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.

Objective To investigate plasma protein expression changes in heatstroke patients using proteomics technology and to identify reliable prognostic biomarkers.Methods A retrospective analysis was conducted on 20 heatstroke patients hospitalized at the 908th Hospital of the Chinese PLA Joint Logistics Support Force from July 2022 to February 2024.Patients were divided into survival(n=16)and death groups(n=4)based on 28-day outcomes.Fasting venous blood samples were collected from both groups for proteomic analysis.Liquid chromatography-mass spectrometry(LC-MS/MS)was used to identify and screen differentially expressed proteins.Kyoto Encyclopedia of Genes and Genomes(KEGG)and Gene Ontology(GO)enrichment analyses were performed.LASSO regression was applied to screen key prognostic biomarkers,and receiver operating characteristic(ROC)curve analysis was used to evaluate their predictive value.Results A total of 27 upregulated and 90 downregulated proteins were identified between the two groups.These proteins were primarily involved in biological processes such as immune responses,complement activation,and metabolic processes.LASSO regression analysis indicated that actin-related protein 2(ARP2),cysteine-rich scavenger receptor type 1 M130(CD163),and catalase(CAT)could serve as effective biomarkers for evaluating heatstroke prognosis.ROC curve analysis demonstrated that ARP2 had higher diagnostic efficacy(AUC=0.98,sensitivity=0.80,specificity=1.00)compared to CD163(AUC=0.94,sensitivity=0.76,specificity=1.00),CAT(AUC=0.96,sensitivity=0.67,specificity=1.00),and acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score(AUC=0.79,sensitivity=0.44,specificity=1.00).Conclusion Elevated plasma ARP2 level has good clinical value for predicting poor prognosis in heatstroke patients.

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

The leaves of sea buckthorn(Hippophae rhamnoides), considered as common food raw materials, have records of medicinal use and diverse pharmacological activities, showing a potential medicinal value. However, the active substances in the sea buckthorn leaves and their mechanisms of action remain unclear. In addition, due to the extensive source and large variety variations, the quality evaluation criteria of sea buckthorn leaves remain to be developed. To solve the problems, this study predicted the main active components, core targets, key pathways, and potential pharmacological effects of sea buckthorn leaves by network pharmacology and molecular docking. Furthermore, ultra-performance liquid chromatography with diode-array detection(UPLC-DAD) was employed to determine the content of active components and establish the chemical fingerprint, on the basis of which the quality markers of sea buckthorn leaves were predicted and then verified by the enzyme activity inhibition method. The results indicated that sea buckthorn leaves had potential therapeutic effects on a variety of digestive tract diseases, metabolic diseases, tumors, and autoimmune diseases, which were consistent with the ancient records and the results of modern pharmacological studies. The core targets of sea buckthorn leaves included PTPN11, AKT1, PIK3R1, ESR1, and SRC, which were mainly involved in the PI3K-AKT, MAPK, and HIF-1 signaling pathways. In conclusion, the active components of sea buckthorn leaves are associated with the rich flavonoids and tannins, among which quercitrin, narcissoside, and ellagic acid can be used as the quality markers of sea buckthorn leaves. The findings provide a reference for the quality control and further development and utilization of sea buckthorn leaves as medicinal materials.
Hippophae/chemistry* ; Network Pharmacology ; Molecular Docking Simulation ; Phosphatidylinositol 3-Kinases/metabolism* ; Flavonoids/analysis* ; Fruit/chemistry*

Aim To explore the mechanism of process¬ing and increasing efficiency of Arisaematis rhizomz preparatum. Methods UPLC-Q-TOF-MS/MS tech¬nology was used to detect the chemical components be¬fore and after processing of Arisaematis rhizomz prepara¬tum, and its mechanism of action was analysed in the treatment of 44 asthma and phlegm " by using network pharmacology. A rat model of allergic asthma was es- tablished to compare the efficacy of Arisaematis rliizoma before and after processing. Results A total of 27 chemical components were identified, among which cur- cumin ,6-gingerol and other components increased after processing. Combined with the database prediction, the action mechanism of the 36 chemical components in the treatment of 44 asthma and phlegm" diseases was dis¬cussed and predicted through network pharmacology. The results of animal experiments showed that the effect of processed Arisaematis rhizoma on allergic asth¬ma was better than that of Arisaematis rhizoma, but there was no significant difference. Conclusions The addition of curcumin, 6-gingerol, camphor, demethyl- curcumin and other components after the processed Ari¬saematis rhizomz preparatum may be the reason for the synergistic effect of Arisaematis rhizomz preparatum in the treatment of allergic asthma.

Medicinal plants have provided numerous medicinal active ingredients for thousands of years and these ingredients have been used in Chinese medicine (CM) and traditional pharmacologies worldwide. Recently, the exploitation and utilisation of medicinal plant resources has increased significantly. The results of the studies have led to the identification of many active components, such as steroidal alkaloids, saponins, terpenoids, and glycosides, in various medicinal plants with different evolutionary levels. Moreover, research on the chemical classification, molecular phylogeny, and pharmacological activity of medicinal plants is increasing in popularity. Pharmacophylogeny is an interdisciplinary topic that studies the correlation between plant phylogeny, chemical composition, and curative effects (pharmacological activity and the traditional curative effect) of medicinal plants. In addition, it provides the basic tools to enable research and development of CM resources. This literature review, based on the genetic relationship between phytogroup and species, highlights the formation process, research content, applications, and future directions of pharmacophylogeny.
Alkaloids ; Glycosides ; Plant Extracts/chemistry* ; Plants, Medicinal/chemistry* ; Saponins ; Terpenes

