Xiaofang Pei

Background: Although education is commonly regarded as a favorable factor for bone health, its association with bone status may vary across different socioeconomic and ecological contexts. We examined the association between educational attainment and QUS (quantitative ultrasound)-defined bone status across five provinces in Southwest China and explored province-specific indirect statistical associations. Methods: This cross-sectional study included 73,600 adults aged ≥ 30 years from Sichuan, Chongqing, Guizhou, Yunnan, and Tibet, recruited through a multistage, stratified cluster sampling design within the China Multi-Ethnic Cohort. QUS-defined bone status was classified as normal, reduced, and markedly reduced using calcaneal quantitative ultrasound-derived T-scores. Geographic effect modification was evaluated using cumulative logit ordinal regression. Province-specific path analysis was further conducted to explore potential indirect associations involving health behaviors, nutritional factors, and socioeconomic status. Partial proportional-odds models were applied in sensitivity analyses. Results: Participants had a mean age of 51.54 years (SD, 11.43), and 62.1% were women. Overall, 60.6%, 35.0%, and 4.4% of participants were classified into the normal, reduced, and markedly reduced QUS-defined bone-status categories. Education showed marked regional heterogeneity in its association with bone status ( P < 0.001): higher educational attainment was associated with poorer bone status in Sichuan (β=-0.088, P < 0.05), Guizhou (β=-0.047, P < 0.05), and Tibet (β=-0.067, P < 0.05), better bone status in Chongqing (β = 0.045, P < 0.05), and no association in Yunnan (β = 0.015, P = 0.155). Path analyses showed distinct provincial patterns: higher alcohol consumption and higher dairy intake in Sichuan; higher income, higher dairy intake, and reduced smoking in Chongqing; lower physical activity in Guizhou and Yunnan; and no statistically significant indirect association was identified in Tibet. In PPOM sensitivity analyses, high (vs. low) educational attainment was associated with lower odds of worse bone status in Chongqing at both cumulative thresholds (OR = 0.738, 95% CI, 0.652–0.837; OR = 0.688, 95% CI, 0.541–0.875), higher odds of worse bone status in Guizhou (OR = 1.174, 95% CI, 1.018–1.352), and no clear association in Yunnan (OR = 0.873, 95% CI, 0.696–1.094). Conclusions: Educational attainment showed province-specific associations with QUS-defined bone status in Southwest China. Indirect statistical associations involving socioeconomic, behavioral, and dietary factors require confirmation in longitudinal studies.

Balneotherapy is a potential complementary approach for sleep problems, but its short-term sleep effects and accompanying biological changes remain unclear. We conducted a prospective single-arm, self-controlled pilot study of 30 adults with sleep disorders, who completed a 10-day residential hot spring balneotherapy program (twice daily at 09:00 and 20:00, 30 min per session, 40-42 °C) at Tianhe Hot Spring, Sichuan Province, residing on-site and receiving identical meals throughout the intervention. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI) and wrist-worn wearable tracking. Serum neurotransmitters, inflammatory cytokines and tryptophan-kynurenine metabolites were assayed, while 16 S rRNA-based gut microbiota profiles were profiled pre- and post-intervention. Post-intervention, PSQI scores decreased, while wearable metrics indicated longer total and nocturnal sleep duration, elevated deep-sleep duration and proportion, better sleep continuity, and a lower rapid eye movement (REM) percentage (P < 0.05). Blood pressure, anxiety/depression scores, and wearable-derived sleep-stress indices also improved. Biomarker profiling revealed elevated GABA, 5-HT, 5-HIAA, BDNF, and IL-10, alongside reduced IL-1β, IL-6, and TNF-α (P < 0.05). Trp metabolism shifted, characterized by decreased Trp and increased 3-HAA and PA (P < 0.05). Structurally, the gut microbiota exhibited an increased abundance of Blautia_A (P < 0.05), with LEfSe analysis identifying post-intervention enrichment of Turicibacter and pre-intervention predominance of Agathobacter. Cross-system network analysis further established 5-HT, IL-6, and IL-10 as the core candidate biomarker profile directly correlating with these multi-domain sleep improvements. Overall, short-term residential hot spring balneotherapy may improve subjective and wearable-derived sleep outcomes, accompanied by coordinated cardiovascular, stress-related, neuroimmune, tryptophan-metabolic, and gut microbiome changes, supporting a multi-system physiological basis for balneotherapy-related sleep improvement.

