ObjectiveThe accuracy of Y-chromosome haplogroup assignment is crucial for tracing paternal lineage in male samples. With the advancement of high-throughput sequencing technologies, high-density Y-SNP genotyping from whole-genome or array-based data has become a standard method for determiningY-chromosome haplogroups. This study systematically evaluated the performance of 4 commonly used high-density SNP genotyping systems—namely, the Global Screening Array (GSA), Chinese Genotyping Array (CGA), Affymetrix array, and the 1240K capture panel—for haplogroup assignment. This work provides a reference for data comparison across different systems. MethodsWe extracted genotype data for the 4 Y-SNP panels from 30× whole-genome sequencing (WGS) data of 1 590 male samples from the 1000 Genomes Project. Additionally, GSA array genotype data from 384 relative pairs (spanning 1st- to 12th-degree relationships) from 109 Chinese Han families were collected. Haplogroup assignment was performed using Y-LineageTracker v1.3.0 software. We assessed the concordance and resolution of haplogroup assignments between the four Y-SNP panels and the WGS data. The consistency and resolution of haplogroup assignments were also evaluated for both the 1000 Genomes Project samples and the 109 family samples collected in this study. Furthermore, the impact of varying numbers of Y-SNPs on haplogroup assignment was examined. ResultsThe GSA and CGA panels demonstrated superior resolution and discrimination of haplogroup subclades compared with the other two panels. The haplogroup assignments from the GSA, CGA, and 1240K panels showed high concordance with WGS data, with consistency rates exceeding 88.70%, whereas the Affymetrix platform exhibited a significantly lower consistency rate of 61.89%. Specifically, the GSA and CGA panels consistently demonstrated superior performance compared with the other two panels in the assignment of haplogroups O-M175 and H-L901, achieving complete concordance (100%) for both haplogroups. In contrast, the Affymetrix panel erroneously assigned all individuals belonging to haplogroup O-M175 to haplogroup K2-M526. Furthermore, its accuracy for haplogroup H-L901 was exceedingly low, at merely 1.41%. This poor performance was characterized by the misassignment of 98.59% of H-L901 samples—specifically, 1.41% to J-M304 and a predominant 97.18% to F-M89. For haplogroup R-M207, all four panels exhibited uniformly high levels of consistency, with concordance values exceeding 94.00%. Notably, for haplogroup E-M96, the 1240K and Affymetrix panels outperformed the GSA and CGA panels in terms of concordance, representing the first instance in which these two panels surpassed the latter. Conversely, for haplogroups J-M304, Q-M242, and I-M170, all 4 panels showed relatively elevated misclassification rates, with the Affymetrix array demonstrating the poorest overall performance. None of the four panels showed any discordant haplogroup assignments among the familial relative pairs analyzed. A positive correlation was observed between the number of Y-SNPs (ranging from 1 000 to 10 000) and classification consistency; however, classification consistency plateaued when the number of Y-SNPs exceeded 10 000. Furthermore, a random sampling analysis conducted on the GSA and CGA panels demonstrated that the haplogroup misclassification rate exhibited negligible fluctuation across the Y-SNP range of 500 to 1 000. Conversely, a marked enhancement in classification consistency was observed as the number of markers increased from 1 000 to 5 000, ultimately reaching a plateau within the interval of 5 000 to 8 000 markers. ConclusionThese findings indicate that the GSA and CGA panels provide high resolution and concordance, delivering reliable Y-haplogroup assignment for forensic investigations.

