Search Results (801)

This case report presents a multidisciplinary forensic investigation into a cold case involving a missing person in Italy, likely linked to a homicide that occurred in 2008. The investigation applied a standardized protocol integrating satellite imagery analysis, site reconnaissance, vegetation clearance, ground-penetrating radar (GPR), and cadaver dog (K9) deployment. A dedicated decision tree guided each phase, allowing for efficient allocation of resources and minimizing investigative delays. Although no human remains were recovered, the case demonstrates the practical utility and operational robustness of a structured, evidence-based model that supports decision-making even in the absence of positive findings. The approach highlights the relevance of “negative” results, which, when derived through scientifically validated procedures, offer substantial value by excluding burial scenarios with a high degree of reliability. This case is particularly significant in the Italian forensic context, where the adoption of standardized search protocols remains limited, especially in complex outdoor environments. The integration of geophysical, remote sensing, and canine methodologies—rooted in forensic geoarchaeology—provides a replicable framework that enhances both investigative effectiveness and the evidentiary admissibility of findings in court. The protocol illustrated in this study supports the consistent evaluation of large and morphologically complex areas, reduces the risk of interpretive error, and reinforces the transparency and scientific rigor expected in judicial settings. As such, it offers a model for improving forensic search strategies in both national and international contexts, particularly in long-standing or high-profile missing persons cases. Full article

Background/Objectives: Advancements in DNA profiling have made it possible to retrieve intact DNA profiles from increasingly minute biological samples. This increased sensitivity in DNA detection has highlighted crucial considerations to be made when handling and packing items from the crime scene to minimize potential contamination from either direct or indirect transfer of DNA. To investigate potential DNA transfer between items stored within the same evidence package, we conducted a simulation study with items commonly encountered during forensic casework. Methods: Participants were grouped in pairs, each of them handling the same type of item to simulate the activity conducted at the crime scene. The items were then collected from each pair of participants and stored in the same evidence package for 4 to 5 days. To evaluate the basal DNA transfer between items within the same package, the packed items were not subjected to friction, force, or long-distance movement in this study. Results: We have observed the occurrence of DNA transfer on 39% of the studied items inside the package, which changed the source attribution of the DNA profiles for 10% of the recovered samples. Our results showed that the types of items were associated with the number of transferred alleles and the amount of DNA recovered, while no association was found between the number of transferred alleles and the amount of DNA on the studied items. Conclusions: Taken together, the results from this study reiterate the importance of packing each item from the crime scene separately, especially when packing items together may impact the interpretation of source attribution. Full article

This study investigates the genetic diversity and population structure of Castanopsis tribuloides, a vital tree species in Asian forest ecosystems. Understanding the genetic patterns of keystone forest species provides critical insights into forest resilience and ecosystem function and informs conservation strategies. We analyzed population samples collected from three distinct locations within Doi Suthep Mountain in northern Thailand using Short Tandem Repeat (STR) markers to assess both intra- and inter-population genetic relationships. DNA was extracted from leaf samples and analyzed using a panel of polymorphic microsatellite loci specifically optimized for Castanopsis species. Statistical analyses included the assessment of forensic parameters (number of alleles, observed and expected heterozygosity, gene diversity, polymorphic information content), population differentiation metrics (G_ST), inbreeding coefficients (F_IS), and gene flow estimates (N_m). We further examined population history through bottleneck analysis using three models (IAM, SMM, and TPM) and visualized genetic relationships through principal coordinate analysis and cluster analysis. Our results revealed significant patterns of genetic structuring across the sampled populations, with genetic distance metrics showing statistically significant differentiation between certain population pairs. The PCA and cluster analyses confirmed distinct population groupings that correspond to geographic distribution patterns. These findings provide the first comprehensive assessment of C. tribuloides population genetics in this region, establishing baseline data for monitoring genetic diversity and informing conservation strategies. This research contributes to our understanding of how landscape features and ecological factors shape genetic diversity patterns in essential forest tree species, with implications for managing forest genetic resources in the face of environmental change. Full article