Background: Osteoporosis and bone loss (OP&BL) are major public health challenges, especially in high-altitude environments with chronic hypoxia. Current diagnostic methods, based on low-altitude populations, are impractical for large-scale screening in resource-limited, high-altitude settings. This study developed a machine learning-based predictive model for OP&BL by integrating oral microbiota data with clinical and questionnaire variables. Methods: We analyzed data from 560 Tibetan adults residing at high altitudes. Bone health status (OP&BL vs. normal) was determined by dual-energy X-ray absorptiometry. Oral microbiota profiles were characterized via 16 S rRNA sequencing. After feature selection using elastic net regression, five machine learning models, namely Logistic Regression (LR), Naïve Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB), were trained (60%, 337/560) and validated (40%, 223/560). Results: Feature selection identified nine predictors: Age, Gender, BMI, oral microbial genera Abiotrophia , Frequency of spicy food consumption (H23), Tooth brushing frequency (J5), Frequency of sweet-drink consumption (J3b), Current marital status (Separated/Divorced, A5_3), and frequency of numbing food consumption (H27). The LR model demonstrated good and stable performance with an AUC of 0.885 (95% CI: 0.823–0.937) on the test set, along with good calibration and the highest net clinical benefit. SHAP analysis indicated that oral factors Abiotrophia and Tooth brushing frequency together accounted for nearly 10% of the model’s total predictive contribution. Conclusions: We developed a machine learning model integrating oral microbiota and clinical data for predicting OP&BL in people living above 3500 m. This model could offer a promising non-invasive tool for early screening in resource-limited settings and highlights the potential role of oral factors in high-altitude bone health.

Accurate serotyping of Salmonella is essential for effective surveillance and outbreak investigation, as serotype diversity directly impacts pathogenicity and public health risk assessment. However, conventional slide agglutination methods are limited by poor reproducibility, labor intensity, and high costs, which hinder their application in high-throughput monitoring programs. To address these limitations, we developed and validated a genomic workflow integrating Multilocus Sequence Typing (MLST), the Salmonella In Silico Typing Resource (SISTR), SeqSero, SeqSero2, and SeqSero2S for serotype prediction using whole-genome sequencing data. This protocol was evaluated through a multicenter analysis of 315 Salmonella isolates collected from food and human sources in Southwest China. The findings of this study demonstrated significantly higher concordance among genomic approaches (up to 100%/99.1%/90.1% in the training set and 100%/97.1%/93.1% in the validation set for SISTR/MLST/SeqSero2S, respectively) compared to traditional serotyping. The workflow includes recommendations for selecting appropriate prediction methods based on surveillance context, emphasizing MLST and SeqSero2S for routine monitoring, SeqSero2S for rapid screening, and SISTR with core genome MLST for outbreak investigations. This approach facilitates the integration of genomic serotyping into public health practice, reducing reliance on traditional serology and improving reproducibility and scalability in Salmonella monitoring programs.

Accurate serotyping of Salmonella is essential for effective surveillance and outbreak investigation, as serotype diversity directly impacts pathogenicity and public health risk assessment. However, conventional slide agglutination methods are limited by poor reproducibility, labor intensity, and high costs, which hinder their application in high-throughput monitoring programs. To address these limitations, we developed and validated a genomic workflow integrating Multilocus Sequence Typing (MLST), the Salmonella In Silico Typing Resource (SISTR), SeqSero, SeqSero2, and SeqSero2S for serotype prediction using whole-genome sequencing data. This protocol was evaluated through a multicenter analysis of 315 Salmonella isolates collected from food and human sources in Southwest China. The findings of this study demonstrated significantly higher concordance among genomic approaches (up to 100%/99.1%/90.1% in the training set and 100%/97.1%/93.1% in the validation set for SISTR/MLST/SeqSero2S, respectively) compared to traditional serotyping. The workflow includes recommendations for selecting appropriate prediction methods based on surveillance context, emphasizing MLST and SeqSero2S for routine monitoring, SeqSero2S for rapid screening, and SISTR with core genome MLST for outbreak investigations. This approach facilitates the integration of genomic serotyping into public health practice, reducing reliance on traditional serology and improving reproducibility and scalability in Salmonella monitoring programs.

