法医学杂志最新文献

Objectives: To explore a deep learning network model suitable for bone age estimation using shoulder joint X-ray images in Chinese Han adolescents.

Methods: A retrospective collection of 1 286 shoulder joint X-ray images of Chinese Han adolescents aged 12.0 to <18.0 years (708 males and 578 females) was conducted. Using random sampling, approximately 80% of the samples (1 032 cases) were selected as the training and validation sets for model learning, selection and optimization, and the other 20% samples (254 cases) were used as the test set to evaluate the model's generalization ability. The original single-channel shoulder joint X-ray images and dual-channel inputs combining original images with segmentation labels (manually annotated shoulder joint regions multiplied pixel-by-pixel with original images, followed by segmentation via the U-Net++ network to retain only key shoulder joint region information) were respectively input into four network models, namely VGG16, ResNet18, ResNet50 and DenseNet121 for bone age estimation. Additionally, manual bone age estimation was conducted on the test set data, and the results were compared with the four network models. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R²), and Pearson correlation coefficient (PCC) were used as main evaluation indicators.

Results: In the test set, the bone age estimation results of the four models with dual-channel input of shoulder joint X-ray images outperformed those with single-channel input in all four evaluation indicators. Among them, DenseNet121 with dual-channel input achieved best results with MAE of 0.54 years, RMSE of 0.82 years, R² of 0.76, and PCC (r) of 0.88. Manual estimation yielded an MAE of 0.82 years, ranking second only to dual-channel DenseNet121.

Conclusions: The DenseNet121 model with dual-channel input combined with original images and segmentation labels is superior to manual evaluation results, and can effectively estimate the bone age of Chinese Han adolescents.

Objectives: To detect the expression changes of citrullinated histone H3 (CitH3) during the development of deep vein thrombosis (DVT) in mice, and to explore its value in estimating the time to thrombosis.

Methods: The inferior vena cava (IVC) of mice was ligated to establish a thrombosis model induced by congestion. Mice were sacrificed under excessive anesthesia at 0 h, 1 d, 3 d, 5 d, 7 d, 10 d, 14 d and 21 d after the modeling, respectively. The congested IVC segments (0 h after modeling) and the thrombosed IVC segments (1-21 days after modeling) were extracted. Immunohistochemistry and double immunofluorescence staining were used to observe the number of neutrophils and the expression of CitH3 during thrombosis. Western blotting was used to detect the protein expression level of CitH3.

Results: During thrombosis, CitH3 was mainly expressed in neutrophils within the thrombus. A small number of neutrophils and a few CitH3-positive cells were observed at 0 h after modeling in the congested IVC. Between 1 d and 21 d after modeling, the number of neutrophils reached a peak at 1 d and gradually decreased. The number of CitH3-positive cells and their ratio to neutrophils began to increase at 1 d, reached a peak at 5 d after modeling, and then decreased. The expression level of CitH3 protein began to increase at 1 d and reached a peak at 5 d after modeling.

Conclusions: The expression of CitH3 during DVI shows temporal changes, and is expected to become a biological marker for estimating the formation time of thrombosis.

The inference of tissue origin of body fluid stains is crucial for case investigation and court proceedings. However, traditional methods for identification of body fluid stains, such as morphological, chemical, and immunoassay identifications have certain limitations, and there is an urgent need for more efficient methods for confirmatory experiments. In recent years, the rapid development of transcriptomics technology has provided new means for the identification of tissue origin of body fluid stains. Different types of RNA in the transcriptome have their own advantages. This paper elaborates in detail on the application of different types of RNA, such as mRNA, miRNA, circRNA, lncRNA, piRNA and microbial transcriptomics in body fluid identification, and summarizes their respective advantages and limitations, in order to provide a reference for related research.

Objectives: To investigate the genetic differences among different populations based on 13 autosomal STR loci in CODIS core.

