, Available online , doi: 10.3967/bes2026.093
, Available online , doi: 10.3967/bes2026.104
, Available online , doi: 10.3967/bes2026.103
Ming Yang,
Jingtao Wu,
Lin Tao,
Weitian Tang,
Weitao Su,
Jiaxin Zhao,
Shengmei Zhang,
Yanbing Li,
Yaoyu Hu,
Ang Li,
Yichao Huang
, Available online , doi: 10.3967/bes2026.100
, Available online , doi: 10.3967/bes2026.098
, Available online , doi: 10.3967/bes2026.092
, Available online , doi: 10.3967/bes2026.085
, Available online , doi: 10.3967/bes2026.082
, Available online , doi: 10.3967/bes2026.071
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.
, Available online , doi: 10.3967/bes2026.107
Objective Pancreatic ductal adenocarcinoma (PDAC) is characterized by numerous severely hypoxic areas that drives tumor progression. Hypoxia-inducible factor-1 (HIF-1) mediates hypoxic responses mainly through HIF-1α upregulation. However, how HIF-1α influences PDAC aggressiveness remains unclear. Doublecortin-like kinase 1 (DCLK1) is overexpressed in PDAC and facilitates tumor development. This study investigated whether hypoxia regulates DCLK1 to enhance PDAC malignancy. Methods Bioinformatics analyses of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were used to assess the prognostic values of HIF-1α and hypoxia score, and the correlation between HIF-1α and DCLK1 expression. mRNA and protein levels of HIF-1α, DCLK1, and epithelial-mesenchymal transition (EMT) markers were detected by reverse transcription polymerase chain reaction and Western blot. Transwell assays detected cell migration and invasion after the inhibition of either HIF-1α or DCLK1. Immunofluorescence staining was performed to analyze the association among HIF-1α, DCLK1, and EMT markers in PDAC tissues. Results Our study demonstrated significant overexpression of HIF-1α and DCLK1 in pancreatic cancer cells under hypoxic conditions. Hypoxia-driven HIF-1α upregulates DCLK1, fostering EMT and enhancing PDAC malignancy. DCLK1 inhibition in PDAC mitigates hypoxia-induced proliferation, invasiveness, and EMT progression. Conclusion HIF-1α boosts DCLK1 in hypoxia, enhancing pancreatic cancer malignancy via EMT. Overall, targeting DCLK1 in the hypoxic tumor microenvironment of PDAC could be a promising therapeutic strategy.
, Available online , doi: 10.3967/bes2026.106
Objective Measles elimination is threatened by the virus’s high infectivity (R0 = 12–18), waning immunity, and persistent immunity gaps. No global study has compared measles seroprevalence before and after elimination. We assessed whether countries that had eliminated measles achieved and maintained the herd immunity threshold and identified key vulnerabilities. Methods For countries with WHO-confirmed measles elimination, we systematically searched PubMed, Web of Science, and Google Scholar for seroprevalence data covering the three years before and the three years after elimination verification. Sample-size-weighted medians and interquartile ranges were calculated to describe seroprevalence, with stratified analyses by age group and country. The overall seroprevalence in the three years before and after elimination verification was also compared. Results Among 31 countries with measles elimination status (351 datasets comprising 562,772 participants), the overall median seroprevalence was 92.8%. Seroprevalence increased significantly with age (P < 0.001), from 56.5% among infants (aged 0–2 years) to 96.8% among older adults (aged > 40 years). Twenty-six percent of the countries had a seroprevalence of ≥ 92%, indicating a low risk of measles transmission, whereas 45% had a seroprevalence of < 85%, indicating a substantial transmission risk. The median seroprevalence decreased significantly from 94.9% during the three years before elimination verification to 83.5% during the three years after verification (P < 0.001). Conclusions Achieving measles elimination does not guarantee that population-level protection will be sustained. Despite high overall seroprevalence, important immunity gaps persist among infants and young children, and some countries do not maintain the required herd immunity threshold following elimination. Targeted interventions are needed to close immunity gaps and strengthen national population immunity to prevent measles resurgence.
