To investigate the spatiotemporal epidemiological characteristics and temporal trends of influenza in Fujian Province, China, from 2005 to 2022. Descriptive epidemiological methods were used to analyze the temporal, demographic, occupational, and regional distribution of influenza cases. Spatial autocorrelation analysis, trend surface analysis, and spatiotemporal scan statistics were applied to explore the spatial distribution and clustering characteristics of influenza. A total of 215,338 influenza cases were reported in Fujian Province during 2005–2022, with the incidence rate showing an overall fluctuating upward trend over time (Z = 4.394, P < 0.001). Children aged 0–5 years and students accounted for the largest proportions of reported cases. Influenza activity demonstrated marked temporal and seasonal variation, with relatively higher activity observed in summer and winter. Trend surface analysis indicated higher case concentrations in eastern and central regions of Fujian Province. Significant positive spatial autocorrelation was identified in multiple years, and six significant spatiotemporal clusters were detected, mainly distributed in southeastern Fujian. Influenza in Fujian Province showed an increasing annual incidence trend, marked temporal fluctuation, and dynamic spatial clustering during 2005–2022. These findings may support more targeted influenza surveillance, prevention, and resource allocation in high-risk populations and areas.

(1) Background: This study investigated the characteristics and influencing factors of exposure to fine particulate matter (PM2.5) and its chemical composition among elderly residents, with the aim of revealing potential differences in exposure. (2) Methods: A total of 258 elderly individuals were monitored for 72 h through individual, indoor, and outdoor PM2.5 measurements. Concentrations were determined, and non-targeted components were analyzed by gas chromatography-mass spectrometry (GC-MS). Through Spearman correlation analysis, generalized linear model, and linear regression to explore the influencing factors. (3) Results: The individual PM2.5 concentration was higher than both the indoor and outdoor concentrations. A total of 20,962 compounds were detected in personal PM2.5 samples, 6794 in indoor PM2.5 samples, among which 4285 compounds were shared between the two sample types. The components were mainly esters, aromatic compounds, and amines. PM2.5 concentration was correlated with age, housing area, humidifier use, and second-hand smoke exposure. Chemical composition is related to outdoor pollution, furniture material, and daily behavior. (4) Conclusions: The individual PM2.5 concentration is higher than the environmental concentration, and its chemical composition overlaps with the indoor and outdoor environment, which is jointly affected by demography, living conditions, and daily behavior.

1,3,6,8-Tetrabromocarbazole (1368-BCZ), an emerging dioxin-like persistent organic pollutant, can enter the body through multiple routes including dietary intake, inhalation, and dermal contact. Growing concerns have been raised about its potential toxicity, particularly regarding its poorly characterized neurotoxic effects. This study aims to elucidate the neurotoxic effects of the emerging pollutant 1368-BCZ by establishing a subchronic exposure mouse model, and to investigate the underlying metabolic disruption mechanisms in the hippocampus that contribute to neurodegenerative damage, through an integrated approach combining neurobehavioral, histopathological, and multi-omics analyses. Neurobehavioral assessments revealed significant neurodegenerative damage in exposed mice, as evidenced by a decreased recognition index (∼10 %), prolonged escape latency (∼20 s), reduced time spent in the target quadrant (5-10 s), and fewer platform crossings (1-2 times) in behavioral tests. Histopathological examination demonstrated distinct damage in the hippocampus, a brain region crucial for episodic memory formation and spatial navigation. Furthermore, 1368-BCZ exposure significantly upregulated neurodegenerative-related proteins in hippocampal tissues, including Aβ (∼3-fold) and phosphorylated Tau (p-Tau, ∼1.5-2.0 fold) relative to the control group. Untargeted metabolomic analyses revealed that 1368-BCZ exposure-induced significant metabolic disturbances in mouse hippocampus, including the downregulation of 602 metabolites and upregulation of 78 metabolites. Integrated transcriptomic and untargeted metabolomic analyses revealed that 1368-BCZ exposure-induced the downregulation of Dpys, Mlycd, and Pank1 mediated the disruption and the cross-talk of the pantothenate and CoA biosynthesis pathway and the β-alanine metabolism pathway might be primarily contributed to neurodegenerative damage in mice. Collectively, our findings demonstrate that subchronic 1368-BCZ exposure induces significant neurotoxicity by disrupting hippocampal metabolic networks, ultimately leading to neurodegenerative damage in mice. This study not only characterizes the neurotoxic effects of 1368-BCZ but also establishes a link between its subchronic exposure and neurodegeneration in vivo, thereby firstly providing mechanistic insights into the neurotoxicity of this emerging persistent pollutant and underscoring its potential environmental health risks.

