ObjectiveAs a common lifestyle habit, alcohol consumption has a controversial association with the onset of Parkinson’s disease (PD). To demonstrate the correlation between alcohol consumption and PD and to identify associated genes, we integrated findings from clinical surveys, genomics, transcriptomics, and animal experiments. MethodsWe investigated the alcohol consumption rates (including both before and after disease onset) among 244 PD patients in China and 177 PD patients from the U.S. NHANES database. Mendelian randomization (MR) analysis was performed using genome-wide association study (GWAS) data for three alcohol-related traits and seven PD-related datasets from the MRC IEU OpenGWAS database. Transcriptomic data from the substantia nigra of PD patients were obtained from three GEO datasets (GSE7621, GSE20141, and GSE49036) to analyze RIT2 gene transcription. Finally, three groups of animal experiments (water/20% ethanol/20% liquor, with 4 C57BL/6J mice per group) were conducted to examine changes in brain RIT2 gene expression and transcriptomic profiles following alcohol consumption. ResultsThe alcohol consumption rates among PD patients in China and the U.S. (9%-18.87%) were significantly lower than the general population rates of 15%-45% in their respective regions (P<0.001), suggesting a possible negative association between alcohol consumption and PD. Subsequently, in 21 bidirectional MR analyses using 3 alcohol-related GWAS datasets and 7 PD-related GWAS datasets, the forward MR analyses (alcohol intake as exposure, PD as outcome) yielded 12 negative associations (OR_IVW<1) and 9 positive associations (OR_IVW>1). Among these, only two negative associations reached statistical significance: alcohol intake frequency (OR_IVW=0.75, 95% CI: 0.60-0.93, P=0.010) and alcohol consumption (OR_IVW=0.20, 95% CI: 0.05-0.83, P=0.026). The forward MR analysis (alcohol intake→PD) identified 235 SNPs, annotated to 316 genes, while the reverse MR analyses (PD→alcohol intake) identified 37 SNPs, annotated to 53 genes. Notably, only the RIT2 gene appeared in both the forward and reverse MR analyses (alcohol intake→PD: rs28597806, rs8083110; PD→alcohol intake: rs4588066). RIT2 is selectively expressed in the human brain (FPKM: 5.259±2.103), with low or no expression in peripheral tissues (FPKM: <1). Analysis of three human substantia nigra transcriptomic datasets revealed a decreasing trend in RIT2 gene expression in PD patients (GSE20141 array signal: 3.49±1.23 vs. 2.33±0.87, P=0.044). Animal experiments demonstrated that administration of 20% ethanol or 20% liquor (approximately 8% ethanol) stimulated a >2-fold upregulation of RIT2 gene expression in the mouse brain. Furthermore, transcriptomic sequencing revealed that the two alcohol-treated groups exhibited 96 (20% ethanol vs. water control) and 4 (20% liquor vs. water control) differentially expressed genes, respectively, indicating that low-dose alcohol consumption can achieve RIT2 upregulation while minimizing impact on other brain genes. In addition to its anti-infective effects, low-dose alcohol consumption primarily influences signaling pathways related to neurodegenerative diseases such as PD and Prion diseases. ConclusionAlcohol consumption is generally considered as a harmful lifestyle habit. However, some studies have also shown a lower risk of mortality among individuals who consume low doses of alcohol (100 g/week of ethanol) or drink occasionally. Currently, one of the research focuses on alcohol consumption is whether the human body can benefit from low-dose alcohol intake. This study provides new evidence supporting a negative association between alcohol consumption and PD, and for the first time, through MR analysis, identifies the RIT2 gene as a potential mediator of the effect of alcohol consumption on PD. RIT2 is selectively expressed in the human brain. Building upon existing evidence indicating downregulated RIT2 gene expression in PD pathogenesis, our experiments confirm that low-dose alcohol consumption can upregulate RIT2 expression in the brain. In brief, alcohol consumption may suppress the pathogenesis of PD by upregulating RIT2 expression in the substantia nigra. China is facing a serious problem of population aging. This study offers important insights for long-term PD prevention and treatment strategies, with the aim of benefiting more potential PD patients through lifestyle modifications, thereby improving the quality of life of the aging population and reducing the economic burden on healthcare.