Journal of forensic sciences最新文献

The legalization of hemp cultivation in the United States has raised the need for reliable methods to distinguish between legal hemp and illegal marijuana. Genetic analysis has emerged as a powerful tool, surpassing traditional chemical methods in specific aspects, such as analyzing trace amounts, aged samples, and different parts of the sample. Genetic differences in cannabinoid synthase genes offer promise for precise crop type determination, particularly focusing on genes like tetrahydrocannabinolic acid synthase (THCAS), cannabidiolic acid synthase (CBDAS), and cannabichromenic acid synthase (CBCAS). However, previous research faced several challenges in developing discriminatory genetic markers, including limited sample sizes, high similarity between the synthase genes, and the presence of pseudo synthase genes. A comprehensive study using Next-Generation Sequencing (NGS) introduced a differentiation flowchart based on THCAS, CBDAS, and THCAS pseudogenes. To bridge the gap between NGS and the practical requirements of crime laboratories, two rapid genotyping assays were developed: a CE-based SNaPshot™ assay and a TaqMan™ real-time PCR assay. While the SNaPshot™ assay effectively differentiated various hemp and marijuana types, differentiation was limited with marijuana samples containing THC% close to the 0.3% legal threshold (0.3%–1%). The TaqMan™ qPCR SNP genotyping assay provided quicker results, making it an efficient choice for crime laboratories. However, this method had the same limitations as the SNaPshot™ assay with addtional challenges in differentiating edible hemp seed samples, and it did not provide additional CBD information. The study also highlighted the influence of two variants of one THCAS pseudogene on chemotype determination, emphasizing the necessity for precise genetic analysis for accurate categorization of cannabis varieties.

The Y chromosomal haplotype is expected to be identical (or close to, depending on the mutation rate) among a male and many of his paternal relatives. This means that often the same evidential value for the DNA evidence is obtained, whether the true donor or one of his close paternal relatives is compared to a crime sample. Commentators (see for example the UK Forensic Science Regulator or Amorim) have suggested to change the proposition pair to compare the probability of the evidence if the Person of Interest (POI) or one of his close paternal relatives left the DNA to the probability of the evidence if an unrelated male from the population left the DNA. We argue that this is problematic because there is no clear definition of close paternal relatives and truly unrelated males do not exist. Instead, we take a starting point in the traditional proposition pair “The source of the male DNA is the POI” versus “The source of the male DNA is not the POI” and make the latter one operational by suggesting that it is formulated as “The source of the male DNA is a random man from the population”. The issue of matching males in the POI's lineage is then addressed either in a comment in the statement or directly through a probability model.

As the court put forward higher requirements for quantitative evaluation and scientific standards of forensic evidence, how to objectively and scientifically express identification opinions has become a challenge for traditional forensic identification methods. Score-based likelihood ratios are mathematical methods for quantitative evaluation of forensic evidence. However, due to the subtle differences in inter-class barefootprints, there is no automatic barefootprints matching algorithm with high accuracy under large-scale dataset validation, and there are few studies related to deep learning barefootprint features for evidence evaluation in court. Therefore, score-based likelihood ratios for barefootprint evidence using deep learning features are proposed by this paper. Firstly, the largest barefootprint dataset (BFD) is constructed, which contains 54,118 barefootprint images from 3000 individuals. Then, an automatic barefootprint feature extraction and matching algorithm is proposed, which achieves a retrieval accuracy of 98.4% on BFD and an AUC of 0.989 for barefootprint validation. Next, Cosine distance, Euclidean distance and Manhattan distance are employed to measure the comparison scores between intra-class and inter-class barefootprints using deep learning features in four dimensions of 64, 128, 512 and 1024, respectively. The performance of proposed model is evaluated by comparing the $��C_{\mathrm{llr}}�$ values and the Tippett plot. Finally, simulated crime scene barefootprint samples are constructed to verify the practical application of the proposed method, which provide further support for the quantitative evaluation of barefootprint evidence in court.

The role of sickle cell trait (SCT) in sudden exertional death is well-recognized in sports and military training. However, it is not yet studied for non-firearm arrest-related death (NF-ARD). With extensive multi-pronged searches, a large database (n = 1389) of NF-ARDs was established. For the years 2006–2021 (inclusive) there were 50 NF-ARDs of Black persons in which postmortem evidence of SCT was found. A control cohort consisted of 414 NF-ARDs of Black persons with no reported SCT. The mean age for SCT cases was 33.1 ± 10.4 years versus 37.0 ± 10.4 years for the control group (p = 0.01). The body-mass index for SCT cases was 28.3 ± 6.6 kg/m² versus 30.7 ± 7.6 kg/m² for the control group (p = 0.03). The prevalence of cardiomegaly was 21% for SCT cases versus 39% in the control cohort (p = 0.008). The postmortem prevalence of SCT in NF-ARDs of Black persons (n = 50, 10.7%) was higher than the prevalence of SCT in the US Black population, which is 7.1% (p = 0.003). In this study of NF-ARDs in Black persons, the prevalence of SCT and the differences between the SCT cases and the control cohort suggest that exertional collapse associated with sickle cell trait may be a contributory factor in NF-ARDs.