Fine particulate matter (PM2.5) exposure is linked to elevated fasting plasma glucose (FPG), but roles of metal constituents and mechanisms remain unclear. We examined cumulative lagged effects of short-term exposure to PM2.5 and its metal constituents on FPG in older adults, identified metal sources, and assessed whether systemic inflammation, measured by the systemic inflammation response index (SIRI), statistically mediated these associations. This 2024 longitudinal cohort enrolled 109 non-diabetic older adults from four communities in Hefei city, Anhui province, with baseline and three follow-ups measuring FPG and SIRI. PM2.5 and 19 metal constituents were estimated for the 1-7 days prior to each visit. Linear mixed-effects (LME) models were used to assess single-pollutant associations, while weighted quantile sum (WQS) regression and Bayesian kernel machine regression (BKMR) were applied to evaluate mixture effects. Positive matrix factorization (PMF) was used to apportion metal sources, and mediation modeling was employed to examine the potential mediating role of SIRI. At lag0-3 days, each 10 μg/m³ increase in PM2.5 was associated with a 4.921% rise in FPG (95% CI: 2.138%, 7.781%). Additionally, 14 metals showed positive associations, with uranium (U), selenium (Se), and manganese (Mn) exhibiting the largest effects. WQS and BKMR indicated that Mn and cesium (Cs) were the primary contributors to the positive mixture effect. PMF attributed Mn to fuel oil combustion and Cs to coal combustion. SIRI statistically mediated the associations between PM2.5, several metals (aluminum (Al), arsenic (As), barium (Ba), gallium (Ga), manganese (Mn)), and FPG. Short-term exposure to PM2.5 and its metal constituents, especially Mn, is associated with higher FPG in the elderly, and these associations are partially explained by systemic inflammation levels.

Background: Gastric cancer remains a leading cause of cancer-related deaths worldwide. Although significant progress has been made in clinical diagnosis and treatment, the molecular mechanisms underlying gastric cancer have not yet been fully elucidated. To address this, this study employs a multi-omics approach to systematically analyze the molecular characteristics of gastric cancer. Methods: This case–control study enrolled 218 GC patients and 218 healthy controls, and adopted a multi-omics strategy combining inductively coupled plasma mass spectrometry (ICP-MS), element-related genome-wide association study (eGWAS), and untargeted metabolomics to explore the element-gene-metabolite regulatory axis in GC. Results: A total of nine plasma differential elements associated with gastric cancer were identified, with a combined diagnostic accuracy of 0.918. Specifically, elements such as Fe, Co, and Li showed significant correlations with 63 genes involved in key signaling pathways, including MAPK, SMAD, and Wnt. Genome-wide association studies (GWAS) revealed that gastric cancer-related genes were significantly enriched in cancer-associated pathways and signaling cascades such as Rap1. Metabolomic analysis further demonstrated that 20 elements in the gastric cancer cohort correlated with 94 metabolites, predominantly enriched in pyrimidine and glutathione metabolism pathways. Conclusions: These nine plasma differential elements showed high combined diagnostic efficacy and were associated with genes and metabolites enriched in cancer-related signaling, metabolic reprogramming, and DNA damage response pathways. Together, these findings suggest potential multi-level associations among plasma elemental alterations, genetic variation, and metabolic dysregulation in GC, providing candidate circulating biomarkers and mechanistic clues for future investigation.

