Forensic Identification Research Articles (Page 1)

MicroRNAs (miRNAs) have emerged as promising biomarkers for the identification of forensically significant body fluids, aiding crime scene reconstruction and the identification of biological material donors. However, despite their potential, inter-study discrepancies and lack of reproducibility have limited their forensic application, particularly in body fluid identification. A major challenge lies in the variability introduced by different nucleic acid extraction methods. This study aimed to evaluate the co-extraction performance and miRNA detection efficiency of three commonly used nucleic acid extraction kits (AccuPrep Genomic DNA Extraction Kit, QIAamp DNA Mini Kit, and miRNeasy Tissue/Cells Advanced Micro Kit) across five saliva input volumes (400 µL, 200 µL, 100 µL, 50 µL, and 25 µL). To assess yield, purity, robustness (CV%), and sensitivity, two quantification platforms were used, followed by a two-step reverse transcription quantitative polymerase chain reaction (RT-qPCR) protocol to evaluate the impact of extraction methods on miRNA expression. Our results demonstrate that both the extraction procedure and the quantity of input material significantly influences nucleic acid recovery. Additionally, we observe variations in miRNA levels depending on the extraction method used. Surprisingly, the kit designed specifically for miRNA extraction yielded relatively poor miRNA recovery. In contrast, the DNA extraction kit (AccuPrep Genomic DNA Extraction Kit) produced the highest nucleic acid yield with moderate purity and showed the lowest Cq values for miRNA targets, indicating better miRNA detection. These findings underscore the importance of the choice of extraction kit, as it can significantly influence both the yield and quality of nucleic acids detected, and the extension, accuracy and reliability of miRNA-based forensic analyses.

Background/Objectives: According to the World Drug Report 2025, cannabis is the most abused drug in the world, being sold in illicit markets in various physical forms ranging from herbal cannabis to cannabis resin and liquid cannabis. Currently, the methods used for cannabis identification are largely based on the morphological features and chemical content of the product. In this respect, identification could be severely impacted if the product is highly fragmented or pulverised. As such, DNA-based molecular techniques offer a viable alternative detection approach. In this study, we have developed a robust DNA testing method for cannabis identification, with high sensitivity and specificity. Methods/Results: Two plant DNA barcode regions, rbcL and matK, were successfully amplified in a cohort of 54 cannabis plant samples. DNA sequences obtained from these samples were blast-searched against GenBank and resulted in returned matched identity of at least 99% compared to their corresponding Cannabis sativa reference sequences. In addition, the amplification of two cannabis-unique markers, the tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes, produced amplicons with expected sizes only in cannabis samples; these amplicons were not detected in those plants closely related to cannabis. Sequence comparison of the majority of samples yielded at least 97% matched identity against C. sativa reference sequences in GenBank. The THCAS and CBDAS markers detected only the cannabis DNA in varying levels of cannabis–hops and cannabis–tobacco DNA mixtures. Lastly, the use of the four markers could effectively differentiate between cannabis and non-cannabis in 27 blinded samples, including 18 actual casework samples. Conclusions: In conclusion, these four genetic markers can be used to discriminate cannabis from other plant species at the genus level, especially in challenging forensic samples lacking morphological features which therefore cannot be determined by traditional detection methods. As such, this method can complement existing techniques to identify a myriad of cannabis samples.

Protein glycosylation modification as a novel forensic biomarker for discriminating monozygotic twins.

The morphometric analysis of the temporomandibular joint (TMJ) using cone-beam computed tomography (CBCT) provides valuable insights into joint morphology and potential sex-based variations, which are critical for clinical and forensic applications. Seventy subjects (35 males, 35 females) aged from 18 to 30 years underwent CBCT imaging. Mandibular fossa and condylar morphology were classified qualitatively. Quantitative measurements included three linear parameters (distance from posterior fossa to articular eminence apex, vertical distance from fossa roof to articular eminence apex, and distance between intermediate point and apex of articular eminence) and one angular parameter. Statistical analysis was performed using Student’s t-test and Chi-square test. Results revealed no significant gender differences in qualitative classifications of mandibular fossa and condyle. Morphological features predominantly included round fossa (60- 64%) and flattened condyles (40-52%). Notably, significant sexual dimorphism emerged in the vertical distance from fossa roof to articular eminence apex and articular eminence angle, with males consistently demonstrating larger measurements. Statistical significance was observed bilaterally, with p-values ranging from 0.001 to 0.033 for these parameters. The findings underscore that, while qualitative TMJ morphological classifications do not show gender predilection, specific quantitative morphometric parameters exhibit meaningful sexual dimorphism. These population-specific reference data hold substantial potential for advancing TMJ disorder diagnosis and for supporting forensic identification procedures, offering a nuanced understanding of joint structural variations across sexes.

