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.

BACKGROUND: The American Heart Association recently released a new cardiovascular health (CVH) metric, Life's Essential 8 (LE8), for health promotion. However, the association between LE8 scores and the risk of chronic kidney disease (CKD) remains uncertain. We aimed to explore the association of LE8 scores with new-onset CKD and examine whether socioeconomic deprivation and genetic risk modify this association. METHODS: A total of 286,908 participants from UK Biobank and without prior CKD were included between 2006 and 2010. CVH was categorized using LE8 scores: low (LE8 scores <50), moderate (LE8 scores ≥50 but <80), and high (LE8 scores ≥80). The study outcome was new-onset CKD, ascertained by data linkage with primary care, hospital inpatient, and death data. Cox proportional hazard regression models were used to investigate the association between CVH categories and new-onset CKD. RESULTS: During a median follow-up of 12.5 years, 8857 (3.1%) participants developed new-onset CKD. Compared to the low CVH group, the moderate (adjusted hazards ratio [HR], 0.50; 95% confidence interval [CI]: 0.47-0.53) and high CVH (adjusted HR, 0.31; 95% CI: 0.27-0.34) groups had a significantly lower risk of developing new-onset CKD. The population-attributable risk associated with high vs. intermediate or low CVH scores was 40.3%. Participants who were least deprived ( vs.  most deprived; adjusted HR, 0.75; 95% CI: 0.71-0.79) and with low genetic risk of CKD ( vs.  high genetic risk; adjusted HR, 0.89; 95% CI: 0.85-0.94) had a significantly lower risk of developing new-onset CKD. However, socioeconomic deprivation and genetic risks of CKD did not significantly modify the relationship between LE8 scores and new-onset CKD (both P -interaction >0.05). CONCLUSION Achieving a higher LE8 score was associated with a lower risk of developing new-onset CKD, regardless of socioeconomic deprivation and genetic risks of CKD.
Humans ; Renal Insufficiency, Chronic/epidemiology* ; Male ; Female ; Middle Aged ; Genetic Predisposition to Disease/genetics* ; Aged ; Risk Factors ; Adult ; Proportional Hazards Models ; Socioeconomic Factors

Processing for deodorization is widely used in the production of animal-derived Chinese medicinal materials. In this study, Heracles Neo ultra-fast gas-phase electronic nose combined with chemometrics was employed to analyze the overall odor difference of Cervi Cornu Pantotrichum(focusing on that derived from Cervus nippon Temminck in this study) before and after processing. The results showed that the electronic nose effectively distinguished between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. HS-GC-MS was used to identify and quantify the volatile components in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum, and 35 and 37 volatile components were detected in the medicinal materials and decoction pieces, respectively. The medicinal materials and decoction pieces contained 28 common volatile components contributing to the odor of Cervi Cornu Pantotrichum. The odor activity value(OAV) of each volatile component was calculated based on the olfactory threshold and relative content. The results showed that there were 17 key odor substances such as isovaleraldehyde, 2-methylbutanal, isobutyraldehyde, hexanal, and methanethiol in the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. All of them had bad odor and were the main source of the odor of Cervi Cornu Pantotrichum. The results of principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) showed that there were significant differences in volatile components between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. Based on the thresholds of P<0.05 and Variable Importance in Projection(VIP)>1, 21 differential volatile odor components were screened out. Among them, isopentanol, isovaleraldehyde, 2-methylbutanal, n-nonanal, and dimethylamine were the key differential odor compounds between the medicinal materials and decoction pieces of Cervi Cornu Pantotrichum. The odor compounds and their relative content reduced, and some flavor substances such as esters were produced after processing with wine, which was the main reason for the reduction of the odor after processing of Cervi Cornu Pantotrichum.
Odorants/analysis* ; Electronic Nose ; Gas Chromatography-Mass Spectrometry/methods* ; Animals ; Volatile Organic Compounds/analysis* ; Deer ; Drugs, Chinese Herbal/chemistry*

