severe pneumonia in combination with acute esophageal necrosis, against a background of chronic alcohol abuse. This case underscores the importance of a thorough forensic investigation, including anamnestic information, autopsy findings and histopathological examination, in order to accurately establish the cause of death, even in presence of dramatic findings such as BE.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heteropaternal superfecundation in dizygotic twins: a case report and literature review.","authors":"Yanina Timasheva, Ilsiyar Tuktarova","doi":"10.1007/s12024-025-01046-z","DOIUrl":"https://doi.org/10.1007/s12024-025-01046-z","url":null,"abstract":"<p><strong>Purpose: </strong>Superfecundation, the fertilization of two oocytes by different spermatozoa within the same ovulatory cycle, can result in monopaternal or heteropaternal dizygotic twins. While monopaternal superfecundation is more common, heteropaternal superfecundation is rare and typically seen in disputed paternity cases. This study presents a case of heteropaternal superfecundation confirmed through forensic DNA analysis and reviews its occurrence in existing literature.</p><p><strong>Methods: </strong>A forensic investigation was conducted in a court-ordered paternity case involving dizygotic twins, their mother, and an alleged father. Buccal swab samples were collected and analyzed using multiplex amplification of 19 STR markers and the amelogenin locus. A second DNA test confirmed the results. Additionally, a dataset of 2,679 paternity tests over 10 years was examined to estimate paternity exclusion rates in twin cases.</p><p><strong>Results: </strong>Genetic analysis confirmed the alleged father's paternity of twin 1 but not twin 2, with 14 out of 19 STR loci showing absent alleles in twin 2. The 10-year dataset showed 553 paternity exclusions (20.64% of cases), with 31 involving twins, of which one case (3.23%) was identified as heteropaternal superfecundation. No significant difference was found between paternity exclusion rates in twin and non-twin cases.</p><p><strong>Conclusions: </strong>This case underscores the value of forensic genetic testing in detecting heteropaternal superfecundation, a rare occurrence with legal and social implications. Advances in DNA analysis may lead to more frequent identification of such cases.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.","authors":"Koo Young Hoi, Sang-Seob Lee, Harin Cheong, Byeongcheol Yoo, Joohwan Jeon","doi":"10.1007/s12024-025-01045-0","DOIUrl":"https://doi.org/10.1007/s12024-025-01045-0","url":null,"abstract":"<p><p>A diagnosis of atherosclerotic cardiovascular disease is critical importance in forensic medicine, particularly because severe atherosclerosis is known to be associated with a high risk of sudden death. In South Korea, the assessment of coronary atherosclerosis during autopsy largely depends on the forensic pathologist's visual measurements, which may limit diagnostic accuracy. The objective of this study was to develop a deep learning algorithm for rapid and precise assessment of coronary atherosclerosis and to identify factors influencing the model's prediction of atherosclerosis severity. A total of 3,717 digital photographs were retrospectively extracted from a database of 1,920 forensic autopsies, with one image each selected for the left anterior descending coronary artery and the right coronary artery. The deep learning algorithm developed in this study demonstrated a high level of agreement (0.988, 95% CI: 0.985-0.990) and absolute agreement (0.986, 95% CI: 0.978-0.991) between predicted and ground truth atherosclerosis values on the test set. The model demonstrated strong overall performance on the test set, achieving a weighted F1-score of 0.904. However, the class-wise F1-scores were 0.957 for mild, 0.785 for moderate, and 0.876 for severe grades, indicating that performance was lowest for the moderate grade. Additionally, decomposition, stent implantation, and thrombi did not have a statistically significant impact on coronary atherosclerosis assessment except for calcification. Although enhancing model performance for moderate grades remains a challenge, this study's findings demonstrate the potential of artificial intelligence as a practical tool for assessing coronary atherosclerosis in autopsy photographs.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Colorimetric methods using gold nanoparticles for forensic investigations.","authors":"S D Anudevi, K Kumar Ebenezar, Shoba Narayan","doi":"10.1007/s12024-025-01049-w","DOIUrl":"https://doi.org/10.1007/s12024-025-01049-w","url":null,"abstract":"","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144667440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and challenges in age estimation in adults: a scoping review of anthropological, dental, biochemical, and molecular methods.","authors":"J Rojas-Torres, L Martínez-Durán, J M de Anta, C Bucchi, G M Fonseca, L A Salazar","doi":"10.1007/s12024-025-01041-4","DOIUrl":"https://doi.org/10.1007/s12024-025-01041-4","url":null,"abstract":"<p><p>Age estimation is crucial in forensic sciences for victim identification, migration studies, and bioarchaeology. In subadults, it is based on maturational changes, while in adults, it relies on degenerative processes, reducing accuracy. Traditional methods, such as anthropological and dental approaches, are widely used, but recent advances in biochemical and molecular biology (BMB) have introduced epigenetic and biochemical analyses. Given the variability in biological aging, it is essential to evaluate and compare these methods for more precise and reproducible results. This article is a scoping review analyzing the accuracy of anthropological, dental, and BMB methods for estimating age in living individuals, cadavers, and adult skeletal remains. A scoping review following PRISMA-ScR guidelines was conducted in PubMed, Scopus, and WOS, covering studies from 2015 to 2024. Articles applying regression models for age estimation and reporting error metrics were included, while reviews and studies without precision data were excluded. Anthropological methods analyze degenerative changes in bone structures, such as the pubic symphysis and acetabulum, with error margins of 4-25 years. Forensic dentistry uses pulp-to-tooth ratios and secondary dentin deposition, yielding mean errors of 2.5-12.5 years. BMB methods, such as DNA methylation, telomere shortening, and aspartic acid racemization, offer accuracies of ± 3 to ± 10 years but require specialized equipment. Artificial intelligence enhances reproducibility, although standardization challenges remain. Age estimation in adults, particularly those over 40, remains challenging. Validating traditional methods, integrating AI, and applying multivariate molecular models can improve accuracy. A multidisciplinary approach is essential for forensic applications.