ABSTRACT Accurate sex estimation from skeletal remains is a critical component of forensic anthropology. This study aimed to evaluate sexual dimorphism in the femur and humerus among Malaysians and thus develop discriminant function formulas based on post-mortem computed tomography (PMCT) images. A total of 400 PMCT scans from adult individuals (200 males and 200 females) were analysed, with standard osteometric measurements taken for the maximum diameter and epicondylar width of the femur and humerus. Statistical analysis revealed significant differences between sexes across all parameters (p < 0.05), with male values consistently larger. Five discriminant function formulas were generated: humerus epicondylar width (91.8%), combination of humerus epicondylar width and femur epicondylar width (92.3%), combination of femur max diameter and humerus epicondylar width (91.8%), combination of humerus maximum diameter and epicondylar width (94.5%), and combination of humerus max diameter and femur epicondylar width (91.8%). Result shows a very significant difference between male and female for all the parameters tested, but no significant differences were observed between ethnic groups, supporting the use of a unified model for the Malaysian population. These findings provide validated, population-specific tools for forensic identification and highlight the value of PMCT in enhancing the objectivity and reliability of sex estimation practices.

Anthropometric identification is crucial in legal contexts, where height is a relevant distinguishing characteristic. The estimation of height through plantar impressions is a tool used in forensic podiatric examinations, and it is relevant to be familiar with the most suitable techniques for correlating the length of the footprints with height. This systematic review aims to evaluate forensic identification methods by analysing inked footprint collection and measurement techniques, their correlation with height, and the available scientific evidence. A systematic review of the available literature was conducted in the following databases: PubMed, Embase, Scopus, Web of Science, CINAHL, and Dialnet. Descriptive observational studies linking the length of plantar impressions of healthy adults of both sexes with their height were included. Seven studies published between 2012 and 2020 were selected. All studies showed statistically significant correlations between height and plantar impression measurements, analyzed using Pearson´s correlation coefficient. The inked footprint method was the most widely used and demonstrated high reliability. Additionally, techniques based on foot contours and pedigraphs also showed strong correlations with height and foot length. However, the scientific evidence on this topic is limited, highlighting the need for further research to optimise these techniques and enhance their validity for forensic applications.

We demonstrate a depth-resolved Swept-Source Optical Coherence Tomography (SS-OCT) system for automated poroscopy network identification in fingerprint biometrics. The system employs a 100 kHz swept-source laser (λ0 = 1060 nm, Δλ = 110 nm) to achieve ~4.5 μm axial and 13 μm lateral resolution at 1.8 mW incident power. High-density scanning (1500 A-scans/B-scan × 500 B-scans over a 3 × 3 mm region) captures volumetric enface images that precisely localize Level 3 sweat-pore microfeatures. In a study of 40 healthy volunteers (20 M, 20 F), an automated ImageJ pipeline extracted pore morphology with sub-pixel accuracy. Two-way ANOVA attributed 60.10% of total variance to pore shape (p < 0.0001) and 14.62% to gender (p < 0.0001). The average pore count was significantly higher in males (20.53 ± 2.14) compared to females (14.00 ± 1.76, p < 0.0001). These results validate SS-OCT-based poroscopy as a robust, depth-resolved alternative for forensic and biometric identification.