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

BACKGROUND:Previous studies by our research group discovered that Jiawei Xiaoyao San has a significant anti-liver cancer effect,but the specific mechanism of action was unclear. OBJECTIVE:To investigate the regulatory effects of the traditional Chinese medicine formula Jiawei Xiaoyao San on the levels of miRNAs in plasma exosomes of rats with diethylnitrosamine chronically induced primary liver cancer,based on high-throughput sequencing combined with bioinformatics. METHODS:SD rats were randomly divided into a blank control group,a liver cancer model group,and a Jiawei Xiaoyao San treatment group.Liver cancer models were induced by continuous administration of diethylnitrosamine for 12 weeks.Starting from the 17th week,rats in the Jiawei Xiaoyao San treatment group were administered Jiawei Xiaoyao San once daily until the end of the 20th week,while rats in the blank control and liver cancer model groups were given an equivalent volume of saline.Anti-hepatocellular carcinoma effects were validated by assessing the morphological structure of rat liver tissues,along with the expression of the hepatocellular carcinoma markers,Glypican-3 protein and serum alpha-fetoprotein.Plasma exosomes from each group of rats were isolated using ultracentrifugation.High-throughput sequencing technology was used to screen for differentially expressed miRNAs in rat plasma exosomes.Bioinformatics was used to predict the potential biomarkers through which Jiawei Xiaoyao San exerts its anti-liver cancer effects via liver cancer-derived exosomal miRNAs,followed by functional analysis. RESULTS AND CONCLUSION:(1)Jiawei Xiaoyao San significantly improved the morphological structure of liver tissues in a rat model of liver cancer.Compared with the liver cancer model group,the expression of liver cancer markers Glypican-3 protein and serum alpha-fetoprotein was significantly reduced in the Jiawei Xiaoyao San treatment group.(2)Bioinformatics analysis showed that in the Jiawei Xiaoyao San group,upregulated miR-223-3p in the liver cancer model group had target binding sites with genes E2F1 and NCOA1,which were closely related to liver cancer survival and prognosis.Therefore,Jiawei Xiaoyao San has a therapeutic effect on liver cancer,possibly by targeting negative regulation of NCOA1/E2F1 through liver cancer plasma-derived exosomal miR-223-3p,thereby playing anti-liver cancer effect.

Objective:To develop a convolutional neural network model of whole slide imaging of cerebrospinal fluid cells for rapid and accurate identification and classification of tumor cells in cerebrospinal fluid.Methods:A total of 8 692 cerebrospinal fluid cytology smears from Huashan Hospital Affiliated to Fudan University from January 2nd, 2019, to December 27th, 2024. As randomly assigned, the training set included 4 941 benign and 1 745 malignant samples, while the validation set comprised of 1 368 benign and 638 malignant samples. Whole-slide digital images were acquired using a cytopathology scanner, cells (clusters) were annotated for classification, and a deep learning model was constructed via tiled image patches for cell detection and classification. Model performance was evaluated using accuracy, sensitivity, specificity, and other indicators. The classification efficiency of manual microscopy was compared.Results:The model achieved a mean precision of 96.75% for cerebrospinal fluid cell classification. For malignant tumor cells, the classification accuracy was 96.61% (mAP=98.36%, AUC=0.97). Subtype classification accuracies for epithelial/epithelioid tumors and small round cell tumors were 97.13% (AUC=0.98) and 95.58% (AUC=0.93), respectively. Compared with manual microscopy, which took (9.70±0.82) minutes for classifying 200 cells, (18.27±1.21) minutes for 500 cells, and often exceeded 60 minutes or infeasible for full slides, the AI model took (3.46±0.49) seconds for 200 cells, (6.76±0.82) seconds for 500 cells, and a median of 48.57 seconds for full slides ( P<0.001), representing an efficiency improvement of approximately 161-170 times, significantly enhancing diagnostic efficiency. Conclusion:This fully automated hierarchical deep learning model enables efficient and accurate tumor cell identification and classification in CSF, providing an effective auxiliary tool for the rapid diagnosis of meningeal carcinomatosis.