Linmei Yao,
Chuanchun Mao,
Yuan Tian,
Yao Lu,
Lu Zhang,
Ge Shen,
Shuling Wu,
Min Chang,
Hongxiao Hao,
Leiping Hu,
Yuanjiao Gao,
Mengjiao Xu,
Yao Xie,
Minghui Li,
Ruyu Liu
, Available online , doi: 10.3967/bes2026.105
Background Serum markers predicting virologic relapse after pegylated interferon-alpha (Peg-IFN-α) discontinuation in patients with chronic hepatitis B (CHB) are lacking. This study aimed to evaluate serum pregenomic RNA (pgRNA), hepatitis B core-related antigen (HBcrAg), and hepatitis B surface antigen (HBsAg) as potential predictors of Peg-IFN-α treatment outcomes and post-treatment virologic relapse in treatment-naïve patients with CHB. Methods A total of 371 treatment-naïve patients with CHB were initially recruited for this study, and 327 patients who completed the entire follow-up period were included in the data analysis. Serum pgRNA and HBcrAg levels were measured by polymerase chain reaction (PCR)-fluorescence probing and enzyme-linked immunosorbent assay (ELISA), respectively. Results Among the 327 followed patients, 112 (34.25%) achieved a virologic response to Peg-IFN-α; 16 (14.29%) experienced virologic relapse over a median follow-up of 12 months. Baseline pgRNA (odds ratio [OR], 0.571), HBcrAg (OR, 0.539), and HBsAg (OR, 0.356) levels correlated with treatment efficacy, demonstrating predictive values with AUCs of 0.821, 0.879, and 0.781, respectively. HBcrAg levels at treatment discontinuation were associated with virologic relapse and showed potential as predictive biomarkers (AUC, 0.790). Conclusions Serum pgRNA, HBcrAg, and HBsAg correlate with Peg-IFN-α response in treatment-naïve CHB. Serum HBcrAg levels at Peg-IFN-α discontinuation show potential as a predictive biomarker for post-treatment virologic relapse and are associated with relapse risk, which may help identify high-risk patients after Peg-IFN-α cessation. Notably, this finding requires further validation in larger cohorts due to the limited number of relapse events.
, Available online , doi: 10.3967/bes2026.101
Objective To identify critical exposure windows and thresholds of maternal residential greenness during pregnancy in relation to preterm birth (PTB). Methods Using the sibling-matched birth cohort data from the "China Green City" Nanning, China (2016–2022), conditional logistic regression and restricted cubic splines (RCS) were applied to assess the nonlinear dose-response associations between normalized difference vegetation index (NDVI) and PTB risk across different pregnancy periods and buffer zones. Results The median seasonal NDVI in Nanning was approximately 0.80, with relatively small differences between seasons. Pre-pregnancy residential NDVI (500 m buffer) was associated with higher PTB risk (adjusted odds ratio [OR] per interquartile range [IQR] = 1.16; 95% CI: 1.05–1.27), showing an upward trend with a risk threshold above 0.80. Third-trimester residential NDVI (500 m buffer) was associated with lower PTB risk (adjusted OR per IQR= 0.59; 95% CI: 0.54–0.65), showing an L-shaped dose-response curve with an inflection point at NDVI 0.30. Conclusion The third trimester and pre-pregnancy periods represent critical windows for the association between maternal residential greenness and PTB. Therefore, optimal levels of maternal residential greenness during pregnancy may reduce the risk of PTB.
Jianan Li,
Yiqi Qiu,
Mike Z. He,
Jianlong Fang,
Yu Wang,
Chenfeng Li,
Yueqiao Zhou,
Jiaonan Wang,
Chen Chen,
Chen Mao,
Xiaoming Shi
, Available online , doi: 10.3967/bes2026.091
Objectives While climate warming has driven concurrent heatwave-ozone events, the magnitude of their joint health impacts remains insufficiently quantified. Particularly, the specific ozone (O3) thresholds that trigger changes in heatwave mortality risks have not yet been determined. Methods A time-series study was performed using mortality data (2013–2018) from 59 counties across the Beijing-Tianjin-Hebei region and surrounding areas in China. Applying quasi-Poisson generalized linear models, the study quantified exposure-response relationships, additive interactions, and attributable fractions for the concurrent events. Heatwaves and O3 pollution events were classified by intensity and duration and combined to define different types of concurrent events. Results The study found that when O3 levels exceeded 130 μg/m3 for at least two days during heatwaves, significant additive interactions markedly intensified mortality risks, particularly for circulatory diseases. These concurrent events increased non-accidental mortality by 13.5%, along with circulatory (16.9%), respiratory (13.9%), nervous system (24.1%), and type 2 diabetes (27.0%) mortality. However, nervous system mortality exhibited unique sensitivity to O3 concentrations during concurrent exposure, following a distinct upward trend. Women, older adults (≥ 65 years), and individuals with circulatory diseases were identified as vulnerable populations. From 2013 to 2018, 29,730 deaths were attributable to concurrent events (52.8% circulatory), with the attributable fraction rising by 0.15% annually. Conclusions These findings provide essential empirical evidence for developing an integrated smart early-warning framework, establishing feasible protocols for alert grading, and supporting the design of targeted interventions for vulnerable populations.
