Huihuan Luo,
Yuanting Xie,
Xinyi Fang,
Bin Pan,
Yalan Xiao,
Jingyu Li,
Xiaoqing Hong,
Dongyang Han,
Wenyue Tu,
Haidong Kan,
Yanyi Xu,
Renjie Chen
, Available online , doi: 10.3967/bes2026.059
Objective Prior epidemiological research demonstrated an association between short-term exposure to fine particulate matter (PM2.5) and acute diabetic events, specifically diabetic ketoacidosis (DKA). However, mechanistic investigations remain lacking to substantiate biological link. Methods Twenty 18-week-old male BKS db/db mice were randomly assigned to two groups (n = 10 per group). Ambient PM2.5 suspension (5 mg/kg in 50 μL) or an equal volume of phosphate-buffered saline was intratracheally instilled once daily for three consecutive days. Within 24 hours after the final instillation (Day 3), serum β-hydroxybutyrate was quantified, and liver tissues were collected for transcriptomic profiling (RNA-seq) to explore potential mechanisms linking PM2.5 to ketone body levels (i.e., β-hydroxybutyrate). Results The PM2.5 group exhibited higher 3-hydroxybutyric acid levels than controls. The liver transcriptome differed significantly between groups. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses indicated differentially expressed genes were primarily associated with lipid metabolism. Further, 43 genes exhibited moderate-to-strong correlations with 3-hydroxybutyric acid (16 positive, 27 negative; coefficients 0.56 – 0.76). These genes are involved in fatty acid oxidation, lipogenesis, lipid transport, glucose metabolism, and inflammation. Conclusion PM2.5 exposure may enhance ketogenesis through disruption of hepatic glucolipid metabolism, providing mechanistic insight into its potential role in acute diabetic metabolic decompensation.
Zhilei Xu,
Ruiling Liu,
Yuling Hu,
Quanzi Yao,
Xin Luo,
Jialiang He,
Lihong Geng,
Lijuan Fu,
Zhaohui Zhong,
Yubin Ding,
Xingyu Lyu
, Available online , doi: 10.3967/bes2026.072
Objective Fine particulate matter (PM2.5) may impair follicular development; however, the roles of its chemical components and their time-specific effects remain unclear. This study examined how PM2.5 and its major components are related to oocyte-related outcomes among women undergoing assisted reproductive technology (ART) and identified critical exposure windows. Methods A total of 51,122 ART cycles were analyzed. Individual exposures to PM2.5 and its components, sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), organic matter (OM), and black carbon (BC), were estimated for three periods: recent (0–3 months), distal (4–12 months), and cumulative (0–12 months). Associations between total, mature, and normally fertilized oocytes were assessed using negative binomial regression. Distributed lag non-linear models (DLNM) identified sensitive windows, and mixture models evaluated joint effects. Results Higher PM2.5 and component exposures were consistently associated with poorer oocyte outcomes, with stronger effects for distal exposure. Two sensitive windows, 1 month and 6–11 months before retrieval, were identified. Mixture analyses indicated SO42- and NH4+ as the dominant contributors. Conclusion Exposure to PM2.5 showed component-specific and time-dependent reproductive toxicity. Both short- and long-term exposure may reduce oocyte quantity and quality, highlighting the importance of improving air quality to support female reproductive health and ART success.
, Available online , doi: 10.3967/bes2026.063
Objective Intensive-care-unit–acquired weakness (ICU-AW), including critical illness polyneuropathy (CIP), critical illness myopathy (CIM), and critical illness neuromyopathy, is a common neuromuscular complication of sepsis. An interpretable machine-learning model for the early prediction of ICU-AW in patients with sepsis was developed and validated using the Medical Information Market for Intensive Care (MIMIC)-IV v3.1 database and local hospital data. Methods A total of 3,842 adult patients who met the Sepsis-3 criteria were enrolled to create the MIMIC-IV database. ICU-AW was defined as per International Classification of Diseases codes in the MIMIC cohort and with a Medical Research Council score of ≤ 48 in the external cohort. Baseline demographics, vital signs, severity scores, and laboratory data within the first 48 h of intensive care unit (ICU) admission were recorded. Features were selected using least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. The dataset was split into training and validation sets in a 7:3 ratio. Seven machine-learning models were constructed: LightGBM, XGBoost, logistic regression, Naïve Bayes, random forest, CatBoost, and a support vector machine. Model performance was assessed in terms of the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis. SHapley Additive explanations (SHAP) analysis was used to interpret the optimal model. Results Among 3,842 patients, 203 (5.28%) were diagnosed with CIM/CIP. Seven key features were selected using the LASSO and Boruta methods. The random forest model performed the best, with an AUC of 0.772 in the validation set and 0.753 in the external cohort. It exhibited good calibration and the highest net benefit. The SHAP analysis revealed that early antibiotic use, early mechanical ventilation, sequential organ failure assessment scores, and age were the main predictors of ICU-AW. Conclusion A random forest model using early ICU data could effectively predict the risk of ICU-AW in patients with sepsis and offer interpretation via SHAP. Thus, it may serve as a clinical decision-making tool for early risk identification and optimized prevention.