Narcotics trafficking is a fundamental part of organized crime, posing significant and evolving challenges for forensic investigations. Addressing these challenges requires rapid, precise, and scientifically validated analytical methods for reliable identification of illicit substances. Over the past five years, forensic drug testing has advanced considerably, improving detection of traditional drugs—such as tetrahydrocannabinol, cocaine, heroin, amphetamine-type stimulants, and lysergic acid diethylamide—as well as emerging new psychoactive substances (NPS), including synthetic cannabinoids (e.g., 5F-MDMB-PICA), cathinones (e.g., α-PVP), potent opioids (e.g., carfentanil), designer psychedelics (e.g., 25I-NBOMe), benzodiazepines (e.g., flualprazolam), and dissociatives (e.g., 3-HO-PCP). Current technologies include colorimetric assays, ambient ionization mass spectrometry, and chromatographic methods coupled with various detectors, all enhancing accuracy and precision. Vibrational spectroscopy techniques, like Raman and Fourier transform infrared spectroscopy, have become essential for non-destructive identification. Additionally, new sensors with disposable electrodes and miniaturized transducers allow ultrasensitive on-site detection of drugs and metabolites. Advanced chemometric algorithms extract maximum information from complex data, enabling faster and more reliable identifications. An important emerging trend is the adoption of green analytical methods—including direct analysis, solvent-free extraction, miniaturized instruments, and eco-friendly chromatographic processes—that reduce environmental impact without sacrificing performance. This review provides a comprehensive overview of innovations over the last five years in forensic drug analysis based on the ScienceDirect database and highlights technological trends shaping the future of forensic toxicology. Full article

This study proposes an optimized footprint recognition model based on an enhanced StarNet architecture for biometric identification in the security, medical, and criminal investigation fields. Conventional image recognition algorithms exhibit limitations in processing barefoot footprint images characterized by concentrated feature distributions and rich texture patterns. To address this, our framework integrates an improved StarNet into the backbone of YOLOv8 architecture. Leveraging the unique advantages of element-wise multiplication, the redesigned backbone efficiently maps inputs to a high-dimensional nonlinear feature space without increasing channel dimensions, achieving enhanced representational capacity with low computational latency. Subsequently, an Encoder layer facilitates feature interaction within the backbone through multi-scale feature fusion and attention mechanisms, effectively extracting rich semantic information while maintaining computational efficiency. In the feature fusion part, a feature modulation block processes multi-scale features by synergistically combining global and local information, thereby reducing redundant computations and decreasing both parameter count and computational complexity to achieve model lightweighting. Experimental evaluations on a proprietary barefoot footprint dataset demonstrate that the proposed model exhibits significant advantages in terms of parameter efficiency, recognition accuracy, and computational complexity. The number of parameters has been reduced by 0.73 million, further improving the model’s speed. Gflops has been reduced by 1.5, lowering the performance requirements for computational hardware during model deployment. Recognition accuracy has reached 99.5%, with further improvements in model precision. Future research will explore how to capture shoeprint images with complex backgrounds from shoes worn at crime scenes, aiming to further enhance the model’s recognition capabilities in more forensic scenarios. Full article

The integration of extended reality (XR) technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is transforming forensic investigation by empowering processes such as crime scene reconstruction, evidence analysis, and professional training. This manuscript presents a systematic review of technological advances in XR technologies developed and employed for forensic investigation, their impacts, challenges, and prospects for the future. A systematic review was carried out based on the PRISMA^® methodology and considering articles published in repositories and scientific databases such as SCOPUS, Science Direct, PubMed, Web of Science, Taylor and Francis, and IEEE Xplore. Two observers carried out the selection of articles and a Cohen’s Kappa coefficient of 0.7226 (substantial agreement) was evaluated. The results show that XR technologies contribute to improving accuracy, efficiency, and collaboration in forensic investigation processes. In addition, they facilitate the preservation of crime scene data and reduce training costs. Technological limitations, implementation costs, ethical aspects, and challenges persist in the acceptability of these devices. XR technologies have significant transformative potential in forensic investigations, although additional research is required to overcome current barriers and establish standardized protocols that enable their effective integration. Full article

