Journal of forensic sciences最新文献

Falling bullets, resulting from celebratory or random gunfire, represent a predictable yet often overlooked public safety problem. Unlike classical firearm injuries, they usually lack external findings typical of close or intermediate-range gunshot wounds, which complicates diagnosis, medico-legal evaluation, and judicial processes. Children and other vulnerable groups are disproportionately affected, leading to preventable mortality and significant medico-legal and social implications. We report the case of an 11-month-old infant who died after being struck by a falling bullet during a wedding celebration. The external examination revealed only a small entrance wound without soot deposition or tattooing. At autopsy, a depressed fracture of the right parietal bone was identified. The bullet penetrated through the parietal lobe and caused fatal damage at the level of the medulla oblongata. Falling bullets lose their initial velocity and rotational stability during ascent and descent, resulting in yaw and cavitation effects upon impact. Despite relatively low kinetic energy, these mechanisms can produce devastating intracranial injuries, particularly in children with thinner and more elastic skulls. Falling bullets are entirely preventable causes of death and injury. Effective legislation, strict enforcement, community-based awareness campaigns, and multidisciplinary collaboration are essential to reduce their occurrence. This case highlights the medico-legal importance of recognizing falling bullet injuries and underscores the urgent need for public health and legal measures to prevent such tragedies.

This study monitored the transfer of inorganic and organic gunshot residues (IGSR and OGSR) in cases where indirect exposure might be questioned, such as during arrests. Mock arrest scenarios (n = 180) were created to assess the effect of two variables on the secondary transfer. First, the level of contact between the detained and the officer (low, medium, and high) during the arrest protocols. Second, the activities preceding the arrest include situations where the officer or person of interest (POI) discharges a firearm or handles a gun without firing. Additionally, measures to mitigate the risk of GSR transfer from the arresting officer and surfaces were evaluated. This included variables like hand washing, wearing various types of gloves, and bagging the POI's hands during transport in a police vehicle (n = 70). All samples underwent screening (ECD and LIBS) and confirmatory analysis (SEM-EDS and LC-MS/MS). The results indicated that IGSR and OGSR behave differently, with their transfer and persistence varying according to the level of contact, activities, and exposure prior to arrest. Secondary transfer was observed in 69% of the experiments but was less likely to occur for OGSR than for IGSR. The OGSR was more prone to loss, as it was not detected in 93% of the medium- and high-contact scenarios. Preventive measures such as hand washing, wearing nitrile gloves, or bagging hands helped decrease the characteristic IGSR particle counts from 5-80 to none-11. These findings enhance the current knowledge of IGS/OGSR transfer and persistence while providing recommendations for arrest protocols and evidence collection.

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.