Effective host defense against pathogens requires coordinated behavioral and immune responses, yet the mechanisms that couple epithelial sensing to these systemic defenses remain poorly understood. Here, we identify a proton-mediated gut-to-neuron signaling pathway that orchestrates host defense in C. elegans. Intestinal pathogens stimulate mechanosensitive Ca2+ influx into intestinal epithelial cells (IECs) through the TRP channel GON-2, activating the Na+/H+ exchanger NHX-6 via the calmodulin CMD-1 to drive basolateral proton release. These protons activate cholinergic motor neurons through the acid-sensing ion channel ASIC-1, enhancing cholinergic transmission to promote both pathogen avoidance and intestinal innate immunity. Notably, mouse NHE1 and ASIC1a can functionally substitute for their nematode counterparts. Together, these findings demonstrate a role for proton signaling in gut-to-neuron communication, revealing a potentially conserved mechanism that links epithelial sensing to neuroimmune defense.

Animals integrate internal states to guide survival-critical decisions, but whether and how intestinal bacteria influence this process by interacting with host metabolic cues remains unclear. Here we show that the intestinal pathogen P. aeruginosa overrides host decision-making in fasted C. elegans by modulating central serotonin (5-HT) signaling. Fasting promotes risk-taking by activating an intestinal energy-sensing pathway that induces the chemoreceptor SRI-36 in ADF 5-HT neurons, sensitizing ADF to food odors and triggering moderate 5-HT release that drives food attraction despite risk. In contrast, intestinal P. aeruginosa reverses this strategy by activating a distinct immune-brain axis that further amplifies SRI-36 expression and ADF sensitivity, leading to excessive 5-HT release that suppresses food attraction and prioritizes safety. These findings reveal a gut-to-brain mechanism by which metabolic and immune signals converge on central 5-HT to reshape behavioral strategies.

Although several cross-sectional studies have examined the relationship between normal-weight central obesity (NWCO) and the risk of cardiometabolic disorders (CMD), longitudinal evidence from cohort studies is still limited, especially regarding diverse CMD outcomes in Chinese populations. We conducted a retrospective cohort study by using health examination records from Jiangsu Province Geriatric Hospital. The study included 5,606 normal-weight (body mass index of 18.0–24.0 kg/m2) participants free of CMD at their baseline visit in 2022. NWCO was defined as normal-weight and central obesity (waist circumference ≥ 90 cm for men or ≥ 80 cm for women), whereas normal-weight non-central obesity (NWNO) was defined as normal-weight without central obesity. Participants were followed until the occurrence incident CMD events or the last visit in 2024. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95

Background:Vitamin D deficiency is prevalent among individuals with depression; however, clinical findings regarding this association have been inconsistent. Additionally, a significant proportion of depressed patients present with dyslipidemia, yet the interplay between vitamin D status, lipid metabolism, and depression remains poorly understood. We aimed to explore the role of vitamin D in depression and to investigate the potential associations between vitamin D status, lipid metabolism, and depressive symptoms. Methods:We recruited 412 first-episode, drug-naïve patients with depression and 180 age-matched healthy controls. Fasting venous blood samples were collected in the morning to quantify serum vitamin D and lipid profiles. Depressive symptoms were assessed on the day of blood collection using both the Patient Health Questionnaire-9 (PHQ-9) and the 17-item Hamilton Depression Rating Scale (HAMD-17). Spearman's rank correlation was employed to examine associations between serum vitamin D concentrations and depressive symptom severity. Binary logistic regression analysis was subsequently performed to identify potential risk factors for depression. Results:Compared with healthy controls, depressed patients had significantly lower serum vitamin D and high-density lipoprotein cholesterol (HDL-C) levels. This sex-specific pattern showed that male patients had lower vitamin D, while female patients had lower HDL-C. Spearman's correlation analysis revealed significant inverse correlations of vitamin D and triglyceride (TG) with PHQ-9 and HAMD-17 scores among depressed patients. Logistic regression analysis indicated that individuals with higher vitamin D levels had a reduced likelihood of depression compared with those with low vitamin D levels (adjusted odds ratio (OR) = 0.950, 95% confidence interval (CI): 0.920-0.982, p = 0.002). Similarly, subjects with elevated HDL-C levels were associated with a lower likelihood of depression relative to those with diminished HDL-C levels (adjusted OR = 0.317, 95% CI: 0.173-0.583, p < 0.001). Conclusion:Serum vitamin D and HDL-C levels were lower in patients with depression than in healthy individuals. Both vitamin D and HDL-C may be inversely associated with depression.