The pupal stage in necrophagous flies represents the longest and least morphologically distinct phase of development, posing a persistent challenge for accurately estimating postmortem intervals (PMI) in forensic investigations. Here, we present a novel molecular approach to pupal age estimation in Sarcophaga peregrina, a forensically important species, by profiling microRNA (miRNA) expression dynamics. High-throughput sequencing across early, mid, and late pupal stages identified 191 known miRNAs, of which nine exhibited distinct monotonic temporal trends. Six miRNAs (miR-210-3p, miR-285, miR-927-5p, miR-956-3p, miR-92b, and miR-275-5p) were validated by qRT-PCR and demonstrated consistent time-dependent expression patterns. Polynomial regression models revealed a strong correlation between miRNA abundance and developmental age (R² = 0.88–0.99). Functional enrichment analyses of predicted miRNA targets highlighted their roles in key regulatory pathways, including ecdysteroid signaling, hypoxia response, autophagy, and energy metabolism. This study establishes, for the first time, a robust miRNA-based framework for estimating pupal age in forensic entomology, underscoring the potential of miRNAs as temporally precise biomarkers for PMI estimation. Full article

Chrysomya megacephala, as one of the common blowflies, displays biological characteristics, such as ovoviviparity and carrion-feeding adaptation. Thus, this species is generally considered of significant ecological, medical, and forensic importance. However, without a high-quality pseudo-chromosome genome for C. megacephala, elucidating its evolutionary trajectory proved difficult. Herein, we assembled and analyzed a high-quality chromosome-level genome assembly of the C. megacephala, combined with PacBio HiFi long reads, Hi-C data, and Illumina reads. The pseudo-chromosomes assembly of C. megacephala spans 629.44 Mb, with 97.05% anchored to five chromosomes. Final assembly includes 1056 contigs (N50 = 1.68 Mb), and 97 scaffolds (N50 = 121.37 Mb), achieving 98.90% BUSCO completeness (n = 1367). Gene annotation predicted 17,071 protein-coding genes (95.60% BUSCO completeness), while repeat masking identified 244.26 Mb (38.82%) as repetitive elements. Additionally, 3740 non-coding RNAs were characterized. Gene family analyses resulted in 10,579 gene families, containing 151 gene families that experienced rapid evolution. Comparative genomic analyses showed that the expanded genes are related to reproduction and necrophagous habits. In addition, we annotated the gene family P450s, CCEs, IRs, GRs, and ORs, all of which represent remarkable expansion, playing a crucial role in the mechanism of locating the hosts for forensic insects. Our research establishes a high-quality genome sequence to facilitate subsequent molecular investigations into significant species within forensic entomology. Full article

The demand for rapid, portable, and cost-effective analytical tools has driven advances in smartphone-based microfluidic sensors. By combining microfluidic precision with the accessibility and processing power of smartphones, these devices offer real-time and on-site diagnostic capabilities. This review explores recent developments in smartphone-integrated microfluidic sensors, focusing on their design, fabrication, smartphone integration, and analytical functions with the applications in forensic science, agriculture, and environmental monitoring. In forensic science, these sensors provide fast, field-based alternatives to traditional lab methods for detecting substances like DNA, drugs, and explosives, improving investigation efficiency. In agriculture, they support precision farming by enabling on-demand analysis of soil nutrients, water quality, and plant health, enhancing crop management. In environmental monitoring, these sensors allow the timely detection of pollutants in air, water, and soil, enabling quicker responses to hazards. Their portability and user-friendliness make them particularly valuable in resource-limited settings. Overall, this review highlights the transformative potential of smartphone-based microfluidic sensors in enabling accessible, real-time diagnostics across multiple disciplines. Full article