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characteristics, mechanisms, and medicolegal perspectives of fatal cardiothoracic injuries in a tertiary care center.","authors":"Asmaa Fady Sharif, Nadia Ezzat Helal, Mai Mohammed Mahran, Heba Ibrahim Lashin","doi":"10.1007/s12024-025-01047-y","DOIUrl":"https://doi.org/10.1007/s12024-025-01047-y","url":null,"abstract":"<p><p>This study aims to investigate the medicolegal aspects of cardiothoracic trauma, including injury patterns, mechanism of infliction, survival time, predictors, and mechanism of mortality. A prospective cohort study included 229 cardiothoracic traumatized patients admitted to the Emergency Departments, who were categorized into patients with fatal or nonfatal injuries. Males constituted more than seven times the females with a 22.3% mortality rate. Intentional, self-inflicted injuries, road traffic accidents (RTAs), and falls from height (FFH) were significantly associated with a higher mortality There was a significant association between mortality and the presence of abrasions, large subcutaneous hematomas, and myocardial, pericardial, and cardiac chamber injuries. Additionally, diaphragmatic injuries, hemothorax, hemopericardium, head injuries (except extradural hemorrhage), and liver injuries were significantly more prevalent in fatal cases (p < 0.05). The injury severity score was significantly higher in fatal than nonfatal injuries (75 versus 29). Hemorrhage and respiratory failure constituted the primary mechanisms of death in 81.8% of trauma induced by sharp weapons and 52.6% of victims involved in RTAs, respectively. Mechanism of death in FFH varied between hemorrhagic shock (55.3%), cerebral injury (35.3%), respiratory failure (17.6%), and spinal cord injury (11.8%). A proposed mortality predictive model including diaphragmatic injury, hemopericardium, self-inflicted injuries, clavicular fractures, hemothorax, subdural and subarachnoid hemorrhage, and facial injuries explained 75.1% of variances in the probability of mortality. This study provides physicians with more knowledge about the predictors of mortality in cardiothoracic traumatized patients, helping to identify high-risk patients, prevent trauma-related deaths, and solve any related medicolegal issues.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144658822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unexpected survival of neonates after attempted hypothermic infantocide.","authors":"Ian J Cohen","doi":"10.1007/s12024-025-01021-8","DOIUrl":"https://doi.org/10.1007/s12024-025-01021-8","url":null,"abstract":"","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144636602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dental age estimation by comparing Demirjian's method and machine learning in Southeast Brazilian youth.","authors":"Allan Abuabara, Thais Vilalba Paniagua Machado do Nascimento, Kaliane Rodrigues da Cruz, Erika Calvano Küchler, Isabela Ribeiro Madalena, Maria Beatriz Carvalho Ribeiro de Oliveira, César Penazzo Lepri, Maria Angélica Hueb de Menezes-Oliveira, Cristiano Miranda de Araujo, Flares Baratto-Filho","doi":"10.1007/s12024-025-01042-3","DOIUrl":"https://doi.org/10.1007/s12024-025-01042-3","url":null,"abstract":"<p><p>This study evaluated the applicability of combining Demirjian's method with machine learning algorithms to estimate the chronological age of children and adolescents from southeastern Brazil, using dental development stages as predictive variables. A retrospective study was conducted using 610 digital panoramic radiographs of children and adolescents. Demirjian's method was applied to classify the permanent mandibular teeth into eight developmental stages. Eight machine learning models-Linear Regression, Gradient Boosting Regressor, K-Nearest Neighbors Regressor, Support Vector Regression, Multilayer Perceptron Regressor, Decision Tree Regressor, Random Forest Regressor, and AdaBoost Regressor-were trained and evaluated using five-fold cross-validation. Model accuracy was compared to the traditional method using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R²). Paired Student's t-tests were used to compare actual chronological age with predicted age estimates, and bootstrapping with 1,000 iterations was performed to calculate 95% confidence intervals (CI95%). Machine learning-based models achieved predictive errors of less than 1.5 years. The Gradient Boosting and Random Forest models demonstrated the highest performance, with an MAE of 0.75 (95% CI: [0.66-0.85]) and an RMSE of 0.92 (95% CI: [0.81-1.05]), representing a 44.03% reduction in MAE and a 43.56% reduction in RMSE compared to Demirjian's method (MAE = 1.34, RMSE = 1.63). Integrating machine learning with Demirjian's method improved the accuracy of dental age estimation, reducing errors and enhancing the reliability of the approach. The application of artificial intelligence reduces the mean absolute error of the dental age estimation method. This approach can optimize diagnoses and assist in both clinical and forensic settings, providing a more precise and adaptable tool for diverse populations.</p>","PeriodicalId":12449,"journal":{"name":"Forensic Science, Medicine and Pathology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144607959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}