BackgroundOccupational fatigue is a complex and widespread issue among healthcare workers, yet its heterogeneous manifestations remain inadequately studied. This study aimed to identify distinct fatigue profiles and examine the multifaceted determinants that differentiate these profiles.MethodsA cross-sectional survey was conducted among 734 healthcare workers. The assessment was conducted using the newly developed Healthcare Worker Occupational Fatigue Scale that has undergone reliability and validity tests. Latent profile analysis was used to identify occupational fatigue subgroups, and multiple logistic regression analysis was conducted to explore the influencing factors of each subgroup.ResultsLatent profile analysis identified four distinguishable occupational fatigue subgroups: the compensated group (17.97%), the prodromal-symptomatic group (35.29%), the decompensated diffuse group (37.87%), and the systemic crisis group (8.86%). Multivariate logistic regression analysis revealed that perceived workload, department affiliation, number of night shifts per month, low intention to stay, noisy environment, commuting time, and individual-level factors, including gender and health status, were significant risk factors for occupational fatigue.ConclusionOccupational fatigue among healthcare workers exhibits substantial heterogeneity and can be categorized into four distinct profiles, with multiple contributing factors. It is necessary to adopt hierarchical and personalized intervention strategies based on precise subgroup characteristics, such as systematically reducing the workload and optimizing the acoustic environment for the severe fatigue group, in order to effectively alleviate the occupational fatigue of healthcare workers.

Purpose: Gout is a common, chronic inflammatory joint disease, and men are more likely to suffer from gout. Improving patient selfmanagement behaviors is a priority in gout healthcare. Psychological capital is associated with self-management behaviors in chronic diseases and can be improved through a number of interventions. However, this topic has not been well studied in gout patients. The aim of this study was to determine the level of psychological capital among male gout patients in Southwest China and to compare differences in self-management behaviors among patients with different levels of psychological capital. Patients and Methods: This was a cross-sectional study. A total of 242 male gout patients were recruited from West China Hospital of Sichuan University, and demographic characteristics, clinical characteristics, psychological capital, and behavioral variables related to patient self-management were collected. K-Means cluster analysis was used to characterize psychological capital. Results: The total psychological capital score of the participants was 134.5 (SD = 21.3). Cluster analysis of the four dimensions of psychological capital yielded three clusters, namely, Cluster 1 (higher level, 29.8%), Cluster 2 (moderate level, 52.3%), and Cluster 3 (poor level, 17.9%). The differences in the self-management behaviors among the three clusters, the differences were statistically significant. Post hoc analyses revealed that cluster 1 scored higher on the self-Management behaviors and its four dimensions than either cluster 2 or cluster 3 (p < 0.05). Conclusion: The psychological capital of men with gout in Southwest China could be improved, and moderate and low levels of psychological capital are associated with suboptimal self-management behaviors. Healthcare providers may target gout patients with low or moderate levels of psychological capital as an intervention and take steps to improve their levels of psychological capital. These results may assist in decision-making for self-management behavioral interventions for gout patients.

