ObjectiveTo elucidate the chemical composition of the reference sample of Jinshui Liujunjian and its distribution characteristics in blood and tissues of rats. MethodsUltra performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry（UPLC-Q-TOF-MS/MS） was used to detect the reference sample solution， plasma， and tissue samples of Jinshui Liujunjian under positive and negative ion modes， respectively. Qualitative Analysis 10.0 software and a self-constructed database were employed for primary mass spectrum matching.Compound identification was further validated by comparing retention times， secondary mass spectral fragments， reference standards， and literature data to deduce fragmentation pathways. ResultsA total of 122 compounds were identified in the reference sample of Jinshui Liujunjian， including 47 flavonoids， 5 amino acids， 13 iridoids， 16 triterpenoid saponins， etc.， of which 42 compounds were confirmed by comparison with reference substances. A total of 21 prototype components were identified in blood components； 50 prototype components were identified in different tissues， among which 13， 10， 7， 21， 11， 6， 14， and 40 prototype components were identified in the heart， liver， spleen， lung， kidney， brain， large intestine， and stomach， respectively. Among them， 7 compounds such as ferulic acid， glycyrrhizic acid， and nobiletin were exposed in the target organs of lung and kidney. ConclusionThis study elucidates the material basis of the reference samples of Jinshui Liujunjian， primarily composed of flavonoids and triterpenoid saponins， along with their in vivo distribution characteristics. These findings provide a scientific basis for establishing quality evaluation indicators and offer references for subsequent pharmacodynamic and pharmacokinetic investigations.

ObjectiveTo elucidate the chemical composition of the reference sample of Jinshui Liujunjian and its distribution characteristics in blood and tissues of rats. MethodsUltra performance liquid chromatography-quadrupole-time-of-flight tandem mass spectrometry（UPLC-Q-TOF-MS/MS） was used to detect the reference sample solution， plasma， and tissue samples of Jinshui Liujunjian under positive and negative ion modes， respectively. Qualitative Analysis 10.0 software and a self-constructed database were employed for primary mass spectrum matching.Compound identification was further validated by comparing retention times， secondary mass spectral fragments， reference standards， and literature data to deduce fragmentation pathways. ResultsA total of 122 compounds were identified in the reference sample of Jinshui Liujunjian， including 47 flavonoids， 5 amino acids， 13 iridoids， 16 triterpenoid saponins， etc.， of which 42 compounds were confirmed by comparison with reference substances. A total of 21 prototype components were identified in blood components； 50 prototype components were identified in different tissues， among which 13， 10， 7， 21， 11， 6， 14， and 40 prototype components were identified in the heart， liver， spleen， lung， kidney， brain， large intestine， and stomach， respectively. Among them， 7 compounds such as ferulic acid， glycyrrhizic acid， and nobiletin were exposed in the target organs of lung and kidney. ConclusionThis study elucidates the material basis of the reference samples of Jinshui Liujunjian， primarily composed of flavonoids and triterpenoid saponins， along with their in vivo distribution characteristics. These findings provide a scientific basis for establishing quality evaluation indicators and offer references for subsequent pharmacodynamic and pharmacokinetic 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 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 Long working hours, as a common risk factor for occupational stress, is closely related to the occurrence of depressive symptoms. Understanding how long working hours affect occupational stress and depressive symptoms will inform occupational health interventions. Objective To quantify the impact of long working hours exposure on occupational stress and depressive symptoms among Internet industry employees, translate black-box outputs into actionable insights, and demonstrate the value of interpretable machine learning for early-warning occupational-health surveillance. Methods A dataset was derived from a cross-sectional survey involving 2866 internet industry employees in China. This survey was part of the project Risk Assessment Of Long Working Hour Exposure And Its Adverse Health Effects, conducted by the National Institute for Occupational Health and Poisoning Control, Chinese Center for Disease Control and Prevention, from 2021 to 2023. Working hours, occupational stress and depressive symptoms were quantified with a set of structured questionnaires including the Core Occupational Stress Scale and the Patient Health Questionnaire. Pairwise associations were screened by Mantel tests and variance-inflation factors. Key predictors identified through feature selection were fed into six machine-learning risk-prediction models. Visual interpretation was provided by feature importance, Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), while directed causal effects and intervention impacts of prolonged working hours exposure on occupational stress and depressive symptoms were dissected with causal explanation of features techniques. Results The positive rates of occupational stress and depressive symptoms among internet employees were 12.9% and 77.8% respectively. Twelve core features for occupational stress and nine for depressive symptoms were retained after selection. After these features were supplied to six predictive algorithms and evaluated on five metrics, the Light Gradient Boosting Machine (LGBM) achieved the highest accuracy—0.89 for occupational stress and 0.79 for depressive symptoms on the hold-out test set. The feature-importance rankings converged on fatigue accumulation and life satisfaction as dominant drivers for both outcomes, whereas weekly working hours and daily overtime emerged as the principal exposure-related predictors. The SHAP summary plots revealed that longer weekly hours and daily overtime systematically elevated the probability of occupational stress. The causal feature explanation further quantified that ascending one category in weekly working hours increased the probability of occupational stress by 7.04%. Conclusion Exposure to long working hours is associated with both occupational stress and depressive symptoms among internet industry employees. Interpretable machine-learning frameworks translate these associations into transparent, defensible drivers, enabling precise identification of the pivotal factors and their interplay. This evidence base equips occupational-health practitioners with actionable insights for designing targeted prevention and intervention strategies.