Forensic entomotoxicology is a subdiscipline that utilizes necrophagous insects as bioindicators for detecting drugs and toxicants in decomposed remains, particularly in cases where conventional biological matrices are no longer available. Toxic substances can profoundly alter insect development, physiology, and community succession, potentially impacting the accuracy of postmortem interval (PMI) estimation. This review systematically summarizes the effects of various xenobiotics, including pesticides, illicit drugs, sedatives, heavy metals, and antibiotics on larval growth, physiological traits, and gut microbial composition in forensically relevant flies. However, most studies to date have relied primarily on phenotypic observations, with limited insight into underlying molecular mechanisms. Significant interspecies and dose-dependent variability also exists in the absorption, metabolism, and physiological responses to xenobiotics. We highlight recent advances in multi-omics technologies that facilitate the identification of molecular biomarkers associated with xenobiotic exposure, particularly within the insect detoxification system. Key components such as cytochrome P450 monooxygenases (P450s), glutathione S-transferases (GSTs), and ATP-binding cassette (ABC) transporters play essential roles in xenobiotic metabolism and insecticide resistance. Additionally, the insect fat body serves as a central hub for detoxification, hormonal regulation, and energy metabolism. It integrates signals related to xenobiotic exposure and modulates larval development, making it a promising model for future mechanistic studies in insect toxicology. Altogether, this review offers a comprehensive and reliable framework for understanding the complex interactions between toxic substance exposure, insect ecology, and decomposition in forensic investigations. Full article

Background/Objectives: Artificial intelligence (AI) is beginning to be applied in wound ballistics, showing preliminary potential to improve the accuracy and objectivity of forensic analyses. This review explores the current state of AI applications in forensic firearm wound analysis, emphasizing its potential to address challenges such as subjective interpretations and data heterogeneity. Methods: A systematic review adhering to PRISMA guidelines was conducted using databases such as Scopus and Web of Science. Keywords focused on AI and GSW classification identified 502 studies, narrowed down to 4 relevant articles after rigorous screening based on inclusion and exclusion criteria. Results: These studies examined the role of deep learning (DL) models in classifying GSWs by type, shooting distance, and entry or exit characteristics. The key findings demonstrated that DL models like TinyResNet, ResNet152, and ConvNext Tiny achieved accuracy ranging from 87.99% to 98%. Models were effective in tasks such as classifying GSWs and estimating shooting distances. However, most studies were exploratory in nature, with small sample sizes and, in some cases, reliance on animal models, which limits generalizability to real-world forensic scenarios. Conclusions: Comparisons with other forensic AI applications revealed that large, diverse datasets significantly enhance model performance. Transparent and interpretable AI systems utilizing techniques are essential for judicial acceptance and ethical compliance. Despite the encouraging results, the field remains in an early stage of development. Limitations highlight the need for standardized protocols, cross-institutional collaboration, and the integration of multimodal data for robust forensic AI systems. Future research should focus on overcoming current data and validation constraints, ensuring the ethical use of human forensic data, and developing AI tools that are scientifically sound and legally defensible. Full article

The aim of this study was to compare the types, textile structures, labels, and fiber compositions of 64 red garments submitted as evidence in selected criminal cases between 2022 and 2024. The research enhanced the current knowledge of the characteristics of red clothing available to consumers and demonstrated the relevance of textile analysis in forensic science. Knitted fabrics were the most commonly used in the garments, followed by woven fabrics, nonwovens, and felts. Fiber identification focused on color and shade, generic classification, morphological structure, and chemical composition, revealing both similarities and distinctions among the samples. In a small percentage of cases, label information was found to be inaccurate. The study also examined the fiber content of threads, patches, logos, prints, and embroidery, underscoring the forensic potential of these often-overlooked elements. The identification of over 300 individual fibers enabled a critical evaluation of the analytical procedures and confirmed their effectiveness in forensic contexts. Full article

The application of artificial intelligence within forensic image analysis marks a significant step forward for the non-destructive examination of evidence, a crucial practice for maintaining the integrity of a crime scene. While non-destructive testing (NDT) methods are established, the integration of AI, particularly for analyzing ballistic evidence, requires further exploration. This preliminary study directly addresses this gap by focusing on the use of deep learning to automate the analysis of bullet holes. This work investigated the performance of two state-of-the-art convolutional neural networks (CNNs), YOLOv8 and R-CNN, for detecting ballistic markings in digital images. The approach treats digital image analysis itself as a form of non-destructive testing, thereby preserving the original evidence. The findings demonstrate the potential of AI to augment forensic investigations by providing an objective, data-driven alternative to traditional assessments and increasing the efficiency of evidence processing. This research confirms the feasibility and relevance of leveraging advanced AI models to develop powerful new tools for Forensic Science. It is expected that this study will contribute worldwide to help (1) the police indict criminals and prove innocence; (2) the justice system judges and proves people guilty of their crimes. Full article