There has been an increasing interest in the association between meteorological risk factors and scarlet fever risk. However, the associations between individual-level exposure to meteorological factors and the scarlet fever risk remain poorly understood. We collected 36, 912 scarlet fever cases in Jiangsu Province, China (2005–2023) from the Nationwide Notifiable Infectious Diseases Reporting Information System. These data were then paired with daily meteorological factors, including temperature, relative humidity, solar radiation, wind speed, surface pressure, and total precipitation, sourced from the ERA5-Land dataset. A time-stratified case-crossover design was applied using conditional logistic regression combined with distributed lag non-linear models to examine both linear and non-linear associations, while adjusting for public holidays and recent outbreaks. Subgroup analyses by age, gender, period, and season were conducted to assess potential heterogeneity. Effect estimates for individual exposures are expressed as odds ratios (ORs), and interactions were evaluated using the relative excess odds due to interaction (REOI), attributable proportion (AP), and the synergy index (S). Significant linear associations (ORs per one-unit increment) were observed for all meteorological variables except wind speed with a 2–5 day lag, peaking at 3 days. Positive associations were found for solar radiation (OR = 1.009, 95 ^2 and surface pressure (OR = 1.088, 95 ^∘ C, relative humidity (OR = 0.995, 95 ^∘ C and fluctuating risk at extreme cold (below −5 ^∘ C). Relative humidity displayed an M-shaped curve, peaking at 56–86 = 0.97). Solar radiation and surface pressure showed overall declining trends, with elevated risks at lower levels (OR = 1.03 at 1.08 MJ/m ^2 ; OR = 1.01 at 99.82 kPa) and fluctuations across their central ranges. Total precipitation and wind speed showed rising trends, with ORs >1 above 15.1 mm and 5.0 m/s. Stronger associations were observed among individuals aged ≥ 6 years (e.g., temperature OR = 0.988, 95

Owing to the absence of straightforward and scientifically validated screening and evaluation tools for the timely identification, diagnosis, and treatment of critical influenza A infection in children, this study aimed to construct an effective model for the early identification of patients at high risk of progressing to critical influenza A infection. The prediction model was developed using the registration data of children diagnosed with influenza A who were admitted to Wuxi Children’s Hospital, the Children’s Hospital Affiliated to Soochow University and the Children’s Hospital Affiliated to Fudan University. Patients were randomly divided into a training group and a validation group at a 7:3 ratio. A logistic regression model was established based on the least absolute shrinkage and selection operator to construct the nomogram. The performance of the nomogram was evaluated by the area under the characteristic curve (AUC), calibration ability, decision curve analysis (DCA) and clinical impact curve analysis (CICA). A total of 170 hospitalized children with influenza A infection were identified, including 92 severe patients and 78 critical patients. The model was composed of the following five predictors: loss of appetite, seizure ≥ 2 times, altered neutrophil-to-lymphocyte ratios, haemoglobin levels, and total number of complications. The AUC of the model in the training set was 0.905, and the specificity and sensitivity were 91.1 https://iavchildren.shinyapps.io/DynNomapp/ ). This predictive model, which is based on clinical history and commonly used laboratory test values, is valuable for predicting the risk of critical influenza A infection in hospitalized children.

ObjectiveTo construct a nomogram for poor sleep quality in patients with systemic lupus erythematosus (SLE).MethodsClinical data from 218 SLE patients who visited a tertiary hospital’s rheumatology and immunology department in Chengdu, Sichuan Province, China, between 2021 and 2022 were analyzed. LASSO analysis and multivariate logistic regression were used to identify independent risk factors, and a nomogram was used to integrate and model the various risk factors. The model was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). Internal validation was conducted using the bootstrap method, and the clinical impact curve (CIC) was used to assess the clinical effectiveness of the predictive model.ResultsIn total, 104 patients (47.7%) had poor sleep quality, while 114 patients did not have poor sleep quality (52.3%). The nomogram for predicting poor sleep quality in patients had an area under the curve of 0.789, a sensitivity of 51.92%, and a specificity of 93.86%. The calibration curve closely approximated the ideal curve; DCA showed a threshold probability of 35%; the C-index was 0.789; and the CIC showed a threshold probability of 60%. These results indicate that the nomogram has good predictive accuracy and clinical utility.ConclusionWe constructed and validated a nomogram for poor sleep quality in patients with SLE, providing a convenient and reliable tool for the clinical prediction of poor sleep quality in these patients. Further multicenter studies are warranted to validate these findings and further elucidate the underlying mechanisms of sleep disturbances in SLE.