Inferring the biogeographic origin of unknown individual is crucial in forensic practice. To enhance the efficacy of biogeographic origin inference, we previously developed a novel panel containing 56 ancestry-informative insertion/deletions (AI-InDels), three Y-InDels, and the Amelogenin gene, all with amplicons less than 200bp, facilitating DNA analysis of degraded samples. In this research, we investigated the forensic performance of the InDel panel in the Kazakh group in China, elucidated the genetic structure of Kazakh group, and verified the panel's efficacy in assigning unknown individuals to the appropriate intercontinental regions. The findings demonstrated that the novel panel showed relatively high genetic polymorphisms of autosomal InDels and could serve as an efficient tool for forensic individual identification in Kazakh group. Furthermore, the multiple results of population genetic analyses indicated that the Kazakh group has a mixture of ancestral components from East Asian and European populations, with East Asian ancestry predominating. Using different machine learning models, we found that the novel panel assigned unknown individuals to their intercontinental regions with at least 99% accuracy at the three-continental level and at least 90% accuracy at the five-continental level. In conclusion, we demonstrated that the novel panel can effectively reveal the genetic structure of the group with mixed genetic background and infer the biogeographic origins of unknown individuals with high accuracy.

The ongoing challenge of doping in sports has triggered the adoption of advanced scientific strategies for the detection and prevention of doping abuse. This review examines the potential of integrating metabolomics aided by artificial intelligence (AI) and machine learning (ML) for profiling small-molecule metabolites across biological systems to advance anti-doping efforts. While traditional targeted detection methods serve a primarily forensic role—providing legally defensible evidence by directly identifying prohibited substances—metabolomics offers complementary insights by revealing both exogenous compounds and endogenous physiological alterations that may persist beyond direct drug detection windows, rather than serving as an alternative to routine forensic testing. High-throughput platforms such as UHPLC-HRMS and NMR, coupled with targeted and untargeted metabolomic workflows, can provide comprehensive datasets that help discriminate between doped and clean athlete profiles. However, the complexity and dimensionality of these datasets necessitate sophisticated computational tools. ML algorithms, including supervised models like XGBoost and multi-layer perceptrons, and unsupervised methods such as clustering and dimensionality reduction, enable robust pattern recognition, classification, and anomaly detection. These approaches enhance both the sensitivity and specificity of diagnostic screening and optimize resource allocation. Case studies illustrate the value of integrating metabolomics and ML—for example, detecting recombinant human erythropoietin (r-HuEPO) use via indirect blood markers and uncovering testosterone and corticosteroid abuse with extended detection windows. Future progress will rely on interdisciplinary collaboration, open-access data infrastructure, and continuous methodological innovation to fully realize the complementary role of these technologies in supporting fair play and athlete well-being.

ABSTRACT Mercury ions present severe threats to both human health and the environment, making their detection of paramount importance. Here, we innovatively designed and synthesized coumarin derivative–based probe TXDS . By virtue of the robust interaction between the thiocarbonyl moiety of TXDS and Hg 2 ⁺, which effectively regulated intramolecular conformational locking, a superior aggregation‐induced emission (AIE) characteristic was successfully constructed. Probe TXDS , coupled with 100 nm Stokes shift, enabled highly sensitive and selective detection of Hg 2 ⁺ within the concentration range of 0.1–10 µM. In addition, probe TXDS demonstrated excellent solid–liquid dual‐mode detection capability for Hg 2 ⁺. Colorimetric sensing extended the detection range of Hg 2 ⁺ by 10‐fold, realizing the complementary advantages of the wide dynamic range of colorimetry and the high sensitivity of fluorimetry. Mechanochemical treatment, involving grinding TXDS with soil samples, achieved visual analysis of Hg 2 ⁺ within 20 s, highlighting the green, efficient, and cost‐effective nature of this approach. Moreover, the dye TXDO exhibited tunable piezochromic behavior, offering potential applications in forensic identification and information encryption.