Objective To explore the genes related to the pathogenesis of polycystic ovary syndrome(PCOS)through bioinformatics methods and verify them in ovarian granulosa cells,providing a reference for screening potential molecular markers of PCOS.Methods The GSE34526 and GSE137684 datasets were re-spectively used as the training set and validation set.The inflammation-related genes potentially associated with the onset of PCOS were explored through differential expression analysis and weighted gene co-expres-sion network analysis.Subsequently,their predictive value was verified through the receiver operating charac-teristic(ROC)curve.Thirty patients with PCOS(the PCOS group)and 27 patients without PCOS(the con-trol group)who were treated in Affiliated Hospital of Youjiang Medical University for Nationalities were se-lected as the research objects.RT-qPCR was applied to verify the expression level of core genes in granular cells.Spearman correlation and multiple linear regression were used to analyze the correlation between the ex-pression level of core genes and various clinical parameters,and the ROC curve was used to analyze the diag-nostic efficacy of the expression level of core genes for PCOS.Finally,the protein expression level of core genes was verified on animal models.Results A total of 1 888 differentially expressed genes,89 module-relat-ed genes and 366 inflammatory response-related genes were identified.The core gene phosphatidylinositol-4,5-diphosphate 3-kinase catalytic subunit γ(PIK3CG)was ultimately obtained by taking co-expressed genes and verifying with the ROC curve.RT-qPCR results showed that the expression level of PIK3CG in granulosa cells of the PCOS group was higher than that of the control group(P<0.05).Spearman correlation analysis showed that the expression level of PIK3CG was positively correlated with BMI,testosterone(T),fasting in-sulin(FINS),and insulin resistance index(HOMA-IR,P<0.05).Multivariate linear regression analysis showed that FINS,HOMA-IR and BMI were increased with the increasing of PIK3CG expression level(P<0.05).ROC curve analysis showed that the area under the curve for diagnosing PCOS patients with PIK3CG mRNA expression levels in granulosa cells was 0.659 3.The immunohistochemical results showed that the ex-pression of PIK3CG protein in the ovarian tissues of mice in the PCOS group was significantly increased.Con-clusion PIK3CG may be potentially associated with the pathogenesis of PCOS.

OBJECTIVES: To evaluate and integrate evidence on the management of sleep disorders in children with attention deficit hyperactivity disorder (ADHD). METHODS: Literature was retrieved based on the 6S model, and evidence related to sleep disorder management in children with ADHD was extracted from the included references. RESULTS: A total of 17 studies were included, from which 16 pieces of evidence were extracted. Of these, 6 were classified as Level 1 evidence and 10 as Level 5. The evidence covered screening, assessment, non-pharmacological interventions, pharmacological interventions, follow-up, and multidisciplinary collaboration. CONCLUSIONS This study integrated evidence on the management of sleep disorders in children with ADHD using an evidence-based approach, providing an evidence-based foundation for managing sleep disorders in this population.
Humans ; Attention Deficit Disorder with Hyperactivity/complications* ; Sleep Wake Disorders/etiology* ; Child ; Evidence-Based Medicine

OBJECTIVE: To assess the cardioprotective effect and impact of Qishen Granules (QSG) on different ischemic areas of the myocardium in heart failure (HF) rats by evaluating its metabolic pattern, substrate utilization, and mechanistic modulation. METHODS: In vivo, echocardiography and histology were used to assess rat cardiac function; positron emission tomography was performed to assess the abundance of glucose metabolism in the ischemic border and remote areas of the heart; fatty acid metabolism and ATP production levels were assessed by hematologic and biochemical analyses. The above experiments evaluated the cardioprotective effect of QSG on left anterior descending ligation-induced HF in rats and the mode of energy metabolism modulation. In vitro, a hypoxia-induced H9C2 model was established, mitochondrial damage was evaluated by flow cytometry, and nuclear translocation of hypoxia-inducible factor-1 α (HIF-1 α) was observed by immunofluorescence to assess the mechanism of energy metabolism regulation by QSG in hypoxic and normoxia conditions. RESULTS: QSG regulated the pattern of glucose and fatty acid metabolism in the border and remote areas of the heart via the HIF-1 α pathway, and improved cardiac function in HF rats. Specifically, QSG promoted HIF-1 α expression and entry into the nucleus at high levels of hypoxia (P<0.05), thereby promoting increased compensatory glucose metabolism; while reducing nuclear accumulation of HIF-1 α at relatively low levels of hypoxia (P<0.05), promoting the increased lipid metabolism. CONCLUSIONS QSG regulates the protein stability of HIF-1 α, thereby coordinating energy supply balance between the ischemic border and remote areas of the myocardium. This alleviates the energy metabolism disorder caused by ischemic injury.
Animals ; Myocardial Infarction/physiopathology* ; Male ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Rats, Sprague-Dawley ; Glucose/metabolism* ; Drugs, Chinese Herbal/therapeutic use* ; Energy Metabolism/drug effects* ; Rats ; Fatty Acids/metabolism* ; Myocardium/pathology*