ObjectiveThe accuracy of Y-chromosome haplogroup assignment is crucial for tracing paternal lineage in male samples. With the advancement of high-throughput sequencing technologies, high-density Y-SNP genotyping from whole-genome or array-based data has become a standard method for determiningY-chromosome haplogroups. This study systematically evaluated the performance of 4 commonly used high-density SNP genotyping systems—namely, the Global Screening Array (GSA), Chinese Genotyping Array (CGA), Affymetrix array, and the 1240K capture panel—for haplogroup assignment. This work provides a reference for data comparison across different systems. MethodsWe extracted genotype data for the 4 Y-SNP panels from 30× whole-genome sequencing (WGS) data of 1 590 male samples from the 1000 Genomes Project. Additionally, GSA array genotype data from 384 relative pairs (spanning 1st- to 12th-degree relationships) from 109 Chinese Han families were collected. Haplogroup assignment was performed using Y-LineageTracker v1.3.0 software. We assessed the concordance and resolution of haplogroup assignments between the four Y-SNP panels and the WGS data. The consistency and resolution of haplogroup assignments were also evaluated for both the 1000 Genomes Project samples and the 109 family samples collected in this study. Furthermore, the impact of varying numbers of Y-SNPs on haplogroup assignment was examined. ResultsThe GSA and CGA panels demonstrated superior resolution and discrimination of haplogroup subclades compared with the other two panels. The haplogroup assignments from the GSA, CGA, and 1240K panels showed high concordance with WGS data, with consistency rates exceeding 88.70%, whereas the Affymetrix platform exhibited a significantly lower consistency rate of 61.89%. Specifically, the GSA and CGA panels consistently demonstrated superior performance compared with the other two panels in the assignment of haplogroups O-M175 and H-L901, achieving complete concordance (100%) for both haplogroups. In contrast, the Affymetrix panel erroneously assigned all individuals belonging to haplogroup O-M175 to haplogroup K2-M526. Furthermore, its accuracy for haplogroup H-L901 was exceedingly low, at merely 1.41%. This poor performance was characterized by the misassignment of 98.59% of H-L901 samples—specifically, 1.41% to J-M304 and a predominant 97.18% to F-M89. For haplogroup R-M207, all four panels exhibited uniformly high levels of consistency, with concordance values exceeding 94.00%. Notably, for haplogroup E-M96, the 1240K and Affymetrix panels outperformed the GSA and CGA panels in terms of concordance, representing the first instance in which these two panels surpassed the latter. Conversely, for haplogroups J-M304, Q-M242, and I-M170, all 4 panels showed relatively elevated misclassification rates, with the Affymetrix array demonstrating the poorest overall performance. None of the four panels showed any discordant haplogroup assignments among the familial relative pairs analyzed. A positive correlation was observed between the number of Y-SNPs (ranging from 1 000 to 10 000) and classification consistency; however, classification consistency plateaued when the number of Y-SNPs exceeded 10 000. Furthermore, a random sampling analysis conducted on the GSA and CGA panels demonstrated that the haplogroup misclassification rate exhibited negligible fluctuation across the Y-SNP range of 500 to 1 000. Conversely, a marked enhancement in classification consistency was observed as the number of markers increased from 1 000 to 5 000, ultimately reaching a plateau within the interval of 5 000 to 8 000 markers. ConclusionThese findings indicate that the GSA and CGA panels provide high resolution and concordance, delivering reliable Y-haplogroup assignment for forensic investigations.

ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

The International Severe Asthma Registry (ISAR) was established in 2017 to advance the understanding of severe asthma and its management, thereby improving patient care worldwide. As the first global registry for adults with severe asthma, ISAR enabled individual registries to standardize and pool their data, creating a comprehensive, harmonized dataset with sufficient statistical power to address key research questions and knowledge gaps. Today, ISAR is the largest repository of real-world data on severe asthma, curating data on nearly 35,000 patients from 28 countries worldwide, and has become a leading contributor to severe asthma research. Research using ISAR data has provided valuable insights on the characteristics of severe asthma, its burdens and risk factors, real-world treatment effectiveness, and barriers to specialist care, which are collectively informing improved asthma management. Besides changing clinical thinking via research, ISAR aims to advance real-world practice through initiatives that improve registry data quality and severe asthma care. In 2024, ISAR refined essential research variables to enhance data quality and launched a web-based data acquisition and reporting system (QISAR), which integrates data collection with clinical consultations and enables longitudinal data tracking at patient, center, and population levels. Quality improvement priorities include collecting standardized data during consultations and tracking and optimizing patient journeys via QISAR and integrating primary/secondary care pathways to expedite specialist severe asthma management and facilitate clinical trial recruitment. ISAR envisions a future in which timely specialist referral and initiation of biologic therapy can obviate long-term systemic corticosteroid use and enable more patients to achieve remission.