Introduction:Drug-resistant tuberculosis (DR-TB) constitutes a global public health crisis, which endangers patients' health, poses a significant transmission risk, and imposes a substantial strain on the healthcare system. Medication adherence is essential for enhancing treatment outcomes and mitigating the proliferation of DR-TB. Purpose:This study aims to explore the real-life dilemmas and facilitators affecting medication adherence in DR-TB patients and provide a reference for improving medication compliance in DR-TB patients. Patients and Methods:A descriptive qualitative study was conducted. 26 patients with DR-TB who were treated with oral medication regimen in a tertiary hospital in Luzhou City, Sichuan Province from March to May 2025 were selected through purposive sampling method for semi-structured interviews, and thematic analysis was used to analyze the data. Results:Five themes and fourteen sub-themes affecting medication adherence of DR-TB patients were identified, encompassing: Individual physiological traits (age-related variations in the perception of future time, polypharmacy in patients with comorbidities), intricate psychology and behaviors (misconceptions of medication effects, psychological distress resulting from stigma, misunderstanding of disease conditions, downward social comparison, divergences in medicine administration practices), synergy in social networks (multi-dimensional support of family members, support and communication from health providers), differences in family finances and living situations (significant family financial strain, influence of family roles), and constraints on medical insurance services (disparities in health insurance coverage, intricacy of the reimbursement procedure, constraints on reimbursement amounts and coverage). Conclusion:Adherence to medication among DR-TB patients is influenced by intricate factors. Health professionals should intervene on the basis of a comprehensive and dynamic assessment of medication adherence to address these influencing factors at various levels, thereby enhancing adherence and therapeutic outcomes.

BACKGROUND:Pharmacogenomics is used to identify genetic factors that influence drug responses, thereby optimizing therapeutic outcomes and reducing adverse effects. The objective of this study is to identify pharmacogenomic variations and their clinical relevance to drug metabolism and toxicity within the Pumi population. METHODS:Eighty-two genetic variants in 43 genes were genotyped in 200 unrelated Pumi individuals using the Agena MassARRAY Assay. Chi-square tests, adjusted for multiple comparisons with Bonferroni correction, were used to compare genotype frequency divergences between the Pumi population and 26 other populations. Population genetic structure diversity and pairwise F-statistics (Fst) were assessed across 27 populations using Structure v2.3.1 and Arlequin v3.5 software. RESULTS:After Bonferroni correction, a number of single nucleotide variations (SNVs) exhibited significant differences in frequency between the Pumi population and other populations. The allele frequencies of ADH1A rs975833, ADH1B rs1229984, TPMT rs1142345, and CYP2A6 rs8192726 in the Pumi population were notably different from the East Asian population or the other 26 populations. PharmGKB data indicate that rs1229984, rs1142345, and rs8192726 are associated with the metabolic efficiency of acetaldehyde, mercaptopurine, and efavirenz, respectively. Additionally, the genetic structure analysis (K = 5) and pairwise Fst calculations revealed that the Pumi population shared a similar genetic background with CHB (Fst = 0.031), JPT (Fst = 0.033), KHV (Fst = 0.035), CHS (Fst = 0.036), and CDX (Fst = 0.037) populations. CONCLUSION:Our findings reveal unique genetic variations and biomarkers within the Pumi population, which contributes pharmacogenomic insights and theoretical foundations for personalized medicine tailored to the Pumi population.

Overlap between primary sclerosing cholangitis (PSC) and systemic lupus erythematosus (SLE) has been documented in previous studies, but the precise causal relationships between them remain elusive. This study aims to clarify the potential causal links between PSC and SLE using Mendelian randomization (MR) and transcriptomic analyses. Genome-wide association study (GWAS) summary data for PSC and SLE were obtained from the IEU OpenGWAS database. The inverse variance weighted (IVW) method was the primary approach for assessing the causal links between PSC and SLE. Potential horizontal pleiotropy and heterogeneity were evaluated to ensure the reliability of the MR results. Additionally, transcriptomic analysis was performed using data from the Gene Expression Omnibus (GEO) database to uncover potential mechanisms insights involving overlapping genes and develop a diagnostic model for PSC and SLE. Genetically predicted PSC had a significant causal effect on SLE, with an OR of 1.190 (95% CI: 1.098-1.290, P ˂.001). Conversely, SLE had causal effect on PSC with an OR of 1.130 (95% CI: 1.046-1.221, P = .002). Multivariate MR analysis by adjustment of body mass index and smoking also revealed the bidirectional causal relationships between PSC and SLE. There were no signs of horizontal pleiotropy or heterogeneity, and the robustness of these results was confirmed via leave-one-out sensitivity analysis. Through integrated transcriptomic analysis and machine learning algorithms, 5 hub genes were identified. Furthermore, the diagnostic accuracy of the 5-gene signature was confirmed via Nomogram, calibration curve, and decision curve analysis, highlighting the diagnostic potential of these hub genes for both PSC and SLE. In conclusion, our MR study successfully confirms the bidirectional causal relationships between PSC and SLE, shedding light on the intertwined nature of these 2 conditions. Moreover, we identified 5 hub genes that potentially mediate the overlap between PSC and SLE, providing valuable insights for early diagnosis and future mechanistic exploration.