Background/Objectives: Ambient fine particulate matter (PM2.5) poses significant health risks to older adult populations, yet the specific contributions of its chemical constituents, particularly non-phthalate and non-organophosphate ester compounds, remain poorly understood. This study aimed to elucidate the mechanistic links between PM2.5-bound ester exposures and metabolic pathway alterations in elderly individuals. Methods: A total of 258 elderly residents aged 60 years or older from Fuzhou, China, were recruited. Personal PM2.5 exposure was monitored over 72 h using UPAS V2 samplers, with chemical components analyzed via gas chromatography–mass spectrometry (GC–MS). Plasma metabolomic profiling was conducted using liquid chromatography–mass spectrometry (LC–MS), and metabolic pathway enrichment was performed using MetaboAnalyst 5.0. Linear regression models adjusted for covariates (age, sex, BMI, lifestyle factors) assessed associations between ester exposures and metabolite abundance. Results: The mean PM2.5 concentration was 38.06 μg/m3, with ester compounds dominating the chemical composition. Twenty high-concentration non-target esters were prioritized for analysis. PM2.5 ester exposure was associated with alterations in key metabolic pathways, including steroid biosynthesis, glycolysis/gluconeogenesis, glycerophospholipid metabolism, and purine/pyrimidine metabolism. When interpreted alongside prior epidemiological evidence, these alterations represent putative links to increased risks of insulin resistance, cardiovascular dysfunction, and metabolic syndrome—relationships that require confirmation in prospective cohort studies and controlled toxicological experiments. Conclusions: Putatively annotated PM2.5-bound ester compounds, particularly non-regulated subclasses, are associated with systemic metabolic alterations in older adults, coincident with perturbations in steroid and lipid metabolism. While these findings are exploratory and hypothesis-generating, they highlight the need to incorporate specific ester profiles into PM2.5 risk assessments and develop targeted interventions for vulnerable aging populations.

BACKGROUND:To project the future disease burden of dengue fever in Fujian Province by accounting for anticipated changes in climate and population, and to inform the development of targeted and effective dengue prevention and control strategies as well as public health interventions; METHODS: We applied a distributed lag nonlinear model (DLNM) calibrated using locally acquired dengue cases from 2015 to 2019 and linked it to future temperature projections from four global climate models under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). These projections were integrated with population scenarios to estimate the temperature-attributable dengue burden through 2099; RESULTS: Dengue risk increased across all scenarios. By 2090-2099, heat exposure (referenced to 18 °C) was associated with a 1.68-fold higher excess risk of dengue fever (95% CI: 1.01-1.89) under SSP1-2.6, 1.50-fold higher (1.38-1.64) under SSP2-4.5, and 1.74-fold higher (1.59-1.88) under SSP5-8.5. Notably, the moderate-emission scenario (SSP2-4.5) yielded a lower risk than both low- and high-emission pathways, reflecting the nonlinear temperature-dengue relationship in this transitional transmission region; CONCLUSIONS: The burden of dengue fever in Fujian Province is projected to increase under all climate change scenarios examined. These findings underscore the need of proactive and climate-informed public health planning to mitigate the growing dengue risk associated with global warming.

Background: Fine particulate matter (PM2.5) and wastewater may carry severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and provide early signals for coronavirus disease 2019 (COVID-19) surveillance. This study investigated environmental factors affecting SARS-CoV-2 distribution in PM2.5 and wastewater in Fuzhou. Methods: PM2.5 and wastewater samples collected from January to December 2023 were tested for SARS-CoV-2 RNA using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Associations between air pollutants and meteorological factors were analyzed using generalized additive models (GAMs) and distributed lag nonlinear models (DLNMs). Results: SARS-CoV-2 was detected in 45.9% of PM2.5 samples. PM2.5 viral concentrations were positively associated with CO, PM2.5, and atmospheric pressure, and negatively associated with temperature and sunshine duration. Wastewater viral concentrations were positively associated with relative humidity and precipitation and negatively associated with NO2 and SO2. PM2.5 signals showed the strongest lag effect at approximately 2 days, while wastewater signals peaked at lag 0. The lag effect herein describes the time interval between environmental SARS-CoV-2 signals and predicted COVID-19 outpatient cases. Elderly populations showed higher environmental sensitivity, with sex-specific differences observed. Conclusions: SARS-CoV-2 signals in PM2.5 and wastewater reflected COVID-19 trends. Integrated multi-media monitoring may improve early warning and support targeted public health interventions.