BACKGROUND:Endometrial receptivity is a key factor in embryo implantation in northwest Tibetan cashmere goats,and the expression of genes related to endometrial receptivity and their variable splicing are still unclear. OBJECTIVE:To analyze and explore genes and variable splicing events related to endometrial receptivity in northwest Tibetan cashmere goats. METHODS:On days 5 and 15 of pregnancy(representing pre receptive endometrium group and receptive endometrium group),three northwest Tibetan cashmere goats were randomly selected.Endometrial tissue was collected and stained with hematoxylin and eosin to observe tissue morphology.Immunohistochemical staining was used to detect the expression of endometrial receptive marker proteins leukemia inhibitory factor and vascular endothelial growth factor.After the total RNA was extracted and the quality test was qualified,transcriptome sequencing was performed to search differentially expressed mRNAs,lncRNAs,circRNAs,and miRNAs,perform functional prediction,and analyze alternative splicing mRNAs and lncRNAs related to endometrial receptivity. RESULTS AND CONCLUSION:(1)Compared with the pre receptive endometrium group,the expression levels of leukemia inhibitory factor and vascular endothelial growth factor proteins in the endometrial tissue of the receptive endometrium group were significantly increased.(2)The sequencing results showed that the differentially expressed genes were mostly mRNA and lncRNA genes,including 250 upregulated mRNAs,193 upregulated lncRNAs,135 downregulated mRNAs,and 123 downregulated lncRNAs,which were significantly enriched in the Wnt,Hedgehog,and Hippo signaling pathways.(3)Alternative splicing event analysis uncovered 8 differentially expressed variable splicing transcripts,which were all mRNA transcripts,including 2 downregulated and 6 upregulated,and were significantly associated with vascular endothelial growth factor receptor signaling,cell motility,and embryonic development.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective To construct an evaluation model for the use of large-scale medical equipment based on the analytic hierarchy process(AHP).Methods The indicators of the evaluation model were determined with considerations on the req-uirements of Accreditation Standard for Tertiary Hospitals(2022 edition),performance assessment standards of municipal departments for large-scale medical equipment in medical institutions over the years,the present situation of medical insti-tutions,key indexes for performance evaluation and the basic operational efficiency of the equipment.The evaluation model was established by calculating the weights of the indicators with AHP and performing the consistency test.The utilization of four CT devices in some hospital in a certain year was used as an example to comprehensively evaluate the rationality and feasibility of the model.Results There were 5 first-level indicators and 14 second-level indicators involved in the large-scale medical equipment usage evaluation model.The first-level indicators were made up of the usage,social benefits,functional utilization,economic benefits,scientific research and teaching benefits,with the weights of 45.156%,21.090%,12.113%,15.983%,and 5.657%,respectively,with all the first-level and second-level indicators passing the consistency test.The analysis of levels indicator weights showed the indicators affecting the operational efficiency included positive rate,expected work rate,utilization rate,etc.Comprehensive evaluation indicated the model was consistent with the traditional evaluation modes when used for ranking the equipment usage.Conclusion The AHP-based large-scale medical equipment usage evaluation model with high practicality facilitates the decision of medical institutions for the utilization,allocation and management of specialized medical equipment.[Chinese Medical Equipment Journal,2025,46(8):86-90]

Objective By analyzing the gene expression data in TCGA-THCA dataset,the differentially expressed genes related to thyroid carcinoma(THCA)were identified,a risk model was construct,and the role of CCL chemokine family in the development of THCA and its effect on the tumor immune microenvironment were investigated.Methods TCGA-THCA dataset was downloaded and analyzed,including 512 cancer samples and 59 adjacent tissue samples.The differentially expressed genes were obtained by taking the intersection of R packages of differential analysis,and the principal component analysis(PCA),Gene Ontology(GO),and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were performed.CellMarker,monocle,and CellChat were used to explore the Cell atlas,pseudotemporal trajectory,and cellular communication.Results A total of 698 up-regulated genes and 285 down-regulated genes were identified.PCA showed that there were significant differences in gene expression patterns between the THCA group and the normal group.GO and KEGG enrichment analysis revealed that differentially expressed genes were mainly involved in biological processes such as extracellular matrix tissue,cell junction assembly,and complement system.The results of risk model showed that the CCL genes was significantly correlated with poor prognosis(P=0.005).Single cell analysis revealed that fibroblasts played a key role in the development of THCA,and the expression of CCL gene was significantly up-regulated in fibroblasts(P<0.05).The results of pseudotemporal trajectory analysis showed that CCL gene was differentially expressed during cellular differentiation;the results of cellular communication analysis showed that the interaction between fibroblasts and endothelial cells is crucial in the tumor microenvironment.Conclusion The expression of CCL chemokine family in THCA is closely related to poor prognosis,especially CCL4,which activates the downstream signaling pathways by interacts with CCR receptors of cancer-associated fibroblasts,thereby promoting the development of cancer.These findings provide novel insights for understanding the role of CCL chemokine family in THCA and its regulation of the tumor immune microenvironment.