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Sustained inflammatory responses are closely related to various severe diseases,and inhibiting the excessive activation of inflammasomes and pyroptosis has significant implications for clinical treatment.Natural products have garnered considerable concern for the treatment of inflammation.Huanglian-Wumei decoction(HLWMD)is a classic prescription used for treating inflammatory diseases,but the necessity of their combination and the exact underlying anti-inflammatory mechanism have not yet been elucidated.Inspired by the supramolecular self-assembly strategy and natural drug compatibility theory,we successfully obtained berberine(BBR)-chlorogenic acid(CGA)supramolecular(BCS),which is an herbal pair from HLWMD.Using a series of characterization methods,we confirmed the self-assembly mechanism of BCS.BBR and CGA were self-assembled and stacked into amphiphilic spherical supra-molecules in a 2:1 molar ratio,driven by electrostatic interactions,hydrophobic interactions,and π-πstacking;the hydrophilic fragments of CGA were outside,and the hydrophobic fragments of BBR were inside.This stacking pattern significantly improved the anti-inflammatory performance of BCS compared with that of single free molecules.Compared with free molecules,BCS significantly attenuated the release of multiple inflammatory mediators and lipopolysaccharide(LPS)-induced pyroptosis.Its anti-inflammatory mechanism is closely related to the inhibition of intracellular nuclear factor-kappaB(NF-κB)p65 phosphorylation and the noncanonical pyroptosis signalling pathway mediated by caspase-11.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

OBJECTIVE: This study reports the first imported case of Lassa fever (LF) in China. Laboratory detection and molecular epidemiological analysis of the Lassa virus (LASV) from this case offer valuable insights for the prevention and control of LF. METHODS: Samples of cerebrospinal fluid (CSF), blood, urine, saliva, and environmental materials were collected from the patient and their close contacts for LASV nucleotide detection. Whole-genome sequencing was performed on positive samples to analyze the genetic characteristics of the virus. RESULTS: LASV was detected in the patient's CSF, blood, and urine, while all samples from close contacts and the environment tested negative. The virus belongs to the lineage IV strain and shares the highest homology with strains from Sierra Leone. The variability in the glycoprotein complex (GPC) among different strains ranged from 3.9% to 15.1%, higher than previously reported for the seven known lineages. Amino acid mutation analysis revealed multiple mutations within the GPC immunogenic epitopes, increasing strain diversity and potentially impacting immune response. CONCLUSION The case was confirmed through nucleotide detection, with no evidence of secondary transmission or viral spread. The LASV strain identified belongs to lineage IV, with broader GPC variability than previously reported. Mutations in the immune-related sites of GPC may affect immune responses, necessitating heightened vigilance regarding the virus.
Humans ; China/epidemiology* ; Genome, Viral ; Lassa Fever/virology* ; Lassa virus/classification* ; Molecular Epidemiology ; Phylogeny

ObjectiveTo analyze the evolutionary characteristics and genetic variations of the HA (hemagglutinin) and NA (neuraminidase) genes of influenza A(H1N1) viruses in Shanghai during 2024, to investigate their transmission patterns, and to evaluate their potential impact on vaccine effectiveness. MethodsFrom January to October 2024, throat swab specimens were collected from influenza like illness (ILI) patients at 4 hospitals in Shanghai. Real-time fluorescence ploymerase chain reaction (RT-PCR) was used for virus detection and isolation of H1N1 influenza viruses. Forty influenza A(H1N1) virus strains were sequenced using Illumina NovaSeq 6000 platform, followed by phylogenetic analyses, genetic distance analysis, and amino acid variation analyses of HA and NA genes. ResultsPhylogenetic tree of the HA and NA genes revealed that the 40 influenza A(H1N1) virus strains circulating in Shanghai in 2024 exhibited no significant geographic clustering, with a broad origin of strains and complex transmission chains. Genetic distance analyses demonstrated that the average intra-group genetic distances of HA and NA genes among the Shanghai strains were 0.005 1±0.000 6 and 0.004 6±0.000 6, respectively, which were comparable to or higher than those observed in global surveillance strains. Both HA and NA genes displayed frequent mutations. Compared to the 2023‒2024 and 2024‒2025 Northern Hemisphere A(H1N1) vaccine strains (WHO-recommended), the HA proteins of 40 Shanghai strains exhibited amino acid substitutions at positions 120, 137, 142, 169, 216, 223, 260, 277, 356 and 451, with critical mutations at positions 137 and 142 located within the Ca2 antigenic determinant. Furthermore, mutations in the NA protein were observed at positions 13, 50, 200, 257, 264, 339 and 382. ConclusionThe genetic background of the 2024 Shanghai influenza A(H1N1) virus strains is complex and diverse, and antigenic variation may affect vaccine effectiveness. Therefore, it is recommended to enhance genomic surveillance of influenza viruses, evaluate vaccine suitability, and implement more targeted prevention and control strategies against imported influenza viruses.