Infectious respiratory particles (IRPs) exhaled by infected patients significantly influence the safety of susceptible patients in fever clinic waiting areas. Understanding the impact of occupancy density (OD) on the IRPs transmission is essential. This study employed real-time CO2 monitoring to assess fever clinic ventilation performance. The findings revealed an average air change per hour (ACH) of 2.2, below the recommended 6 ACH for infection control. The effects of high, medium, and low OD on the IRPs transmission were analyzed using computational fluid dynamics at 2.2 ACH, considering scenarios where the infected patient was located upstream, downstream and in the seating area of the waiting area. The results showed that IRPs released from the upstream infector had the highest suspension rates, ranging from 14 % to 20 %, while IRPs from other locations had suspension rates below 5 %. Further analysis indicated that the maximum and upper quartile intake fraction (IF) in susceptible populations caused by upstream infectors decreased as OD decreased. At high OD, the upper quartile IF was 0.1 %, which was 1.5 and 2.1 times higher than at medium and low OD, respectively. This decreasing trend was not observed for downstream and seating area infectors. Significance test revealed that IF at high OD was significantly higher than at medium OD only at seating area infectors, with no significant difference in other scenarios. In conclusion, fever clinics with insufficient ventilation should prioritize increasing ventilation rates over merely reducing OD to control infection risks.

OBJECTIVE:To examine the comprehensive health impacts of exercise on people with cancer by systematically summarising existing evidence and assessing the strength and reliability of the associations. DESIGN:Umbrella review of meta-analyses. DATA SOURCE:PubMed, Embase, Cochrane and Web of Science databases were searched from their inception to 23 July 2024. ELIGIBILITY CRITERIA FOR SELECTING STUDIES:Meta-analyses of randomised controlled trials that investigated the associations between exercise and health outcomes among people with cancer. RESULTS:This umbrella review identified 485 associations from 80 articles, all evaluated as moderate to high quality using A Measurement Tool to Assess Systematic Reviews (AMSTAR). Two hundred and sixty (53.6%) associations were statistically significant (p<0.05), 81/485 (16.7%) were supported by high-certainty evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria. Compared with usual care or no exercise, moderate- to high-certainty evidence supported the view that exercise significantly mitigates adverse events associated with cancer and its treatments (eg, cardiac toxicity, chemotherapy-induced peripheral neuropathy, cognitive impairment and dyspnoea). Exercise also modulates body composition and biomarkers (eg, insulin, insulin-like growth factor-1, insulin-like growth factor-binding protein-1 and C-reactive protein) in people with cancer, and enhances sleep quality, psychological well-being, physiological functioning and social interaction, while improving overall quality of life. CONCLUSION:Exercise reduces adverse events and enhances well-being through a range of health outcomes in people with cancer.

BACKGROUND:Poor sleep quality is common among Chinese medical students. Identifying its predictors is essential for implementing individualized interventions. However, clinical prediction models targeting sleep quality in this population remain scarce. This study aimed to develop and validate a nomogram to predict poor sleep quality among Chinese medical students. METHODS:A cross-sectional study was used to collect data among Chinese medical students at the Hubei University of Medicine. A total of 2893 medical students were randomly divided into training (70%) and validation (30%) groups. Multivariable Firth logistic regression analysis was performed to examine factors associated with sleep quality. Thereafter, these factors were used to develop a nomogram for predicting sleep quality. The predictive performance was evaluated by receiver operating characteristic curve (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA). RESULTS:A total of 70.4% of medical students in the study reported poor sleep quality. The predictors of sleep quality included grade, gender, self-assessment of interpersonal relationships, and self-assessment of health status. The scores of the nomogram ranged from 0 to 189, and the corresponding risk ranged from 0.50 to 0.95. The calibration curve showed that the nomogram had good classification performance. The area under the curve (AUC) of the ROC for the training group is 0.676, and that for the validation group is 0.702. The DCA demonstrated that the model also had good net benefits. CONCLUSIONS:The nomogram prediction model has sufficient accuracies, good predictive capabilities, and good net benefits. The model can also provide a reference for predicting the sleep quality of medical students.