Aim To explore the action mechanism of isoquercitrin(IQ)in ameliorating anxiety based on network pharmacology,cellular transcriptomics,molecu-lar docking and animal experiments.Methods The common targets of anxiety disorders and IQ were ob-tained by using relevant databases.The protein-protein interaction network,the biological function and signa-ling pathway enrichment analysis were conducted by u-sing the common targets.Primary hippocampal neurons were cultured in vitro,and corticosterone was added to induce neurons to establish a corticosterone injury mod-el.IQ treatment was added to the culture system,and transcriptomics was used to screen for differentially ex-pressed genes and enrich for differentially expressed pathways.Subsequently,the results were validated by quantitative real-time polymerase chain reaction(qRT-PCR).Possible targets and signaling pathways for IQ treatment on anxiety were speculated and screened u-sing network pharmacology,transcriptomics and molec-ular docking.The anxiety rat model was constructed,and the anxiety state of rats was evaluated after IQ in-tervention,and the protein expression level of hippo-campus was detected to verify the relevant mechanism.Results Network pharmacology,cellular transcrip-tome,and molecular docking analyses revealed that the key mechanism of IQ for anxiety may be related to the BDKRB2/PI3K/Akt signaling pathway.Animal exper-iments showed that IQ was effective in improving anxie-ty behaviour and learning memory ability in rats.IQ increased the movement distance and residence time in the central area of the open field,the time and number percentage of entries into the open arm in the elevated plus maze,and the spontaneous alternations score in the Y maze in rats,and significantly elevated protein expression of BDKRB2,PI3K,Akt and decreased pro-tein expression of NF-κB in the hippocampus.Conclu-sions Isoquercitrin can effectively treat anxiety,and the mechanism of action may be related to the regula-tion of BDKRB2/PI3K/Akt signaling pathway in hip-pocampus.

Objective:To analyze the factors associated with long-term survival after radical resection of hepatitis B virus(HBV)-associated hepatocellular carcinoma(HCC),and to construct random forest and Cox regression models,to evaluate the two models.Methods:A total of 368 patients with HBV-infected HCC who underwent radical resection were selected retrospectively.These patients were categorized as having a good prognosis(n=266)or a poor prognosis(n=102)based on their survival and mortality status.Univariate and Cox regression analysis were used to identify fac-tors that predict poor prognosis in HCC patients after surgery,and Cox regression and random forest prediction models were constructed and evaluated.Results:There were significant differences in smoking history,Child-Pugh classifica-tion,cirrhosis,microvascular invasion,TNM staging,tumor capsule integrity,platelet-to-lymphocyte ratio(PLR),regular antiviral therapy,HBV-DNA load,alpha-fetoprotein(AFP),neutrophil-to-lymphocyte ratio(NLR),systemic immune in-flammatory index(SII),and albumin-to-globulin ratio(AGR)between the two groups(P<0.05);Cox regression showed that cirrhosis,microvascular invasion,regular antiviral treatment,HBV-DNA load,NLR,PLR,SII,and AGR were related factors that negatively affected the prognosis of patients with HBV-infected HCC after surgery(P<0.05),with an AUC of 0.870 for predicting prognosis;the importance ranking obtained by the random forest model was HBV-DNA load,cirrho-sis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII,with an AUC of 0.926 for predicting prog-nosis;the AUC predicted by the random forest model was greater than that predicted by the Cox regression model(Z=2.411,P=0.016).Conclusion:HBV-DNA load,cirrhosis,regular antiviral therapy,microvascular invasion,NLR,PLR,AGR,and SII are factors that affect the poor prognosis of patients with HBV-related HCC after surgery.The random for-est prediction model constructed based on these factors has high predictive value and is superior to the Cox regression prediction model.