Background: Palatal rugae are unique anatomical structures located on the anterior part of the hard palate, characterized by individual morphological stability and variability. Their potential role in orthodontic diagnosis and forensic identification has garnered increasing attention. While previous studies have explored specific rugae traits, few have comprehensively analyzed multiple characteristics across different sagittal malocclusion types, genders, and treatment phases. Aim: To evaluate and compare the number, shape, length, orientation, and union of palatal rugae among individuals with different sagittal malocclusion types (Class I, Class II Division 1, Class II Division 2, and Class III), across genders and between pre-orthodontic and post-orthodontic treatment stages. Materials and Methods: This cross-sectional clinical study involved 300 participants, aged 18 to 28 years, divided equally into three groups based on untreated sagittal malocclusion: Class I, Class II, and Class III, according to Angle’s classification. Key parameter that includes number, shape, length, orientation, and presence of union across different malocclusion classes were assessed and statistically analyzed using Social Sciences (SPSS) Version 26.0. Results: Rugae number significantly varied across malocclusion types (p = 0.002), with the highest count observed in Class II Division 2 and the lowest in Class III. Females exhibited a significantly higher rugae count than males (p = 0.021). Rugae length showed no significant changes between pre- and post-treatment stages (p &gt; 0.05). Shape distribution differed significantly by malocclusion class (p = 0.049), with wavy and curved patterns dominating specific groups. Orientation patterns were also significantly associated with malocclusion types (p = 0.034), with posterior orientation more frequent in Class III and Class II Division 2. The presence of rugae union was rare and statistically insignificant across groups. Conclusion: Palatal rugae characteristics-particularly number, shape, and orientation—demonstrate significant diagnostic associations with sagittal malocclusion types and gender. Their morphological stability across treatment stages supports their utility as a reliable, non-invasive diagnostic adjunct in orthodontics and a valuable tool in forensic identification.

Effective conservation of wild mammals necessitates accurate taxonomic classification and reliable genetic reference data. In China, the List of State Key Protected Wild Animals serves as a critical tool for species protection. However, taxonomic revisions and gaps in genetic data can impede its effectiveness. In this study, we updated the List of State Key Protected Wild Animals (2021) by incorporating recent taxonomic and distributional evidence, resulting in a refined list of 169 mammalian species that are protected. We identified 15 taxa lacking complete mitochondrial genome data and addressed this gap by generating 12 new mitogenomes for nine taxa using a combination of GenBank database mining and next-generation sequencing of museum specimens and fecal samples. These efforts led to the establishment of a curated mitochondrial genome reference database encompassing 164 species. Our analyses also uncovered taxonomic ambiguities in genera such as Moschus and Naemorhedus, and highlighted mislabeling issues within public genetic databases. This curated database enhances the accuracy of forensic species identification, supports biodiversity monitoring, and strengthens wildlife law enforcement. Our findings underscore the value of integrating historical specimens with mitogenomic approaches to advance wildlife conservation efforts.

The research paper examines the pharmacognostic assessment, as well as the in-vivo studies. This paper presents a rigorous, expert-level comparative evaluation of Variable Number Tandem Repeat (VNTR) profiling applied to three common forensic biological matrices blood, saliva, and hair in a hypothetical project designed to illuminate foundational issues in forensic DNA analysis. Although VNTRs have been largely superseded by Short Tandem Repeat (STR) systems in contemporary casework, VNTR-based workflows remain a powerful pedagogic vehicle for exploring the core determinants of analytical performance. Using standardized extraction and VNTR-typing workflows across matrices, we systematically examined analytical efficiency, genotype quality, and reproducibility while assessing the impact of starting DNA quantity, matrix-specific inhibitory substances, and sample-associated contamination. Comparative findings indicate that whole blood consistently yielded the highest-quality and most reproducible VNTR profiles, whereas saliva produced variable results influenced by bacterial and food-derived contaminants, and hair particularly shed or rootless shafts frequently returned low-yield or partial profiles. Reproducibility analyses highlighted greater intra- and inter-assay variability for saliva and hair compared with blood, underscoring matrix-dependent limits on discriminatory power. Beyond historical interest, these results translate directly to modern STR practice: they clarify why sample collection, inhibitor mitigation, and DNA quantitation remain critical determinants of success irrespective of marker system. The study therefore both situates VNTRs in the historical arc of forensic genetics and distills enduring methodological lessons for contemporary forensic laboratories and training programs.