In China, children entering middle school are confronted with puberty, environmental changes, and increased academic pressure, all of which significantly increase the likelihood of experiencing psychological issues. Children’s mental health is crucial for their future development, and exploring efficient and cost-effective intervention methods is worth pursuing. This study selected 102 Shanghai middle school freshmen aged under 14 years as subjects to verify the intervention effect of poetry therapy on children’s negative emotions. The intervention materials are modern Chinese poems selected from the Chinese Ministry of Education’s mandatory reading list for primary and secondary school students. The results show that modern poetry therapy has a significant effect on children. This paper explores the emotional regulatory mechanism of poetry for children from the perspectives of literary defamiliarization and the subconscious in psychology. Poetry therapy may formally enter the field of professional attention in psychological counseling and group counseling for children in the future.

Background: Alzheimer’s disease (AD) currently lacks effective disease-modifying treatments. Recent research suggests that ferroptosis could be a potential therapeutic target. Mendelian randomization (MR) is a widely used method for identifying novel therapeutic targets. Objective: Employ genetic information to evaluate the causal impact of ferroptosis-related genes on the risk of AD. Methods: 564 ferroptosis-related genes were obtained from FerrDb. We derived genetic instrumental variables for these genes using four brain quantitative trait loci (QTL) and two blood QTL datasets. Summary-data-based Mendelian randomization (SMR) and two-sample MR methods were applied to estimate the causal effects of ferroptosis-related genes on AD. Using extern transcriptomic datasets and triple-transgenic mouse model of AD (3xTg-AD) to further validate the gene targets identified by the MR analysis. Results: We identified 17 potential AD risk gene targets from GTEx, 13 from PsychENCODE, and 22 from BrainMeta (SMR p < 0.05 and HEIDI test p > 0.05). Six overlapping ferroptosis-related genes associated with AD were identified, which could serve as potential therapeutic targets (PEX10, CDC25A, EGFR, DLD, LIG3, and TRIB3). Additionally, we further pinpointed risk genes or proteins at the blood tissue and pQTL levels. Notably, EGFR demonstrated significant dysregulation in the extern transcriptomic datasets and 3xTg-AD models. Conclusions: This study provides genetic evidence supporting the potential therapeutic benefits of targeting the six druggable genes for AD treatment, especially for EGFR (validated by transcriptome and 3xTg-AD), which could be useful for prioritizing AD drug development in the field of ferroptosis.

Acute myeloid leukemia (AML) patients with DNA methyltransferase 3A (DNMT3A) mutation display poor prognosis, and targeted therapy is not available currently. Our previous study identified increased expression of Exportin1 (XPO1) in DNMT3AR882H AML patients. Therefore, we further investigated the therapeutic effect of XPO1 inhibition on DNMT3AR882H AML. Three types of DNMT3AR882H AML cell lines were generated, and XPO1 was significantly upregulated in all DNMT3AR882H cells compared with the wild-type (WT) cells. The XPO1 inhibitor selinexor displayed higher potential in the inhibition of proliferation, promotion of apoptosis, and blockage of the cell cycle in DNMT3AR882H cells than WT cells. Selinexor also significantly inhibited the proliferation of subcutaneous tumors in DNMT3AR882H AML model mice. Primary cells with DNMT3A mutations were more sensitive to selinexor in chemotherapy-naive AML patients. RNA sequencing of selinexor treated AML cells revealed that the majority of metabolic pathways were downregulated after selinexor treatment, with the most significant change in the glutathione metabolic pathway. Glutathione inhibitor L-Buthionine-(S, R)-sulfoximine (BSO) significantly enhanced the apoptosis-inducing effect of selinexor in DNMT3AWT/DNMT3AR882H AML cells. In conclusion, our work reveals that selinexor displays anti-leukemia efficacy against DNMT3AR882H AML via downregulating glutathione pathway. Combination of selinexor and BSO provides novel therapeutic strategy for AML treatment.