Sex determination is a fundamental component of forensic identification and medicolegal investigations. Several studies have investigated sexual dimorphism through mandibular osteometric measurements, including the position of anatomical foramina such as the mandibular and mental foramen (MF), reporting population-specific discrepancies. This study assessed the reliability and predictive ability of specific anthropometric mandibular measurements for sex estimation using three-dimensional (3D) cone beam computed tomography (CBCT) surface reconstructions. Methods: CBCT scans from 204 Greek individuals (18–70 years) were analyzed. Records were categorized by sex and age. Five linear measurements were traced on 3D reconstructions using ViewBox 4 software: projections of the inferior points of the right and left mental and mandibular foramina and the linear distance between mental foramina projections. A binary logistic regression (BLR) model was employed. All measurements showed statistically significant sex differences, with males presenting higher mean values. The final model achieved accuracy of 66.7% in sex prediction, with two vertical measurements—distances from the right mandibular foramen and the left mental foramen—identified as the strongest predictors of sex. The positions of the mandibular and mental foramina demonstrate sex-related dimorphism in this Greek sample, supporting their forensic relevance in population-specific applications. Full article

Background: The gut–brain axis (GBA) has been demonstrated to be involved in normal neurodevelopment, with its dysfunction potentially contributing to the onset of mental disorders. In this systematic review and meta-analysis, we aimed to examine the relationship between levels of specific biomarkers of intestinal permeability or inflammation and scores of depressive symptoms or suicidality. Methods: All studies investigating the link between depressive symptoms and/or suicidality and biomarkers associated with intestinal permeability or inflammation were included. Studies providing data for comparisons between two groups—depressive or suicidal patients vs. healthy controls, or suicidal vs. non-suicidal patients—were included in the meta-analysis. Studies examining the correlation between depressive symptoms and biomarker levels were also included into the review. Data were independently extracted and reviewed by multiple observers. A random-effects model was employed for the analysis, and Hedge’s g was pooled for the effect size. Heterogeneity was assessed using the I² index. Results: Twenty-two studies provided data for inclusion in the meta-analysis, while nineteen studies investigated the correlation between depressive symptoms and biomarker levels. For depressive symptoms, when compared to the controls, patients showed significantly increased levels of intestinal fatty acid-binding protein (I-FABP) (ES = 0.36; 95% CI = 0.11 to 0.61; p = 0.004; I² = 71.61%), zonulin (ES = 0.69; 95% CI = 0.02 to 1.36; p = 0.044; I² = 92.12%), antibodies against bacterial endotoxins (ES = 0.75; 95% CI = 0.54 to 0.98; p < 0.001; I² = 0.00%), and sCD14 (ES = 0.11; 95% CI = 0.01 to 0.21; p = 0.038; I² = 10.28%). No significant differences were found between the patients and controls in levels of LPS-binding protein (LBP) and alpha-1 antitrypsin (A-1-AT). For suicidality, four studies were identified for quantitative analysis, three of which focused on I-FABP. No significant differences in I-FABP levels were observed between suicidal patients and the controls (ES = 0.24; 95% CI = −0.30 to 0.79; p = 0.378; I² = 86.44%). Studies investigating the correlation between depressive symptoms and levels of intestinal permeability and inflammation biomarkers did not provide conclusive results. Conclusions: A significant difference was observed between patients with depressive symptoms and controls for biomarkers of intestinal permeability (zonulin, which regulates tight junctions), inflammatory response to bacterial endotoxins (antibodies to endotoxins and sCD14—a soluble form of the CD14 protein that modulates inflammation triggered by lipopolysaccharides), and acute intestinal epithelial damage (I-FABP, released upon enterocyte injury). Studies investigating suicidality and related biomarkers were limited in number and scope, preventing definitive conclusions. Overall, these findings suggest that biomarkers of gut permeability represent a promising area for further investigation in both psychiatric and forensic pathology. They may have practical applications, such as supporting diagnostic and therapeutic decision-making in clinical settings and providing pathologists with additional information to help determine the manner of death in forensic investigations. Full article