, Available online , doi: 10.3967/bes2026.061
Objective Traditional disease prevention strategies that rely on fixed parameters and macro-level models struggle to capture the diversity of individual behaviors and environmental complexities. Indoor spaces with high population densities and poor ventilation, such as schools and hospitals, are particularly vulnerable to pathogen transmission. The coronavirus disease (COVID-19) pandemic highlighted the need for precise intervention strategies. Methods We developed a spatial-individual agent-based model that integrates fine-grained spatiotemporal dynamics, where transmission risk is quantified by the exact distance and duration of contact. This model was applied to a high-resolution case study of a university dormitory floor to evaluate various testing frequencies, scopes, and isolation intensities. Results Simulations showed that a dormitory-wide isolation policy outperformed individual restrictions by protecting uninfected rooms. Counter-intuitively, dormitory-based testing every three days lowered infection risks compared to daily class-based testing by minimizing high-density interactions. In spatially constrained environments, stricter isolation reduces the overall outbreak duration but increases the contact transmission rate among individuals sharing the same enclosed space. Conclusion Epidemic control in high-density environments requires balancing testing frequency and isolation stringency based on spatial constraints. Under strict isolation, frequent testing is vital for breaking transmission chains. In less restrictive settings, moderately reducing the testing frequency minimizes unnecessary contact. These findings provide data-driven guidance for optimizing public health policies on campuses.
, Available online , doi: 10.3967/bes2026.065
Objective Evidence regarding the association between long-term ozone exposure and chronic obstructive pulmonary disease (COPD) has primarily originated from high-income countries, with limited studies in China. Methods This nationwide cross-sectional study included 66,752 Chinese adults. Patients with COPD were identified using post-bronchodilator spirometry. Long-term ozone exposure was estimated using the average ozone concentrations in the grid cells covering the participants’ residential counties. Logistic regression was used to analyze the ozone–COPD association, adjusting for individual-level risk factors and socioeconomic factors. Additive interaction models were employed to assess the modification of the ozone–COPD association by county-level gross domestic product (GDP) per capita and temperature. Results Each 10-µg/m3 increase in annual ozone exposure was significantly associated with a higher risk of COPD (odds ratio [OR]: 1.172, 95% confidence interval [CI]: 1.039−1.322). In comparison with counties in the highest quartile of GDP per capita, the association between ozone exposure and COPD was stronger in counties in the lowest quartile of GDP per capita (P < 0.05). Counties with lower winter temperatures exhibited a stronger ozone–COPD association than those with warmer winters (P < 0.05). The relative excess risks due to the interaction of ozone with GDP per capita and winter temperature were 0.219 (95% CI: 0.095–0.344) and 0.254 (95% CI: 0.103–0.404), respectively. Conclusion Socioeconomically disadvantaged and colder regions exhibited greater susceptibility to ozone-related COPD. Targeted interventions aimed at these vulnerable countries are needed to mitigate inequalities in ozone-related COPD.
, Available online , doi: 10.3967/bes2026.070
Objective Evidence regarding the association between long-term ozone exposure and chronic obstructive pulmonary disease (COPD) has primarily originated from high-income countries, with limited studies in China. Methods This nationwide cross-sectional study included 66,752 Chinese adults. Patients with COPD were identified using post-bronchodilator spirometry. Long-term ozone exposure was estimated using the average ozone concentrations in the grid cells covering the participants’ residential counties. Logistic regression was used to analyze the ozone–COPD association, adjusting for individual-level risk factors and socioeconomic factors. Additive interaction models were employed to assess the modification of the ozone–COPD association by county-level gross domestic product (GDP) per capita and temperature. Results Each 10-µg/m3 increase in annual ozone exposure was significantly associated with a higher risk of COPD (odds ratio [OR]: 1.172, 95% confidence interval [CI]: 1.039−1.322). In comparison with counties in the highest quartile of GDP per capita, the association between ozone exposure and COPD was stronger in counties in the lowest quartile of GDP per capita (P < 0.05). Counties with lower winter temperatures exhibited a stronger ozone–COPD association than those with warmer winters (P < 0.05). The relative excess risks due to the interaction of ozone with GDP per capita and winter temperature were 0.219 (95% CI: 0.095–0.344) and 0.254 (95% CI: 0.103–0.404), respectively. Conclusion Socioeconomically disadvantaged and colder regions exhibited greater susceptibility to ozone-related COPD. Targeted interventions aimed at these vulnerable countries are needed to mitigate inequalities in ozone-related COPD.