, Available online , doi: 10.3967/bes2026.090
Background Type 2 diabetes mellitus (T2DM) is linked to social determinants of health (SDOH); however, long-term trends in its prevalence and the association with SDOH remain unclear. Thus, we investigated national trends in T2DM prevalence and the cumulative burden of SDOH. Methods We analyzed data from the Korea National Health and Nutrition Examination Survey (2007–2024), including adults aged ≥ 19 years. T2DM was defined by physician diagnosis, use of anti-diabetic medication, fasting glucose ≥ 126 mg/dL, or hemoglobin A1c ≥ 6.5%. SDOH was assessed across seven key domains—education level, marital status, household income, food security, type of health insurance, employment status, and home ownership— and classified into low- (≤ 2) and high-burden (> 2) groups; the cumulative score was also analyzed as ordinal and continuous variables to evaluate dose-response relationships. Trends were examined using weighted linear regression, and adjusted odds ratios (aORs) were estimated using multivariable logistic regression across pre- (2007–2019), intra- (2020–2022), and preliminary post-pandemic (2023–2024) periods. Age-standardized estimates and sensitivity analyses, including inverse probability weighting and mediation analysis, were performed to ensure robustness. Results A total of 85,695 adults (males; 35,392, 41.30%) were included. The prevalence of T2DM showed increasing trend (8.55%, 95% CI: 7.96% to 9.13% in 2007–2009; 11.66%, 95% CI: 11.11% to 12.22% in 2016–2019), and a surge was observed at the onset of the pandemic in 2020, peaking at 13.66% (95% CI:12.24% to 15.07%) in 2021 before rebounding to 14.19% (12.99% - 15.39%) in 2024. High cumulative SDOH was associated with T2DM (aOR=1.33, 95% CI: 1.25 to 1.41), showing a clear dose-response relationship where the risk progressively escalated with higher SDOH scores (score ≥ 6: 1.79 [1.58 to 2.04]). Among individual components, lack of private health insurance showed the strongest association (aOR, 1.30 [95% CI: 1.22 to 1.39]). Other major risk factors included established demographic and clinical predictors such as older age, male, and obesity. Conclusions T2DM prevalence has increased over time, and a high cumulative burden of SDOH remains consistently associated with this prevalence, highlighting the need for equity-focused public health strategies.
, Available online , doi: 10.3967/bes2026.089
Objective Rescue workers are frequently exposed to traumatic events and are at a high risk of developing post-traumatic stress disorder (PTSD). We developed and compared machine-learning models for PTSD risk and assessed their generalizability. Methods This multicenter cross-sectional study included 13,462 rescue workers from 13 units for model development/internal validation and 8,757 workers from two additional units for external validation. Probable PTSD was defined as a PTSD Checklist for DSM-5 score ≥ 33. Sixteen variables were reduced using principal component analysis (PCA) or exploratory factor analysis (FA). Nine machine-learning algorithms were trained using a stratified 70:30 split, 5-fold cross-validation, and synthetic minority oversampling. Results Among PCA-based models, extreme gradient boosting (XGBoost) achieved the highest external area under the curve (AUC, 0.8931), whereas random forest showed the highest sensitivity. Among the factor-analysis-based models, XGBoost achieved the highest external AUC (0.8832), whereas random forest provided a sensitivity-oriented alternative. Original-variable SHapley Additive exPlanations (SHAP) analysis identified acute stress disorder (ASD), self-rating depression scale (SDS), self-rating anxiety scale (SAS), passive smoking, monthly family income, smoking status, body mass index (BMI), and age as the main contributors. Conclusion Dimensionality-reduced machine-learning models showed measurable discriminatory ability for PTSD risk stratification, and model choice should reflect screening priorities.