The occurrences of various illegal activities on beaches require effective geological and environmental investigation methods. Among these methods, the room-temperature magnetic analysis of soils and sediments represents a nondestructive investigation method for various amounts, types, and grain sizes of magnetic minerals. Here, to verify the usefulness of magnetic analysis in forensic geology research, beach sediment samples from nine sites in the Shimokita Peninsula, Japan, were measured using magnetic analysis to determine the correlations between their concentration-dependent magnetic parameters and actual regional characteristics. The results revealed that the values of various parameters, namely the low-field magnetic susceptibility, anhysteretic remanent magnetization, and isothermal remanent magnetization (IRM), were relatively higher at sites near Ti and Fe sedimentary ore deposits. Further, thermomagnetometry results revealed that magnetite was the main magnetic carrier of the sediments. Moreover, pyrrhotite was detected around Ti–Fe mine sites. Furthermore, the results of the investigated parameters reflected the regional characteristics of the amount of magnetic minerals in the beach sediments. Low-temperature IRM curves and the magnetic grain size parameter also displayed sample-site-reflective characteristics. Thus, we believe that magnetic analysis represents an effective method for estimating the provenance of beach sediments in forensic geology research.

Ink analysis played an important role in document examination, but the limited dataset made it difficult for many algorithms to distinguish inks accurately. This article aimed to evaluate the feasibility of two data augmentation (DA) methods, Gaussian noise data augmentation (GNDA) and extended multiplicative signal augmentation (EMSA), for the classification of felt-tip pen ink brands. Four brands of felt-tip pens were analyzed using FT-IR spectroscopy. Five classification models were used, convolutional neural network (CNN), K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The results showed that the datasets generated by GNDA and EMSA are similar to the original datasets and have some diversity. The EMSA method had optimal classification results when combined with CNN, with classification accuracy (ACC), precision (PRE), recall (REC) and F1 score reaching 99.86%, 99.87%, 99.86%, 99.86%, and 99.86%, compared with GNDA-CNN method (ACC = 80.90%, PRE = 87.34%, REC = 81.62%, F1 score = 79.23%). This study shows that when raw spectral data is small, DA methods can be combined with neural network models to identify ink brands effectively.

A 77-year-old professional gold craftsman ingested a white powder used in goldsmithing, mistaking the powder for a health supplement. He detected a strange taste and immediately fell sick, reported the incident to 911, and was taken to the emergency room. He died approximately 8.5 h post-ingestion despite treatment. There were no significant findings in the autopsy, the victim's heart blood sample, gastric contents, and the white powder the victim had taken were submitted to the department of forensic toxicology. Using scanning electron microscopy energy dispersive X-ray analysis, potassium and gold (Au) were detected in the white powder. Ion chromatography analysis detected cyanide. Concentrations of cyanide were 0.5 mg/L in heart blood and 13.3 mg/L in gastric contents. Qualitative and quantitative analyses of Au in the heart blood sample and gastric contents using inductively coupled plasma-optical emission spectrometry detected concentrations of 79.8 mg/L and 2010.1 mg/L, respectively. Au and cyanide synergistically enhance cytotoxicity through inhibition of detoxification and increasing intracellular accumulation. In the present case, the detected blood cyanide concentration was sub or minimally lethal, and the blood Au concentration was high. The cause of the victim's death was the combined toxicity of Au and cyanide.

Surface-enhanced Raman spectroscopy (SERS) was utilized to measure low-level fentanyl concentrations mixed in common cutting agents, cocaine, 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, and caffeine. Mixtures were prepared with a fentanyl concentration range of 0–339 μM. Data was initially analyzed by plotting the area of a diagnostic peak (1026 cm⁻¹) against concentration to generate a calibration model. This method was successful with fentanyl/MDMA samples (LOD 0.04 μM) but not for the other mixtures. A chemometric approach was then employed. The data was evaluated using principal component analysis (PCA), partial least squares (PLS1) regression, and linear discriminant analysis (LDA). The LDA model was used to classify samples into one of three designated concentration ranges, low = 0–0.4 mM, medium = 0.4–14 mM, or high >14 mM, with fentanyl concentrations correctly classified with greater than 85% accuracy. This model was then validated using a series of “blind” fentanyl mixtures and these unknown samples were assigned to the correct concentration range with an accuracy >95%. The PLS1 model failed to provide accurate quantitative assignments for the samples but did provide an accurate prediction for the presence or absence of fentanyl. The combination of the two models enabled accurate quantitative assignment of fentanyl in binary mixtures. This work establishes a proof of concept, indicating a larger sample size could generate a more accurate model. It demonstrates that samples, containing variable, low concentrations of fentanyl, can be accurately quantified, using SERS.