Methods: Data of 13 autosomal STR loci (CSF1PO, FGA, THO1, TPOX, vWA, D3S1358, D5S818, D7S820, D8S1179, D13S317, D16S539, D18S51, D21S11) were collected from 95 populations in scientific journals between 1999 and 2021, soursed from the PubMed database, which had been published. Allele frequencies of loci were sorted out and forensic genetic parameters including gene differentiation coefficient (G_st), total heterozygosity (H_t), subpopulation heterozygosity (H_s) values, and Nei's DA genetic distance were calculated. Principal component analysis, phylogenetic tree, and multidimensional scale analysis were conducted to assess population genetic structure.

Results: A total of 265 alleles were detected at the 13 STR loci in these 95 populations. The mean values of G_st, H_t, and H_s were 0.023 247, 0.797 915 and 0.779 365. Population genetic analyses reflected significant differences among populations from Asia, Africa and Europe. In Asian populations, there was a certain degree of distinction between mainland and island populations; the Han population showed a certain degree of distinction with surrounding populations in mainland; while within the Han population, there were two distinct clusters formed by the northern Han and the southern Han.

Conclusions: The 13 autosomal STR loci in CODIS core demonstrate potential value for population identification across different groups, and may be used for the differentiation of ethnic groups, among different continental populations.

Objectives: To evaluate the forensic application value of used dental floss as a source of biological evidence for individual identification by analyzing the effects of dental floss sample collection methods, DNA extraction methods, preservation conditions, and sampling sites on the success rate of STR typing.

Methods: Dental floss samples were collected using three techniques: direct cutting, cotton swab wiping, and flocked swab wiping, respectively. DNA was extracted respectively by the Chelex, spin column-based and magnetic bead-based methods. DNA quantification and STR typing were performed using the Qubit kit and FGI HumDNA Typing kit (Platinum), respectively. Storage environments (temperature and humidity, ultraviolet radiation) and sampling locations (the floss part, the handle part) on DNA quantity and STR typing were evaluated.

Results: Through conducting a statistical analysis of three key indicators of average DNA mass concentration, STR locus detection rate, and typing accuracy rate, the direct cutting method demonstrated the highest efficacy, followed by cotton swab wiping mothed, and the flocked swab wiping method had the lowest efficacy. Direct cutting yielded an average DNA mass concentration greater than (4.94±1.87) ng/μL, with STR locus detection and accuracy rates of 100%. Bead-based DNA extraction method produced superior DNA concentration and quality compared to spin column-based and Chelex methods, regardless of whether the sampling technique used. Preservation conditions had a significant impact on the DNA analysis of samples. Particularly, the STR typing accuracy of samples preserved at 55 ℃/50%RH for 35 days dropped to (81.82±12.31)%, and that of samples exposed to ultraviolet radiation for 12 h dropped to (55.46±34.31)%. DNA concentration from the handle part of dental floss was extremely low, with an STR typing accuracy of only (30.91±27.35)%.

Conclusions: Using cotton swabs to wipe or directly cutting the thread of dental floss samples, and combining this approach with the magnetic bead method for DNA extraction, can best guarantee the concentration and quality of DNA. In addition, samples should be stored in low-temperature, low-humidity environment, protected from light and ultraviolet radiation.

At present, the drug substitutes represented by new psychoactive substances are gradually becoming popular, leading to an increasing demand for identifying novel drugs with unknown structures in drug investigation. Nuclear magnetic resonance (NMR) spectroscopy is an important tool for analyzing molecular structures. In the absence of standard substances, quantitative NMR (qNMR) can undertake the quantitative analysis of target substances in complex mixtures and has unique advantages in the research of new drugs and their precursor drugs. Due to the limitations of the site and maintenance costs, as well as relatively complex operation, high-field superconducting NMR is less commonly applied in drug research. The desktop low‑field NMR developed in recent years provides a new alternative solution. Due to the use of permanent magnets, its size is reduced, and the operation and maintenance costs are lowered. It has been widely used in various research fields. This article reviews the development of low-field NMR technology, summarizes the application of desktop low-field NMR in screening and identification of suspicious substances, rapid content determination, analysis of drug manufacturing processes and synthetic routes, and correlation traceability. It also looks forward to the prospects and development directions of this technology in drug research, aiming to provide a reference for researchers who work in analytical chemistry and drug research.