Journal of forensic sciences最新文献

Zolpidem, a non-benzodiazepine hypnotic, is one of the most widely prescribed sleep aids in South Korea and is frequently implicated in forensic toxicology. A retrospective analysis was conducted on zolpidem-positive autopsy cases (N = 1583) investigated by the National Forensic Service between 2022 and 2024. The manner and cause of death were classified, and zolpidem concentrations were measured in three biological matrices: peripheral blood (PB), cardiac blood (HB), and liver. Zolpidem analysis was performed using gas chromatography-mass spectrometry (GC/MS) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) following solid-phase extraction (SPE). Among zolpidem-positive autopsies, suicide accounted for 43.1% (n = 676), with drug poisoning representing the dominant method (72.8% of suicides). In drug-related suicide cases (n = 251), zolpidem was the sole detected agent in 15.1%, while the majority (84.9%) involved poly-drug exposure. A wide range of non-pharmaceutical toxicants-including carbon monoxide, agrochemicals, chemical substances such as gases, nitrite, ethylene glycol, cyanide, and other miscellaneous agents-were also co-detected. Zolpidem was also the most frequently detected drug in driving under the influence of drugs (DUID) cases (30.0%) and drug-facilitated sexual assault (DFSA) submissions (5.1%) during the study period, highlighting its role in both fatal and nonfatal forensic toxicology. These findings emphasize the importance of appropriate clinical use, matrix- and context-specific toxicological assessment, and strengthened public health strategies to address the potential risks associated with zolpidem use.

In many criminal investigations, scalp hair is commonly used as physical evidence that can be used to identify the suspect. People commonly dye their hair, which requires prebleaching of virgin and previously colored hair. Surface-enhanced raman spectroscopy (SERS) is a minimally destructive, fast, and reliable technique that can detect and identify dyes present on hair. SERS is based on the enhancement provided by Raman scattering from metal nanostructures that can be drop-casted on a small fraction of hair. In this pilot study, we investigate the extent to which prebleaching of virgin and previously colored hair from one subject can alter the accuracy of SERS-based identification of permanent and semipermanent dyes. We found that prebleaching of virgin hair does not affect spectroscopic signatures of dyes applied on such hair afterwards. Prebleaching of colored hair results in only small changes in the intensity of the SERS spectra of dyes applied on it afterwards. However, these small changes do not alter the accuracy of SERS-based analysis of hair dyes. Furthermore, if prebleached, the underlying colorants are not dominant enough to affect SERS spectra of the dyes applied after bleaching, except in cases where the new dye gives weak Raman signals. These findings help to understand the robustness of SERS in the detection and identification of dyes present on hair, highlight the potential for the use of SERS in forensic identification of bleached hair and, consequently, advance the forensic analysis of hair.

The increasing sophistication of image manipulation techniques challenges traditional forensic image analysis (FIA) methods. Detecting tampered images accurately and efficiently has become crucial, particularly in sectors like law enforcement and media. The objective of this research is to enhance DenseNet architectures to improve tampered image detection by increasing accuracy, reducing processing time, and improving robustness. The approach combines advanced techniques, including Gabor-bilateral filtering (G-BF) for improved feature extraction, MS-DenseNet for multiscale feature extraction (MSFE) and attention mechanisms (AMs), and GAN-DenseNet to generate realistic features. These methodologies help address limitations in detecting subtle image tampering. Enhancing DenseNet improved tampered image detection accuracy from 85% to 95% and reduced processing time from 5 to 7 s to less than 1 s. The model also demonstrated increased robustness, making it suitable for real-world applications in forensic analysis. The future work aims to integrate advanced AMs, fine-tune GANs to enhance feature generation by 10%-15%, optimize real-time detection, and target 98% accuracy for further advancements in FIA and tampered image detection.