BACKGROUND:A large number of studies have confirmed that the therapeutic effectiveness of mesenchymal stem cell secretome is comparable to that of mesenchymal stem cells,but the mechanism of its action is still unclear. OBJECTIVE:To summarize the research progress of mesenchymal stem cell secretome in recent years,to investigate the molecular mechanism of its therapeutic effect,to analyze the current problems and to look forward to the future development. METHODS:The terms"exosomes,mesenchymal stem cells secrete,extracellular vesicles,mesenchymal stem cells,mechanism"were used as English search terms in the PubMed database.Articles that were not related to the research purpose of the article and duplicated articles were excluded.At the same time,we combined the method of literature tracking.Finally,109 articles that met the criteria were incuded for the review. RESULTS AND CONCLUSION:(1)The mesenchymal stem cell secretome promotes tissue repair and regeneration through delivering genetic material,immunomodulatory factors,growth factors,etc.to target cells,by activating anti-apoptotic,regulating angiogenesis,modulating fibrosis and pro-survival pathways in target cells.(2)The potential of mesenchymal stem cell secretome in disease therapy has also been confirmed.Numerous research results have shown that mesenchymal stem cell secretome can be used as a new cell-free treatment for inflammatory and degenerative diseases.(3)Mesenchymal stem cell secretome has been engineered to have more efficient therapeutic effects in recent years.However,due to the heterogeneity of the mesenchymal stem cell secretome and the complexity of its components,the exact mechanism of its therapeutic effect is still unclear.(4)At present,further research is needed to identify the key targets of mesenchymal stem cell secretome,and innovative specific and enhanced mesenchymal stem cell secretome should be developed by combining with engineering and genetic engineering technologies in the future.

Background Occupational stress has emerged as a critical public health concern affecting the physical and mental well-being of workers in the manufacturing sector. However, researchers typically evaluate its core driver—cumulative fatigue—using a crude binary “present/absent” variable, thereby overlooking the high-dimensional complexity and heterogeneity inherent in fatigue characteristics. This oversimplification constrains both the precision and predictive performance of occupational stress risk assessment model. Objective Leveraging a data-driven approach, to survey data on cumulative fatigue among manufacturing employees, and then use this new classification to develop and validate an occupational stress prediction model, with an ultimate aim of enhancing the accuracy and effectiveness of occupational stress assessment. Methods A set of cross-sectional survey data on 3871 manufacturing employees in 2021 were derived from the “Long working hours exposure and its adverse health effect risk assessment” program of the National Institute for Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention. Occupational stress was assessed using the Core Occupational Stress Measurement Scale, while cumulative fatigue was evaluated via the Worker’s Self-Diagnostic Questionnaire for Fatigue Accumulation. Boruta method was applied to screen core variables from 20 occupational stress influencing factors. Dimensionality reduction of cumulative fatigue features was performed using a Bayesian sparse autoencoder (BASE), followed by comparison and application of clustering methods to achieve multi-class classification of the reduced features. Six machine learning classification models were then selected including logistic regression, support vector machine, decision tree, random forest, adaptive boosting, and lightweight gradient boosting machine (LightGBM) to construct and compare two occupational stress prediction models involving cumulative fatigue combined with ten other core variables: one utilizing the original binary cumulative fatigue label as a core variable, and another employing the novel fatigue classification proposed herein as a core variable. Results The positive rate of occupational stress among the manufacturing employees was 38.9%. The Boruta method identified 11 core variables of occupational stress: depressive symptoms, cumulative fatigue, age, length of service, tenure in current position, average weekly working hours, average daily overtime hours, average monthly income, low levels of exercise, life satisfaction, and sleep quality. The BSAE reduced the original cumulative fatigue factors to 12 dimensions, while the K-means clustering grouped the reduced fatigue features into three categories: no fatigue, moderate fatigue, and sever fatigue. Six occupational stress prediction models were constructed using the three-category labels of cumulative fatigue and ten additional factors. The results indicated that the LightGBM model performed best, achieving an area under the curve (AUC) of 0.78, an accuracy of 0.77, and an F1 score of 0.72. Compared with the model incorporating the traditional binary labels for cumulative fatigue, the best prediction AUC (0.72), accuracy (0.66), and F1 score (0.65) improved by 6%, 11%, and 7%, respectively. Conclusion Cumulative fatigue is a significant predictor of occupational stress among manufacturing employees. Applying data dimensionality reduction combined with three-class cluster analysis to characterize cumulative fatigue improves the performance of occupational stress prediction models. From a data-driven perspective, machine learning methods demonstrate strengths in processing complex datasets, capturing nonlinear relationships, and generating predictions based on key variables. This study therefore confirms that refined processing of core factors substantially enhances the predictive capability of machine learning models in assessing occupational stress risk in the manufacturing workforce.

