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.

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

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 investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

AIM To investigate the inhibitory effect of gallic acid on HCC1806 triple-negative breast cancer xenograft growth in nude mice.METHODS The nude mouse models of HCC1806 triple-negative breast cancer xenograft growth were established and randomly divided into the model group,the gallic acid group(200 mg/kg)and the adriamycin group(5 mg/kg),with 6 mice in each group.After 21 days of respective drug administration,the nude mice were killed and had their xenografts procured and measured.The nude mice had their serum levels of IL-6 and TNF-α detected by ELISA;their morphological changes of xenografts observed with HE staining;their protein expressions of p53,PCNA and Ki67 in xenograft detected by immunohistochemistry;their mRNA expressions of PCNA,Ki67,Bcl-2,Bax,Caspase-3,PI3K,Akt,JNK and p38 detected by RT-qPCR;and their protein expressions of Bcl-2,Bax,Caspase-3,cleaved-Caspase-3,PI3K,p-PI3K,Akt,p-Akt,JNK,p-JNK,p38 MAPK and p-p38 MAPK in xenografts detected by Western blot.RESULTS Compared with the model group,the groups intervened with either gallic acid or adriamycin shared decreased tumor weight(P＜0.05);increased serum levels of IL-6 and TNF-α(P＜0.05,P＜0.01);condensed or irregular nuclei of xenograft,and increased number of cell necrosis;increased protein expressions of p53 and Bax and the ratios of cleaved-Caspase-3/Caspase-3,p-JNK/JNK and p-p38/p38 in xenograft(P＜0.01);decreased protein expressions of PCNA,Ki67 and Bcl-2 and the ratios of p-PI3K/PI3K and p-Akt/Akt(P＜0.01);decreased mRNA expressions of PCNA,Ki67,Bcl-2,PI3K and Akt(P＜0.05,P＜0.01);and increased mRNA expressions of Caspase-3,Bax,JNK and p38(P＜0.05,P＜0.01).CONCLUSION Gallic acid can inhibit HCC1806 triple-negative breast cancer xenograft growth in nude mice,and the underlying mechanism may involve suppression of pro-cell proliferation proteins,activation of pro-apoptotic proteins,and modulation of the PI3K/Akt and JNK/p38 signaling pathways.

AIM To investigate the inhibitory effect of gallic acid on HCC1806 triple-negative breast cancer xenograft growth in nude mice.METHODS The nude mouse models of HCC1806 triple-negative breast cancer xenograft growth were established and randomly divided into the model group,the gallic acid group(200 mg/kg)and the adriamycin group(5 mg/kg),with 6 mice in each group.After 21 days of respective drug administration,the nude mice were killed and had their xenografts procured and measured.The nude mice had their serum levels of IL-6 and TNF-α detected by ELISA;their morphological changes of xenografts observed with HE staining;their protein expressions of p53,PCNA and Ki67 in xenograft detected by immunohistochemistry;their mRNA expressions of PCNA,Ki67,Bcl-2,Bax,Caspase-3,PI3K,Akt,JNK and p38 detected by RT-qPCR;and their protein expressions of Bcl-2,Bax,Caspase-3,cleaved-Caspase-3,PI3K,p-PI3K,Akt,p-Akt,JNK,p-JNK,p38 MAPK and p-p38 MAPK in xenografts detected by Western blot.RESULTS Compared with the model group,the groups intervened with either gallic acid or adriamycin shared decreased tumor weight(P＜0.05);increased serum levels of IL-6 and TNF-α(P＜0.05,P＜0.01);condensed or irregular nuclei of xenograft,and increased number of cell necrosis;increased protein expressions of p53 and Bax and the ratios of cleaved-Caspase-3/Caspase-3,p-JNK/JNK and p-p38/p38 in xenograft(P＜0.01);decreased protein expressions of PCNA,Ki67 and Bcl-2 and the ratios of p-PI3K/PI3K and p-Akt/Akt(P＜0.01);decreased mRNA expressions of PCNA,Ki67,Bcl-2,PI3K and Akt(P＜0.05,P＜0.01);and increased mRNA expressions of Caspase-3,Bax,JNK and p38(P＜0.05,P＜0.01).CONCLUSION Gallic acid can inhibit HCC1806 triple-negative breast cancer xenograft growth in nude mice,and the underlying mechanism may involve suppression of pro-cell proliferation proteins,activation of pro-apoptotic proteins,and modulation of the PI3K/Akt and JNK/p38 signaling pathways.

Further evidence is needed to explore the impact of high-altitude environments on the neurologic function of neonates.Non-invasive techniques such as cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography can provide data on cerebral oxygenation and brain electrical activity.This study will conduct multiple cerebral near-infrared spectroscopy and amplitude-integrated electroencephalography monitoring sessions at various time points within the first 3 days postpartum for healthy full-term neonates at different altitudes.The obtained data on cerebral oxygenation and brain electrical activity will be compared between different altitudes,and corresponding reference ranges will be established.The study involves 6 participating centers in the Chinese High Altitude Neonatal Medicine Alliance,with altitude gradients divided into 4 categories:800 m,1 900 m,2 400 m,and 3 500 m,with an anticipated sample size of 170 neonates per altitude gradient.This multicenter prospective cohort study aims to provide evidence supporting the impact of high-altitude environments on early brain function and metabolism in neonates.[Chinese Journal of Contemporary Pediatrics,2024,26(4):403-409]

Redox-sensitive cysteine(RSC)thiol plays an important role in many biological processes such as photosynthesis,cellular metabolism,and transcription.Therefore,it is necessary to identify red-ox-sensitive cysteine accurately.However,traditional redox-sensitive cysteine identification is very ex-pensive and time-consuming.At present,there is an urgent need for a mathematical calculation method to identify sequence information and redox-sensitive cysteines quickly and accurately.Here,we devel-oped an effective predictor called iRSC-PseAAC,which used the dimension reduction algorithm LDA combined with the support vector machine to predict redox-sensitive cysteine sites.In the cross-validation results,the specificity(Sp),sensitivity(Sn),accuracy(Acc)and Matthews correlation coefficient(MCC)were 0.841,0.868,0.859 and 0.692 respectively.In the independent dataset results,the Sp,Sn,Acc and MCC were 0.906,0.882,0.890 and 0.767 respectively.compared with existing prediction methods,iRSC-PseAAC had obvious improvement effect.The method proposed for this study can also be used for many problems in computational proteomics.