, Available online , doi: 10.3967/bes2026.058
Background Traditional Health Technology Assessments (HTAs) commonly overlook the broader societal and economic externalities of vaccines, leading to systematic undervaluation and suboptimal resource allocation. This study aimed to develop and validate a comprehensive vaccine value framework and prioritize individual elements for future HTA integration. Methods A two-phase mixed-methods approach was employed for framework development. Phase 1 involved a systematic literature review of major databases to construct an initial conceptual framework. Phase 2 utilized a two-round modified Delphi study involving a multidisciplinary expert panel to validate and refine the framework. Six evaluative criteria, categorized under the dimensions of "Relevance" and "Feasibility," were weighted and applied to score each value element. Finally, a comparative analysis of the raw and weighted scores was conducted to identify five priority value elements for future integration into HTAs. Results The final validated framework comprised 5 value categories, 21 value elements, and 75 actionable value items. Although traditional metrics achieved the highest consensus, the following five "broader" elements emerged as top priorities for future inclusion: (1) Enhancement of Health System Security, (2) Macroeconomic Gains, (3) Social Equity and Ethics, (4) Prevention of Institutional Disruptions, and (5) Value to Other Interventions. Conclusion This study established a standardized multitiered roadmap to capture the multifaceted value of vaccines. By introducing actionable Tier-3 indicators, the framework operationalizes the assessment of broader vaccine benefits and offers a practical tool to support equitable and comprehensive evidence-based policymaking. Furthermore, the identification of the five priority value elements provides a feasible pathway for integrating extended vaccine externalities into future HTAs. Ultimately, this standardized framework will facilitate holistic decision-making and support the optimal allocation of resources within national immunization programs.
Wenxuan Zhao,
Yu Wang,
Changzhen Xiang,
Chenfeng Li,
Chen Chen,
Jiaonan Wang,
Jianlong Fang,
Feng Lu,
Kai Chen,
Shilu Tong,
Jie Ban,
Xiaoming Shi
, Available online , doi: 10.3967/bes2026.062
Objective City-specific tools for assessing and warning about respiratory disease risks are underdeveloped, limiting effective public health response. This study aimed to develop and validate a novel city-specific prediction framework (WHAair-LSTM) for forecasting daily respiratory outpatient visits by integrating a composite air pollution health index. Methods Based on over 223.7 million hospital visits across multiple megacities, we constructed and validated a five-level morbidity-driven composite air pollution index (WHAair) for each city using city-specific exposure-response relationships. An LSTM model was built using WHAair, temperature, humidity, and historical visit data to predict next-day visits. The proposed modeling framework was developed with city-level data, and it was externally validated using datasets from other cities. Results Higher WHAair levels were significantly associated with increased outpatient visits. The model demonstrated excellent predictive performance (Beijing: R2 = 0.963, RMSE = 53.5) and effectively captured visit surges. Excluding WHAair degraded model accuracy (ΔRMSE = +44.1%). The framework maintained robust performance in external validation, confirming its transferability. Conclusion The WHAair-LSTM framework provides a scalable and practical tool for city-level respiratory disease early warning by bridging environmental monitoring with clinical practice.
Qingyin Bu,
Qian Wang,
Gang Zhang,
Yifan Wang,
Longhu Sun,
Shuang Liang,
Fan Yang,
Zhazheng He,
Honggang Yi,
Zhening Pu,
Juncheng Dai
, Available online , doi: 10.3967/bes2026.069
Objective To investigate the effects of metabolic syndrome (MetS) and its interaction with genetic factors on lung cancer incidence and mortality. Methods The cohort analysis included 355,344 participants from the UK Biobank. MetS was defined using the modified National Cholesterol Education Program Adult Treatment Panel III criteria. Cox proportional hazards models were used to evaluate the associations between MetS-related variables, their interactions with genetic factors, and lung cancer outcomes (incidence and mortality). Results MetS was associated with increased risks of lung cancer incidence (hazard ratio [HR]: 1.31, 95% confidence interval [CI]: 1.22–1.42) and mortality (HR: 1.35, 95% CI: 1.24–1.48). Risk increased proportionally to the number of metabolic abnormalities. Increased waist circumference, reduced high-density lipoprotein cholesterol, and elevated glycated hemoglobin were independently associated with both outcomes. Participants with both high genetic risk and MetS had the highest risk of lung cancer incidence (HR: 2.07, 95% CI: 1.82–2.35) and mortality (HR: 2.12, 95% CI: 1.83–2.45) compared with those with low genetic risk and no MetS. A significant positive additive interaction was observed between waist circumference and genetic risk. Conclusion Metabolic abnormalities are important modifiable risk factors for lung cancer. Integrating metabolic health assessment with genetic risk profiling may improve risk stratification and targeted prevention of lung cancer.