Allergen-specific immunotherapy（AIT） remains the only therapeutic approach with the potential to modify the natural course of allergic rhinitis（AR）. Nevertheless, considerable inter-individual variability exists in patients'responses to AIT. To facilitate more reliable assessment of treatment efficacy, the China Rhinopathy Research Cooperation Group（CRRCG） convened young and middle-aged nasal experts in China to formulate the present consensus. The recommended subjective outcome measures for AIT comprise symptom scores, medication scores, combined symptom and medication scores, quality-of-life assessments, evaluation of disease control, and assessment of comorbidities. Objective indicators may supplement these measures. Currently available objective approaches include skin prick testing, nasal provocation testing, and allergen exposure chambers. However, these methods remain constrained by practical limitations and are not yet appropriate for routine implementation in clinical efficacy evaluation. In addition, several biomarkers, including sIgE and the sIgE/tIgE ratio, sIgG4, serum IgE-blocking activity, IgA, cytokines and chemokines, as well as immune cell surface molecules and their functional activity, have been shown to have associations with AIT outcomes. While these biomarkers may complement subjective assessments, they are subject to significant limitations. Consequently, large-scale multicenter trials and real-world evidence are required to strengthen the evidence base. The present consensus underscores the necessity of integrating patients'subjective experiences with objective testing throughout the treatment process, thereby providing a more comprehensive and accurate framework for efficacy evaluation. Looking forward, future investigations should prioritize the incorporation of multi-omics data and artificial intelligence methodologies, which hold promise for overcoming current limitations in assessment strategies and for advancing both the standardization and personalization of AIT.
Humans ; Allergens/immunology* ; China ; Consensus ; Desensitization, Immunologic ; Immunoglobulin E ; Quality of Life ; Rhinitis, Allergic/therapy* ; Treatment Outcome ; East Asian People

Objective: To analyze the disease burden and trends of oral diseases among China’s elderly population (1990-2021) and provide evidence for developing targeted intervention strategies Methods : Using data from the Global Burden of Disease (GBD) 2021 study, we extracted prevalence, incidence, and disability-adjusted life years (DALYs) for oral conditions (permanent dental caries, edentulism, periodontal diseases, and other oral disorders) in individuals aged ≥60 years in China. Due to data limitations, other oral diseases only included DALYs and prevalence. Age-standardized rates (ASR)—including age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), and age-standardized DALYs rate (ASDR)--were calculated. Trends were assessed via Joinpoint regression using average annual percentage change (AAPC), stratified by sex and age groups (60-64, 65-69, 70-74, 75-79, 80-84, 85-89, 90-94, 95+ years). Results: From 1990 to 2021, China’s elderly population exhibited distinct trends in oral disease burden. Overall oral diseases showed declining ASDR and ASPR, yet ASIR slightly increased. Permanent dental caries demonstrated significant rises across ASDR, ASIR, and ASPR. Edentulism showed declining ASDR and ASPR alongside stable ASIR. 95+ age group saw rising rates. Periodontal diseases remained largely stable in ASDR and ASPR but experienced a slight ASIR decline. Other oral disorders showed mild ASDR decline and stable ASPR. Notably, sex and age disparities persisted. Women consistently bore higher burdens for overall oral diseases, caries, edentulism, and other oral diseases but lower periodontal disease rates compared to men. 85-89, 90-95, 95+ age group faced rising DALYs and prevalence for overall oral diseases, while all other age groups demonstrated declining trends in both DALYs and prevalence; for permanent caries, the 60-64 age group showed the largest increases in DALY rate, incidence, and prevalence; edentulism demonstrated the most pronounced and sustained rises in DALY rate and prevalence in the 95+ group, while declining most rapidly in the 60-64 age group; for periodontal disease, both DALY rates and prevalence declined in the 90-94 and 95+ age groups, but increased across all measures (DALY rate, incidence, and prevalence) in the 70-74 and 75-79 age group; other oral conditions exhibited relatively stable burden distributions or minor changes, with no significant age-specific shifting trends observed. Conclusion From 1990 to 2021, China’s elderly oral disease burden declined overall, but caries surged, edentulism improved, periodontal diseases stabilized, and other oral diseases slightly declined. Prioritizing older women and the adults aged 85+ is critical to addressing evolving oral health needs.