Objective: To investigate the basic situation of pre-analytical quality management in blood station laboratories in North China, and to provide baseline data for promoting the homogenization and standardization of these pre-analytical processes in each blood station laboratory. Methods: A cross-sectional status survey was designed based on the quality management regulations of blood stations, ISO15189 standards and relevant quality management requirements. This survey covering various aspects including laboratory general situation, sample collection and temporary storage, transportation, reception, and quality continuous improvement situations. Data analysis was performed on the survey results of each laboratory. Results: All the 38 blood station laboratories in North China had established a pre-analytical quality management system framework and implemented basic pre-analytical quality control activities; however, there were differences in implementation. 1) Among the 12 basic quality items, 3 items were monitored by all the investigated laboratories (100%), 6 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 60%). There were no significant differences in the monitoring index among the three regions and among different types of laboratories (P>0.05). 2) Among the total of 26 items in the three key processes before testing (sample collection and storage, transportation, reception and processing), 12 items were monitored by all laboratories (100%), 11 items were monitored by the vast majority of laboratories (about 90%), and 3 items were monitored by a portion of laboratories (about 75%). There were no significant differences in monitoring index among different regions and types of laboratories (P>0.05). Conclusion: This survey provides a reference and basis for the gap analysis of the pre-analytical process quality management in 38 blood station laboratories across North China. It facilitates laboratories in identifying pre-analytical quality problems, resolving problems, preventing errors, and ensuring that the quality of blood samples before testing meets the established requirements. It lays a foundation for the homogenization of pre-analytical quality management in regional blood stations.

Objective: To determine the frequency and main reasons of unqualified samples by analyzing the quality of pre-analytical samples in blood stations in North China, thereby providing a reference and basis for gap analysis in the implementation of pre-analytical process quality management for participating laboratories and ensuring that only high-standard and high-quality blood samples proceed to testing. Methods: Data on the quality of pre-analytical samples from blood station laboratories in North China was collected via questionnaire. Statistical analysis were performed on: 1) the basic information of samples quality monitoring in the laboratories; 2) the distribution of the overall pre-analytical unqualified rate of samples and the pre-analytical unqualified rate of samples in each laboratory; 3) the distribution of reasons for sample disqualification. Results: 1) The overall pre-analytical unqualified rate of samples in blood station laboratories in North China was 4.55, with a total sigma level of 5.39σ. The 25th, 50th and 75th percentiles (P25, P50, P75) for the total unqualified rate were 0.00, 1.10 and 5.96, respectively. The corresponding percentiles for the Sigma level were 5.34σ, 5.71σ, and 6.00σ, respectively. The pre-analytical unqualified rate of serological and nucleic acid samples (4.89 vs 4.22) showed a significant difference (χ =9.575, P<0.05). 2) The average unqualified rate of samples in region A, B and C was 1.71, 9.50 and 12.64 (χ =1 590.721, P<0.05), and the sigma level was 5.66σ, 5.21σ and 5.16σ, respectively. 3) The main reasons for unqualified serological samples were chylous blood (72.65%), hemolysis (17.39%), abnormal hematocrit (5.80%), and insufficient volume (3.50%). The main reasons for the unqualified nucleic acid samples were chylous blood (78.26%), hemolysis (8.84%), failure to centrifuge as required (5.01%), abnormal hematocrit (4.66%), and insufficient volume (1.92%). Conclusion: In North China, the quality indicators for the pre-analytical processes in blood station laboratories are generally well-managed. Laboratories in region A outperformed the national average in pre-analytical specimen quality control. However, participating laboratories exhibit gaps in implementing pre-analytical quality management. Through effective analysis of pre-analytical process quality metrics and inter-laboratory comparisons, laboratories can identify discrepancies and address shortcomings. By establishing clear quality objectives, they can achieve continuous improvement and ensure the validity of test results.