, Available online , doi: 10.3967/bes2026.057
Objective This study aimed to comprehensively characterize the genomic diversity, evolutionary dynamics, pathogenic potential, antimicrobial resistance, and secondary metabolite capacity of the Nocardia genus using whole-genome analyses. Methods We analyzed 751 publicly available Nocardia genomes using genome-based species delineation, phylogenomics, pangenome analysis, and comparative functional profiling to assess taxonomy, virulence, antibiotic resistance genes (ARGs), and biosynthetic gene clusters (BGCs). Results Phylogenomic analyses resolved five major clades: N. farcinica, N. carnea, N. asteroides, N. transvalensis, and N. otitidiscaviarum groups. The pangenome is open, comprising 467,566 gene clusters and reflecting extensive genomic diversity. Virulence factors and ARGs exhibit clade-specific patterns: the N. farcinica group harbors the most complete virulence repertoire and diverse resistance determinants, whereas the N. carnea and N. asteroides groups carry fewer genes. Analysis of 10,196 BGCs across 46 classes revealed conserved clusters of non-ribosomal peptide synthetases, terpenes, and type I polyketide synthases, with higher biosynthetic potential in the N. farcinica, N. transvalensis, and N. otitidiscaviarum groups. Several genomes encode BGCs associated with antibacterial or anticancer compounds. Conclusion This comprehensive genome analysis of Nocardia, representing the most complete sampling to date, clarifies phylogeny, reclassifies misassigned strains, identifies potential novel species, and reveals clade-specific patterns of virulence, resistance, and secondary metabolism.
Lu Yu,
Zheng Li,
Peijie Sun,
Shuyang Yan,
Wanying Shi,
Wenqi Hao,
Wanling Li,
Mingkun Yu,
Dejin Yang,
Yingli Qu,
Saisai Ji,
Wenli Zhang,
Feng Zhao,
Yawei Li,
Haocan Song,
Jiayi Cai,
Ying Zhu,
Song Tang,
Feng Tan,
Yuebin Lv,
Xiaoming Shi
, Available online , doi: 10.3967/bes2026.045
Objective To investigate associations between heavy metals and metalloids (HMMs) exposure and hepatic fibrosis risk, and to explore the modifying role of thyroid hormones. Methods Using nationally representative data of 9,543 adults from the China National Human Biomonitoring, hepatic fibrosis risk was assessed with the Fibrosis-4 index (FIB-4). Weighted logistic and linear regression models evaluated links between 13 HMMs and fibrosis outcomes. Dose-response relationships were modeled with restricted cubic splines, and subgroup analyses explored potential effect modification. Results Blood cobalt (Co) (OR = 1.613, 95% CI: 1.126-2.310) and blood manganese (Mn) (OR = 1.699, 95% CI: 1.238-2.331) showed nonlinear positive associations with hepatic fibrosis risk, while urinary tin (Sn) (OR = 0.888, 95% CI: 0.797-0.990) was inversely associated. Low triiodothyronine (T3) levels increased Co-induced fibrosis risk and may enhance the protective effect of Sn, while high T3 levels exacerbated Mn-related risk. Stratified analysis by thyroxine (T4) levels showed directionally consistent associations with the main findings. Conclusion Blood Co and Mn nonlinearly increased hepatic fibrosis risk, urinary Sn reduced it. T3 levels modulated these metal-specific risks, highlighting thyroid hormones as potential modifiers in HMMs-induced hepatotoxicity.