A significant portion of document fraud cases, often associated with organized crime, involves the manipulation of physical records using correction materials such as pen or fluids and tapes. Although these materials are visually similar, they differ in chemical composition. Fluids typically contain volatile organic solvents and pigments, while tapes consist of pigment-coated adhesive films. In this study, the ATR-FTIR spectroscopy as a non-destructive analytical technique was explored to obtain spectral data from 205 prepared samples of correction materials and visually similar substances. PCA was used for exploratory data reduction, followed by PLS-DA for supervised classification based on spectral patterns. Further, feature importance was interpreted using VIP and coefficient plots, highlighting key wavenumbers contributing to class separation. The developed models exhibited excellent performance, achieving 100% accuracy in external validation for differentiating correction materials from similar-looking substances, as well as discriminating between correction pen and correction tape samples. The findings demonstrate that ATR-FTIR spectroscopy coupled with chemometrics provides a fast, accurate, and non-destructive method for the forensic classification of correction materials, offering substantial utility in real-world document examination scenarios. Further, the spectral repository developed in this study offers a valuable reference resource for forensic document examiners, aiding in the identification of questioned materials and supporting the investigation of document forgery cases.

Bloodstain pattern types, such as wipes and swipes, are frequently encountered at crime scenes and can offer critical insight into the sequence of events. However, these pattern types can be difficult to reliably distinguish, highlighting the need for modern, objective approaches to classification that reduce the potential for human error. In this study, 50 participants were asked to classify 40 test bloodstain pattern images (20 wipes and 20 swipes). These same images were subsequently classified using Microsoft Azure Custom Vision (MACV), an artificial intelligence (AI) image recognition platform. The MACV model was trained using 5425 bloodstain pattern images, including impact, expirated, cessation cast-off, wipe, and swipe stains, across a range of background colors. At the 50th training iteration, the AI achieved 100% accuracy in classifying both wipe and swipe patterns, outperforming participants who achieved an average accuracy of 52% (47% for wipes and 57% for swipes), marking a 48% improvement in classification performance. The model was further trained to the 80th iteration using rotated images, achieving 98.75% accuracy on the rotated test set.

Protocol verification is a fit-for-purpose test of laboratory procedures. We present here verification testing of a DNA mixture interpretation protocol, following ANSI/ASB Standard 020, Standard for Validation Studies of DNA Mixtures, and Development and Verification of a Laboratory's Mixture Interpretation Protocol. The blind testing called for in the standard was performed on a set of nine DNA mixtures created with contributors unique to the verification, using a range of donor ratios (distinguishable and indistinguishable), DNA inputs (0.25-3.6 ng), and numbers of contributors (2-4). The testers ("verifiers") were given .hid files, along with limited contextual information that simulated the state of caseworker knowledge prior to PCR amplification, and they were tasked with determining contributor number and suitability for interpretation, analyzing each interpretable mixture, and generating simple likelihood ratios (LRs) and corresponding verbal predicate assignments. Although the differences observed across verifiers were within the scope of the draft mixture interpretation procedure and resulted in non-consequential differences among the calculated LRs, we found that the process led to significant improvements in training efficiency and pre-release protocol refinement. We also found that DNA mixture selection, verifier training prior to verification testing, and assessment criteria development must all be considered carefully to make the process as effective as possible, particularly in a multi-laboratory system. The planning and results summarized in this paper can serve as a template to other forensic DNA laboratories seeking to incorporate the recommendations of ANSI/ASB Standard 020 into their quality assurance systems.

Forensic anthropologists commonly estimate osteological sex using separate morphological and metric analyses, without integrating both data types into a single classification model. Combining data types into one model has the potential to increase sex classification accuracies for the skull. Therefore, the present study seeks to improve classification accuracies for the skull by combining morphological and metric variables using random forest (RF) modeling. The main objectives are (1) generate multiple RF models that incorporate various combinations of morphological and metric variables for estimating osteological sex from an unknown individual, (2) compare the performance of morphological, metric, and combined data RF models, and (3) compare the results of the RF models to current methods for osteological sex estimation of the skull. The sample included 212 European Americans (males = 106, females = 106) and 191 African Americans (males = 114, females = 77). The models were trained on 80% of the sample and tested using a 20% holdout sample. Multiple models were generated using morphological, metric, and combined variables. Across all model types, the skull and cranium models achieved higher accuracies compared to the mandible models. The morphological and combined models attained higher accuracies compared to the metric models. Additionally, the morphological and combined RF models attained comparable classification accuracies to current standard osteological sex estimation methods, as well as compared to previous studies that integrated skull measurements and traits. Future research should continue exploring RF modeling for osteological sex estimation, including models combining metric and morphological variables from multiple skeletal regions.