Objective: To evaluate the feasibility and effectiveness of the application of internet-based HIV testing in men who have sex with men (MSM) in practical application and provide evidence for its application in the future. Methods: MSM who visited the internet-based intervention platform for at least one time from June to December 2020 were selected for the study. The information about platform visit, the number of self-test kits provided, the basic characteristics of the MSM and their satisfactory level were collected. And multivariable logistic regression analyses were conducted to identify the potential factors associated with the reporting of self-test results. Results: By the end of December 31^th, 2020, a total of 132 267 platform visits had been recorded, and 3 511 HIV self-test kits had been provided upon the MSM's requests, and 3 237 MSM (92.2%) reported self-test results. The HIV positive rate was 2.4% (69/2 855) and the confirmation rate of positive HIV test results was 86.7% (52/60). The MSM who asked for self-test kits online were mainly aged ≤30 years, had education level of college or above, and found their sexual partners through internet or dating software. Multivariable logistic regression analyses showed that repeat of online HIV self-test kits application (OR=3.50,95%CI:2.10-5.83), guarantee deposit of 50 yuan at application (OR=2.55,95%CI:1.33-4.89), monthly economic income 1-3 000 yuan (OR=1.54,95%CI:1.05-2.28) or no income (OR=1.71,95%CI:1.20-2.42) and online sexual partners finding (OR=1.49,95%CI:1.13-1.95) were associated with higher reporting rate of self-test results. The satisfactory rate the MSM to the service of platform was 99.5% (217/218). Conclusions: The study confirmed the feasibility and effectiveness of internet-based intervention for HIV tests in MSM, which could promote the self-test of HIV in MSM and facilitate the early detection of HIV infection through social media platforms and multi-channel promotion.
Male ; Humans ; Homosexuality, Male ; HIV Infections/prevention & control* ; Feasibility Studies ; Sexual and Gender Minorities ; HIV Testing ; Internet

Objective:To investigate the relationship of triglyceride (TG), fasting blood glucose (FPG) and triglyceride glucose product index (TyG) with the incidence of hypertension, and provide basic data for the prevention and treatment of hypertension in the population.Methods:A total of 23 581 individuals who met the research criteria in Jinchang cohort were selected as the research subjects, the Cox proportional hazard model was used to analyze the relationship of TG, FPG, and TyG with the risk of hypertension. A stratified analysis was conducted by sex.Results:After adjusting for confounding factors, compared with the normal TG group, the HR(95% CI) of the elevated TG margin group and the elevated group were 1.16 (1.01-1.34) and 1.49 (1.30-1.70), respectively in the total population. Among men, they were 1.13 (1.01-1.27) and 1.17 (1.06-1.30), and among women, they were 1.05 (0.88-1.26) and 1.06 (0.88-1.28). Compared with the normal FPG group, the HR (95% CI) of the FPG-impaired group were 1.29 (1.13-1.48) in the total population, 1.26 (1.08-1.48) in men and 1.59 (1.14-2.21) in women. Taking the lowest quartile array as a reference, the HR (95% CI) of the highest quartile array of TyG was 1.73 (1.45-2.07) in the total population, 1.32 (1.14-1.53) in men and 1.87 (1.37-2.54) in women. TG, FPG had a nonlinear dose-response relationship with the risk of hypertension, while TyG had a linear correlation with the risk of hypertension. Conclusions:Higher TG, FPG, and TyG levels are independent risk factors for the incidence of hypertension. People with higher TG, FPG and TyG are at high risk for hypertension, to which close attention should be paid in the prevention and treatment of hypertension.

Objective:To explore the relationship between lipid indicators and the incidence of diabetes, and to compare the diabetes prediction and identification power of traditional lipid combined lipid indicators, in order to explore the best alternative indicators for identifying and predicting diabetes.Methods:Based on the Jinchang cohort, a nested case-control study was conducted in 1 025 new cases of diabetes after excluding patients with malignant tumor and related endocrine, circulatory system disease, then an age (±2 years), gender matched 1∶1 control group of 1 025 cases was set to analyze the relationship between the incidence of diabetes and lipid parameters.Results:Among the traditional lipid parameters, the fourth quartile of TG, TC, and LDL-C indicated higher risks of developing diabetes, which was 14.00 times (95% CI: 9.73-20.15), 2.15 times (95% CI: 1.65-2.79) and 1.66 times (95% CI: 1.29-2.14) than that of the first quartile, respectively. The risk of developing diabetes indicated by the fourth quartile of HDL-C was 0.21 times than that indicated by the first quartile (95% CI: 0.15-0.28). In the combined lipid parameters, the fourth quartile of TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C indicated higher risks of developing diabetes, which was 14.86 times (95% CI: 10.35-21.34), 8.12 times (95% CI: 5.94-11.01), 5.85 times (95% CI:4.34-7.88) and 5.20 times (95% CI: 3.85-7.03) than that indicated by the first quartile, respectively. The areas under the ROC curve of TG, TC, HDL-C, LDL-C, TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C were 0.76 (95% CI: 0.74-0.78), 0.59 (95% CI: 0.57-0.61), 0.67 (95% CI: 0.65-0.69), 0.57 (95% CI: 0.55-0.59), 0.77 (95% CI: 0.75-0.78), 0.73 (95% CI: 0.71-0.75), 0.69 (95% CI: 0.67-0.71) and 0.66 (95% CI: 0.64-0.68), respectively. The optimal diabetes predicting point cuts of TG, TC, HDL-C, LDL-C, TG/HDL-C, TC/HDL-C, LDL-C/HDL-C and non-HDL-C were 1.40, 4.70, 1.28, 3.25, 1.17, 3.43, 2.46, and 3.58 mmol/L, respectively. Conclusions:Lipid metabolic disorder is a risk factor for diabetes. TG and TG/HDL-C are the good lipid metabolism indicators for the prediction of diabetic.