Helicobacter pylori infects nearly half of the world’s population and contributes to chronic gastritis, gastric cancer, and other gastrointestinal diseases. Upon diagnosis, antibiotic-based regimens are typically used for eradication. However, the increasing antibiotic resistance of H. pylori and the side effects of these medications highlight the need for safer and more effective alternatives, particularly those derived from natural products. In this study, by using high-throughput molecular docking, 12 commercially available potential HsrA inhibitors were screened out from 13,142 natural compounds in the TCMSP database for the first time. After evaluation of their binding-mode stability to HsrA by using molecular dynamics (MD) simulations and their minimum inhibitory concentration (MIC), sesamolin (3) and coptisine (6), exhibiting stable binding modes and strong anti-H. pylori activity, were selected, and their inhibitory effects on the DNA-binding activity of HsrA were further confirmed using electrophoretic mobility shift assay (EMSA). However, compound 3 bound HsrA with high affinity (K _D = 17.08 nM), approximately 23-fold stronger than compound 6 (K _D = 399.60 nM). Further RT-qPCR analysis demonstrated that compound 3 exerted regulatory effects stronger than those of 6 on HsrA-linked genes (tlpB, flhA), urease genes (ureA, ureB), and the oxidative stress gene (sodB; reported to inversely correlate with HsrA levels). Consistently, 3 showed a greater suppression of urea chemotaxis and a larger increase in superoxide dismutase (SOD) activity than 6. Our investigation uncovered sesamolin, a common lignan found in sesame, which could be a potential anti-H. pylori compound by binding with HsrA and regulating the related key genes of H. pylori.

Antibiotic resistance has become a serious challenge to global public health. The spread of antibiotic resistance genes (ARGs) among humans, animals, and the environment has become a critical issue within the “One Health” framework. Chinese wet market with live poultry trade provides an interface for close interaction between humans and chickens, and is considered as potential source for disease dissemination. However, the understanding of ARGs in this kind of market, including their shared profiles, influencing factors, and potential horizontal transfer subtypes and directions, remains limited. In this study, we explored the microbiome, resistome, and mobility of ARGs, and identified putative horizontal gene transfer (HGT) events in the Chinese wet market system by utilizing metagenomic assembly and binning. Consequently, a total of 1080 ARG subtypes were identified from 36 metagenomes, and 221 subtypes were shared among human feces, chicken feces, chicken carcasses, and the environment. The composition of ARGs was influenced by mobile genetic elements (MGEs) and bacterial communities. As for the host of ARGs, 89 ARG-carrying genomes (ACGs) were identified, with 18 of them carrying multiple ARGs and MGEs, indicating the potential mobility of ARGs. Notably, six ACGs were identified as opportunistic pathogens carrying multiple ARGs and MGEs, which were annotated as Escherichia coli, Acinetobacter johnsonii, Klebsiella variicola, Klebsiella pneumoniae, and Citrobacter freundii. In addition, 164 potential HGT events were identified based on ACGs, and ParS, vanB, ugd, and macB were annotated as potentially transferred ARG subtypes in humans and the wet market. This study offers new insights into the potential for HGT of ARGs within a Chinese wet market setting, highlighting putative transmission patterns among humans, poultry, and the environment. To our knowledge, few studies have explored ARG transfer potential in this context using metagenome-assembled genomes, making this a valuable contribution to One Health surveillance.

Previous studies investigating the influence of hot spring bathing on sleep quality have predominantly focused on the short-term effects through questionnaire surveys without blood collection for biochemical tests. Here, we undertook a comprehensive investigation of the long-term health effects of hot spring bathing among the residents of Hot Spring Village. A total of 140 participants were enrolled, and their demographic characteristics and the patterns of hot spring bathing were obtained via face-to-face interview, and sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). Moreover, the blood samples of the participants were collected for biomarker detection with the ELISA assay. Logistic regression analysis was applied to evaluate the relationship between sleep quality and serum biomarkers among long-term hot spring bathers. In individuals aged 65 and older, the results demonstrated that hot spring bathing (OR = 0.18, 95%CI: 0.05–0.68), particularly with a length of ≥ 30 min (OR = 0.10, 95%CI: 0.02–0.53) and a frequency of ≥ 3 times/week (OR = 0.07, 95%CI: 0.01–0.32) were significant protective factors for good sleep quality (P < 0.05). Furthermore, reduced TNF-α (OR = 1.03, 95% CI: 1.01–1.06) and increased 5-HT levels (OR = 0.98, 95% CI: 0.97–0.99) were associated with good sleep quality. Interestingly, for the first time, we observed that the TNF-α significantly decreased (P < 0.05) in the bathing group, along with an increasing trend of 5-HT and BDNF. Moreover, among participants in this group who reported good sleep quality, there was a notably significant decrease in TNF-α and an increase in 5-HT levels as well. These findings suggested that long-term hot spring bathing is associated with good sleep quality through the alteration of TNF-α and 5-HT levels, which could be potential biomarkers for future investigation on the health-promoting effects of bathing.