Background: Stature is a vital anthropometric parameter in forensic science, anthropology, and medical practice. In situations where complete body remains are unavailable, reliable regression models using alternative body measurements are essential for stature estimation. Objective: This study aimed to develop regression models for estimating stature using selected anthropometric variables, elbow height (EH), buttock-knee length (BKL), and buttock-popliteal length (BPL), in a Nigerian population. Methods: A cross-sectional study was conducted on 240 adult participants. Standard anthropometric procedures were employed to measure stature, EH, BKL, and BPL. Data were analyzed using descriptive statistics, independent t-tests, and linear regression. Multicollinearity was assessed using the variance inflation factor (VIF), and model accuracy was evaluated with the standard error of estimate (SEE). Results: The average height was 169.96 ± 8.02 cm, average EH was 20.99 ± 2.56 cm, average BKL was 58.53 ± 3.51 cm, and average BPL was 49.04 ± 3.27 cm. Regression analyses showed that BKL and BPL were significant predictors of height (p &lt; 0.05), while EH was not significantly associated. The regression models demonstrated good predictive accuracy (SEE &lt; 1, VIF &lt; 2). Sex-specific analyses indicated slightly higher predictive accuracy among females (r = 0.61, SEE = 5.159) compared to males (r = 0.56, SEE = 5.846). The overall regression model produced a moderate correlation coefficient (r = 0.61) with an SEE of 6.411. Conclusion: BKL and BPL are reliable predictors of stature in this Nigerian population, whereas EH is less useful. The regression models developed provide a population-specific tool for stature estimation, with practical applications in forensic identification, medical evaluation, and ergonomic design. Future studies with larger sample sizes and additional anthropometric parameters are recommended to enhance predictive accuracy.

Infrared (IR) spectroscopy is a powerful tool for analyzingcomplexsubstances. It allows one to identify materials based on their functionalgroups. Nevertheless, spectral interpretation of composite materialswith IR spectroscopy remains a significant challenge for analyticaland forensic laboratories, especially if the samples are small, producingsignificant scattering that complicates the interpretation of IR spectra.Effectively tackling this challenge, we present an innovative IR-basedmethod for nondestructively identifying the individual componentswithin a mixture. Our study focuses on two- and multicomponent stronglyscattering homogeneous mixed-composition microspheres filled withorganic polymers. The results reveal that our algorithm can (1) reconstructthe pure absorption of functional groups in composite systems by eliminatingscattering effects, (2) identify the number of components in a mixture,(3) determine the volume fractions of the constituents, and (4) generatepure permittivity spectra for each component in the mixture. Whatsets this method apart is its noninvasive natureit does notrely on expensive separation techniques such as chromatography ortime-consuming calibration processes. Additionally, it may be fullyautomated, making it accessible for users of various levels of expertisein spectroscopy. The method addresses critical challenges in analyticaland forensic chemistry, enabling previously unattainable insights,such as rapid quantification of trace chemicals in drug formulationsfor forensic analysis or identification of synthetic polymer blendsin environmental microplastics.

Kinship verification using biometric traits is crucial for finding missing children, rapid forensic identification, and social media analysis. Ear biometrics is gaining attention due to its uniqueness, permanence, and non-intrusiveness. However, current research on ear-based kinship verification is limited, and the factors associated with ear similarities across kinship-related individuals require to be evaluated. To fill this gap, our study developed deep learning models to quantify the similarity between ear images and conduct the kinship verification task. Two ear image datasets, namely SCED and CNKE, were collected from Chinese subjects. The SimiNet model, which utilized a pre-trained ResNet50 as its backbone, was constructed to evaluate the cosine similarity between ear image pairs. For kinship verification, the VTrans model was established by combining a pre-trained VGG16 with Transformer modules. Based on the cosine similarity, the SimiNet model obtained 93.53% accuracy and an AUC (Area Under the Curve) of 0.98 for personal verification. The similarity analysis further revealed that kinship-related individuals of the same sex displayed higher ear similarity scores. The VTrans model attained 71.17% accuracy and an AUC of 0.76 on the CNKE dataset. Heatmaps revealed that the VTrans model focused on the helix and the upper half of the ear during kinship verification. The model code has been provided in Github ( https://anonymous.4open.science/r/SimNet_VTrans-EB41 ) to facilitate refinement in future research. Our study has successfully established deep learning models for ear similarity quantification and kinship verification, providing effective tools for biometric forensic identification.

In forensic saliva testing, false negatives of the Phadebas® and false positives of the RSID™-saliva methods generated by citric acid are critical issues for interpreting the results. Citric acid-containing beverages, which are typically mixed with forensic saliva samples, may produce indistinguishable results in two commercially available saliva tests in the presence and absence of saliva. To solve this issue, this study aimed to evaluate the usefulness of the indirect enzyme-linked immunosorbent assay (ELISA)-based method of detecting cystatin D to identify saliva in citric acid-containing beverage-mixed samples. Four commercially available beverages were mixed with human saliva or ultrapure water, and the mixed stain samples were examined by the aforementioned three saliva tests. Two conventional saliva tests could not distinguish between the samples with and without saliva. Although the positive detection rates of the ELISA method decreased when mixed with beverages, no saliva-free beverage samples produced false positives. Our results indicate that the detection of cystatin D by indirect ELISA would be useful in identifying saliva from samples potentially mixed with citric acid-containing beverages.