, Available online , doi: 10.3967/bes2026.043
Objective Immunoglobulin G (IgG) N-glycosylation is associated with mild cognitive impairment through the regulation of inflammatory balance; however, the underlying mechanisms remain unclear. Methods Our study utilized a post-genome-wide association studies (GWAS) method that integrated GWAS data for cognitive function with gene expression quantitative trait loci (eQTL), protein QTL (pQTL), and IgG N-glycan-QTL data. Results Mendelian randomization (MR) analyses suggested bidirectional causalities between glycan peaks (GPs) and cognitive function, with GP7, GP12, and GP19 showing a causal effect on cognitive function, while cognitive function conversely showed a causal effect on GP1 and GP8. Two proteins and 10 genes were implicated in the regulation of IgG N-glycosylation. Furthermore, multivariable MR results suggested complex causalities between genes/proteins and IgG N-glycans, which jointly promote or independently affect cognitive function. Conclusion Our study reveals a novel mechanism by which genes, proteins, and modified IgG N-glycans converge to pathologically affect cognitive function.
Fengjie Wang,
Yutong Zhou,
Ri De,
Runan Zhu,
Yu Sun,
Dongmei Chen,
Liping Jia,
Qi Guo,
Yao Yao,
Zhen Zhu,
Naiying Mao,
Linqing Zhao
, Available online , doi: 10.3967/bes2026.032
Objective LLC-MK2/TMPRSS2 cells constitutively express TMPRSS2, eliminating the requirement for additional trypsin during HPIV3 culture. The efficiency of LLC-MK2/TMPRSS2 for isolating HPIV3 from respiratory specimens was evaluated in comparison with Madin-Darby Canine Kidney (MDCK). Methods HPIV3-positive respiratory specimens from children with acute respiratory infections (February-June 2025) were inoculated into LLC-MK2/TMPRSS2 and MDCK. The cytopathic effect (CPE) was monitored microscopically, and the proportion of positive cells was evaluated using direct immunofluorescence assay (DFA). Viral infection dynamics were assessed using the cycle threshold (Ct) values obtained by qPCR. Results Among 50 specimens, 35 strains (35/50, 70%) were successfully isolated using LLC-MK2/TMPRSS2, while 14 strains were isolated using MDCK (14/50, 28%). More pronounced CPE and a higher number of virus-infected positive cells were shown in LLC-MK2/TMPRSS2 compared to that in MDCK (P < 0.001 and P = 0.001, respectively). Among specimens with an initial Ct < 27, the isolation rate of LLC-MK2/TMPRSS2 was higher and the Ct values were lower (< 27) (82.6%, 19/23). Among specimens with an initial Ct of 23 ≤ Ct < 27, the number of specimens with a supernatant Ct ≥ 27 (63.6%, 7/11) was significantly less than that in MDCK (P = 0.003). Conclusion LLC-MK2/TMPRSS2 exhibits superior adaptability and replication efficiency in the isolation of HPIV3 from respiratory specimens.
, Available online , doi: 10.3967/bes2026.071
Yanyan Zhou,
Keyi Yu,
Ming Liu,
Zhenzhou Huang,
Yanqing Che,
Mengyu Shi,
Zhenpeng Li,
Xiaoli Du,
Duochun Wang,
Liyan Ma,
Li Yu
, Available online , doi: 10.3967/bes2026.068
, Available online , doi: 10.3967/bes2026.056
, Available online , doi: 10.3967/bes2026.024
, Available online , doi: 10.3967/bes2026.016
Objective To investigate the association between occupational high-temperature exposure and accelerated biological aging. Methods A total of 140 male workers exposed to occupational high-temperatures and 207 male non-exposed control workers were selected as study subjects. Questionnaire surveys and health examinations were conducted. Biological age and organ-specific biological age were calculated using the Klemera–Doubal method. Generalized linear models were used to analyze the effects of occupational high-temperature exposure, body mass index (BMI), smoking, alcohol consumption, and sleep duration on biological age (BA) acceleration and organ-specific biological age. Results Significant differences were observed between the exposed and control groups in length of service, systolic blood pressure, red blood cell count, albumin levels, urea, creatinine, BA acceleration, and liver–kidney BA acceleration (P < 0.05). Compared with the control group, which showed a BA acceleration of 0.04 ± 1.34 years, the exposed group demonstrated significantly higher BA acceleration of 0.62 ± 1.31 years. After adjustment for covariates, workers exposed to high-temperatures exhibited significantly higher BA acceleration and liver–kidney BA acceleration than controls (P < 0.001). High-temperature exposure and BMI were associated with BA acceleration, with a significant interaction between the two factors (P < 0.05). High- temperature exposure, BMI, and smoking were identified as risk factors for BA acceleration, whereas sleep duration was a protective factor (P < 0.05). Conclusion Occupational high-temperature exposure may accelerate biological aging. An interaction exists between occupational high-temperature exposure and BMI in relation to BA acceleration.
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