This study investigated links between skeletal age estimation error and lifestyle variables to better elucidate sources of interpersonal variability in the rates of skeletal aging. Skeletal age for 180 individuals from the New Mexico Decedent Image Database was estimated by applying the Suchey-Brooks method and transition analysis to 3D models of the pubic symphysis, and age estimates were compared to known age-at-death. Age estimation bias and accuracy for both methods were evaluated first with respect to single lifestyle variables, then random forest modeling was used to test variability with respect to all lifestyle variables. Age estimation bias was shown to be significantly different with respect to sex when applying transition analysis, but not when applying Suchey-Brooks, and males tended to be underaged relative to females of the same age. While no statistically significant differences in bias existed for either method between BMI categories, random forest modeling indicated that body size exerts a limited but variable influence on skeletal aging. Additional variables were highlighted as potentially influential to skeletal aging by random forests, such as socioeconomic status, but ultimately, model performance and variable importance plots demonstrated that these influences were slight and nonuniform. These data suggest that including considerations of lifestyle variables in skeletal aging methods would not improve aging estimates.

The City of Tulsa initiated The 1921 Graves Investigation in 2020 in order to recover and identify African American victims of the 1921 Tulsa Race Massacre from unmarked graves. The Tulsa Race Massacre occurred in downtown Tulsa over May 31, 1921, to June 1, 1921, as an act of mob violence, homicide, looting, and arson by white Tulsans against the African American residents of the neighboring Greenwood community. Thirty-nine deaths from both races were documented, and one of the known African American burial sites was in the city cemetery, Oaklawn. Oklahoma statutes regarding disinterment resulted in a recovery plan in which the forensic anthropology lab was established on the cemetery grounds, and lab procedures were adapted for managing fair to poor skeletal preservation, all while accommodating community support and working under media scrutiny. Collaboration between City personnel and the anthropological experts included application of traditional and novel resources to create and operationalize the field laboratory, including adapting a portable building into a functional lab, finding lightweight and opaque remains transport containers, using commonly acquired supplies for new purposes, and modifying analytical techniques for field radiography not previously observed in the literature. These customizations have resulted in the successful implementation of the field laboratory, and the analysis of skeletal remains over five episodes of recoveries and analyses of decedents, as well as a complete deconstruction of the lab at the end of each field season. We present this field laboratory and toolkit assemblage as a resource to the literature of anthropological field analysis.

Diphenhydramine (DPH) is a widely available first-generation antihistamine that, in supratherapeutic doses, can cause severe toxicity and even death. While commonly reported in cases of combined drug toxicity, fatalities attributed to DPH toxicity alone are rarely documented in forensic literature. A retrospective review was conducted of deaths attributed solely to DPH toxicity, investigated by the Cook County Medical Examiner's Office (CCMEO) between 2015 and 2024. Demographic information, postmortem findings, and circumstantial data were collected from autopsy, toxicology, and investigative reports. Twenty-five cases were included (13 males, 12 females; age range 14-55 years). Peripheral blood concentrations ranged from 3300 to 228,000 ng/ml. The most commonly observed autopsy findings included pulmonary edema and pill fragments in the gastrointestinal tract. Most cases (20 out of 25) were certified as suicides, with depression being the most frequently identified psychiatric risk factor. A comparative review of the literature (1998-2024) confirmed the wide variability of DPH peripheral blood concentration in cases of acute toxicity, ranging from 14,640 to 285,000 ng/ml. Demographic patterns and manner of death prevalence were similar to the CCMEO cases, with additional reports highlighting adolescent overdoses linked to social media challenges and intentional ingestions among socially vulnerable individuals. DPH-only toxicity should be carefully considered in postmortem investigations, especially due to the lack of specific autopsy findings and the wide range of postmortem concentrations in all matrices. Circumstantial evidence, scene findings, and reference to the available literature are essential for the accurate certification of the cause and manner of death in such cases.