Yue Chen,
Wanying Shi,
Huijie Chang,
Liang Ding,
Chen Chen,
Yingli Qu,
Zhenyi Yin,
Yongmei Wang,
Zhanhong Xue,
Fanye Long,
Luxi Wei,
Caihong Jiang,
Peipei Dong,
Ying Zhu,
Yuebin Lyu,
Xiaoming Shi
, Available online , doi: 10.3967/bes2026.086
Objective The prospective cohort study aimed to examine the link between blood Manganese (Mn) concentrations and incident cognitive impairment risk in Chinese elderly individuals. Methods This study enrolled 6,868 individuals aged 60 years and above from two cohort studies from 2017 to 2024. High-resolution inductively coupled plasma mass spectrometry was employed to quantify blood Mn concentrations. Cognitive function was evaluated by Mini-Mental State Examination (MMSE). Cox proportional hazards models were used to assess the association between blood Mn levels and risk of cognitive impairment, while linear mixed-effects models were used to examine their associations with MMSE and domain-specific cognitive scores. Restricted cubic spline (RCS) was confirmed to explore the dose-response relationship. Results During an average follow-up of 4.00 years, 1,138 developed cognitive impairment. The mean blood Mn concentration across all participants was 11.297 ± 3.763 µg/L. After full adjustment for covariates, higher blood Mn levels (per interquartile range [IQR] increase) were significantly associated with greater declines in MMSE score (β = −0.033, 95% confidence interval [CI]: −0.048, −0.018) and in multiple domain-specific scores, including orientation, attention and calculation, language, naming, and recall, with the strongest associations observed for naming (β = −0.043, 95% CI: −0.070, −0.015) and recall (β = −0.047, 95% CI: −0.064, −0.030). Individuals in the highest quartile (Q4) of blood Mn levels exhibited a 28.5% greater risk of cognitive impairment (Adjusted hazard ratio [HR] =1.285, 95% CI: 1.081, 1.526) versus the lowest quartile (Q1) of blood Mn levels. The risk of cognitive impairment was 13.1% higher for each IQR increase in blood Mn (HR = 1.131, 95% CI: 1.051, 1.217). A positive and linear dose-response pattern was confirmed by the RCS model (P for non-linearity > 0.05). Conclusions In this population-based prospective cohort study of older adults, higher blood Mn levels were significantly associated with poorer cognitive performance and an increased risk of cognitive impairment, with the most pronounced associations observed for naming and recall ability, suggesting that reducing Mn exposure could serve as a modifiable target for cognitive health protection among older adults.
, Available online , doi: 0.3967/bes2026.084
Objective To investigate the epidemiology and risk factors for chronic urticaria (CU) in Chinese adults with cardiometabolic diseases (CMDs), and its effect on CMD recognition and management. Methods We conducted a nationwide cross-sectional analysis using data from the 2018–2019 China Chronic Disease and Risk Factor Surveillance, which included 118,036 adults with CMDs. Weighted CU prevalence and CMD awareness, treatment, and control rates (stratified by CU status) were estimated using a complex survey design, whereas risk factors for CU were identified using multivariable logistic regression. Results The weighted CU prevalence among patients with CMDs was 2.90%, peaking at 3.94% in 60–69-year-old individuals, and was higher in females (3.41%) than in males (2.56%), especially in rural areas. Key risk factors included current smoking (odds ratio [OR] 1.17, 95% confidence interval [CI]: 1.06–1.29) and abnormal sleep duration (<5 h: OR 1.79, 95% CI: 1.61–1.99; >10 h: OR 1.39, 95% CI: 1.19–1.61). Patients with CU showed stronger association with improved CMD awareness (hypertension: 50.71% vs. 40.27%; diabetes: 47.14% vs. 36.21%; dyslipidemia: 30.71% vs. 17.12%) and treatment rates (hypertension: 41.37% vs. 34.22%; diabetes: 41.31% vs. 32.52%; dyslipidemia: 18.29% vs. 9.94%) than non-CU patients but with comparable control rates. Conclusion CU is prevalent in patients with CMD and associated with distinct demographic and lifestyle risk factors. Although CU facilitates earlier detection of CMD, it does not improve disease control, indicating the need for integrated dermatology-cardiometabolic care.