ABSTRACT Background: Palatal rugae are unique anatomical landmarks that remain stable over time, making them a valuable tool in forensic odontology. However, the impact of prosthetic rehabilitation, particularly complete dentures and dental implants, on rugae morphology remains a subject of study. This research aims to compare palatal rugae patterns in dentate individuals, denture wearers, and implant users to assess their significance in forensic identification. Methods: A comparative study was conducted on 60 individuals divided into three groups: dentate individuals, complete denture wearers, and implant users. Rugae patterns were analyzed using digital scanning, and statistical comparisons were made. Results: Significant alterations in rugae morphology were observed in denture users (P = 0.02), whereas implant users demonstrated better preservation of rugae (P = 0.04). Conclusion: Palatal rugae remain a reliable forensic marker in implant users, but complete dentures may alter identification accuracy.

This study conducted a retrospective analysis of head and neck magnetic resonance imaging (MRI) scans from the Pediatric Radiology Department at Ankara University Faculty of Medicine, covering the period from 2014 to 2022. The objective was to assess facial soft tissue thickness (FSTT) in children aged 3-18years, thereby contributing to forensic and anthropological identification processes and establishing a dataset. A sample of 300 healthy youngsters was included, divided into three age groups: 3-8years, 9-13years, and 14-18years, according to age and body mass index (BMI) percentiles. The findings demonstrate that FSTT rises with age, with subnasale dimensions attaining 15.0 ± 2.9mm in males and 12.6 ± 2.3mm in females at the age range of 14-18years (p < 0.001). Males demonstrated elevated FSTT values at the glabella landmark, whereas children in the 85th and 95th BMI percentiles exhibited significantly higher FSTT values (p < 0.001). The most significant BMI-related differences were observed in the labiale superius and subnasale in females aged 9 to 18years. This study offers the pilot comprehensive dataset on variations in FSTT across children, categorized by age, sex, and BMI in Türkiye. The dataset primarily facilitates profile-based approximations, given its emphasis on midline features. However it may also provide limited utility in 3D facial reconstruction efforts. The findings will be essential for forensic and anthropological research. The findings address a gap in the forensic science literature and highlight the necessity for more research with varied age groups and foreigners.

C6-keto-opioids, such as hydrocodone, hydromorphone, oxycodone, and oxymorphone, are a group of semi-synthetic morphine-like analgesics with extensive applications in clinical settings and high potential for abuse and misuse. Therefore, they have become targets of workplace forensic urine drug testing (UDT) for years. Due to the undesired C6-C7 keto-enol tautomerization, the C6 ketone often needs to be deactivated prior to further derivatization for GC-MS analysis. Although it has been over two decades since the method of converting the C6 ketone to its methoxime-derivative was initially reported, little information has been published regarding the resulting Z/E-methoxime-derivative isomers' formation mechanism, stereo-configurations, or relative kinetic or thermodynamic features. Mixed Z/E-methoxime-derivative isomers create a potential peak resolution issue for GC-MS-based C6-keto-opioids identification and quantification, since the two isomers are often difficult to be completely separated by GC and they share common fragmentation pathways. We here provided the first detailed report and qualitative conformational analyses of the Z/E-methoxime-derivative isomers of C6-keto-opioids and their isomerization under the non-aqueous Brønsted-Lowry acidic conditions. By in-depth studying the C6-keto-opioids Z/E-methoxime-derivative isomers, we were able to gain important insights into potential reaction condition optimization with an attempt to reduce the formation of the minor methoxime-derivative isomer, thus, to minimize the potential interferences caused by co-existing of the two isomers and further improve the method's limit of detection (LOD) and/or limit of quantification (LOQ). Our report offered valuable information that could facilitate other laboratories using the similar derivatization procedures for GS-MS-based C6-keto-oipoids testing to improve their testing method sensitivity and enhance their analysis product quality.