Qiao Song,
Wen Li,
Yang Liu,
Sigen Li,
Leyang Ju,
Jingrong Cao,
Shuo Gao,
Zhichen Liao,
Yaqi Wang,
Yuli Hou,
Haixia Ma,
Yunxiu Zhang,
Diandian Chen,
Wenshuo Yang,
Xiang Yang,
Qiliang Li,
Peichang Wang
, Available online , doi: 10.3967/bes2026.083
Objective Reduced brain energy metabolism is a key feature of Alzheimer's disease (AD); however, lactate dehydrogenase (LDH)-related metabolic changes in AD are not fully understood. This study aimed to evaluate serum LDH activity in patients with mild cognitive impairment (MCI) and AD. We used public transcriptomic datasets to explore the features of LDHA and LDHB across brain regions, cell types, and co-expression networks. Method In a retrospective clinical cohort of 132 healthy controls (HC), 87 patients with MCI, and 103 patients with AD, we compared serum LDH activity after adjusting for major clinical covariates. Next, we used public transcriptomic datasets (AlzData, Agora, CELLxGENE, and genotype-tissue expression (GTEx)) to evaluate regional and cell-type-specific expression patterns of LDHA and LDHB. We also characterized LDH-associated functional networks using GTEx normal brain data for baseline co-expression analysis and AD-related weighted gene co-expression network analysis (WGCNA). Results Clinical cohort analysis showed that serum LDH activity was significantly decreased in patients with MCI and AD. Serum LDH activity did not significantly correlate with mini-mental state examination (MMSE) or montreal cognitive assessment (MoCA) scores. Brain transcriptomic analysis revealed that LDHA expression was significantly downregulated in AD-related regions (entorhinal cortex, hippocampus, and temporal cortex), whereas LDHB showed a downward trend in several AD-related regions. Both genes were detectable across multiple brain cell types with relatively prominent expression in neurons. Functional analysis showed that, under normal conditions, LDHA-correlated genes were broadly involved in glycolysis, vesicle trafficking, autophagy, and proteostasis, whereas LDHB-correlated genes were highly concentrated in mitochondrial oxidative phosphorylation and the citric acid (TCA) cycle. AD-related WGCNA showed region-dependent organization of LDHA- and LDHB-containing modules, with repeated enrichment in synaptic vesicle-related processes, mitochondrial respiration, autophagy, and proteostasis-related pathways. Conclusion This study provides clinical and transcriptomic evidence of LDH-related metabolic alterations in patients with AD. Reduced serum LDH activity in MCI and AD, together with decreased LDHA/LDHB expression in AD-related brain regions and region-dependent LDH-associated co-expression networks, supports a potential link between LDH-related metabolism and mitochondrial energy metabolism, synaptic function, and proteostasis in AD. These findings should be interpreted as exploratory, and require validation through paired prospective and mechanistic studies.
, 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.
, 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.
, Available online , doi: 10.3967/bes2026.102
Objective Wildfires have intensified globally; however, evidence of their long-term, multi-disease health impacts across countries at varying development levels remains scarce. Therefore, this study aims to quantify the associations between wildfires and cause-specific mortality while accounting for socioeconomic disparities. Methods Using an ecological panel dataset covering 62 countries from 2000 to 2023, we quantified the global associations between annual wildfire activity, measured using a satellite-derived burn index, and age-standardized mortality from 22 outcomes. Results After adjusting for socioeconomic indicators and temporal trends using mixed-effects models, higher wildfire activity was found to be independently associated with increased all-cause mortality and significantly more deaths from cardiovascular, digestive, and metabolic diseases. Stratified analyses revealed pronounced heterogeneity according to the Human Development Index (HDI): high-HDI countries experienced the largest mortality burden and the broadest range of affected chronic diseases, whereas low-HDI countries showed stronger links to deaths from HIV/AIDS and violence. Conclusion These findings indicate that the health risks of wildfires are neither uniform nor confined to respiratory outcomes but instead reflect the intersection of climate-related hazards, demographic transitions, and disease structure. As wildfires intensify under global warming, our results underscore the need for adaptive health systems and climate policies that consider the differential vulnerabilities across developmental contexts.
, Available online , doi: 10.3967/bes2026.087
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