Circadian rhythm disruption is associated with cognitive impairments, yet the mechanisms involving cholinergic signaling remain elusive. This study investigated whether shift work alters cholinergic factors and impairs cognition through the basal forebrain–hippocampal pathway. In human subjects, we assessed cognitive performance using the Montreal Cognitive Assessment (MoCA) and measured serum cholinergic biomarkers in shift workers and non-shift workers. In parallel, male C57BL/6 J mice subjected to circadian disruption were used to model shift-work–induced cognitive deficits, and male ChAT-IRES-Cre mice were employed for optogenetic interrogation of the basal forebrain–hippocampal cholinergic projection. Shift workers exhibited lower MoCA scores and altered cholinergic factor profiles compared with non-shift workers. In mice, circadian disruption caused cognitive impairment, reduced cholinergic neuron excitability, and disrupted synaptic transmission in the basal forebrain–hippocampal circuit. Optogenetic activation of this cholinergic pathway rescued cognitive performance in ChAT-IRES-Cre mice. These findings suggest that shift work–associated cognitive decline may involve cholinergic dysregulation, and that the basal forebrain–hippocampal cholinergic projection represents a potential circuit-level target for circadian-related cognitive impairment. Circadian disruption modeling unveils the role of basal forebrain–hippocampal cholinergic signaling in shift-work cognition and mice.

Background: This study was performed to evaluate the early warning value of wastewater-based epidemiology (WBE) in monitoring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its correlation with population-level coronavirus disease 2019 (COVID-19) infection trends. Methods: Wastewater samples from Fuzhou’s Sewage Treatment Plant A were concentrated via membrane filtration and quantified using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Viral load data were integrated with sentinel hospital positivity rates and respiratory outpatient visits from 11 city hospitals. Stratified cross-correlation lag analysis was performed by gender, age, and hospital type. Results: Using the lowest single-day genome concentration as a proxy for daily SARS-CoV-2 levels was advantageous. Wastewater viral concentrations correlated positively with clinical cases, with peaks preceding reports by 0 to 17 days. Stratified analysis further indicated that women, older adults, and individuals from general hospitals were more sensitive to changes in wastewater viral loads, showing stronger correlations between infection trends and wastewater signals. Conclusions: Wastewater surveillance of SARS-CoV-2 can effectively predict COVID-19 infection trends and offers a scientific basis for stratified and targeted interventions. The findings underscore the value of WBE as an early warning tool in public health surveillance.

Objective:The aim of this study was to analyze the effects of short-term exposure to low concentrations of air pollutants on the volume of respiratory outpatient visits in hospitals and their lagged effects. Methods:The study collected outpatient data from seven hospitals in Fuzhou City, air pollution data provided by the Fuzhou Environmental Monitoring Center Station, and meteorological data from the Fuzhou Meteorological Bureau for analysis from 2019 to 2022. Time series analysis was used to explore the relationship between air pollutants and meteorological factors and daily outpatient visits for respiratory diseases by constructing a generalized linear model (GLM). Results:From 2019 to 2022, the total outpatient volume of respiratory diseases in 7 hospitals in Fuzhou was 1,530,000, with pediatrics accounting for 72.44% and internal medicine accounting for 27.56%. Air pollutants such as PM2.5, PM10, NO2, and SO2 all had significant impacts on the total respiratory and pediatric respiratory outpatient volumes. NO2 and PM10 had the greatest impact on respiratory diseases on the day of pollution exposure or 1 day later, while SO2 and PM2.5 exhibited longer lag effects, with the most significant impact occurring at a lag period of 4-6 days. The impact of air pollution on pediatric respiratory disease outpatient visits was generally more significant than that on adult. Conclusion:Low concentrations of air pollution significantly impacted respiratory outpatient visits in Fuzhou, especially in children. Despite relatively good air quality, air pollution in low-pollution areas poses a public health risk, highlighting the need for targeted pollution control policies.

BACKGROUND:Previous studies have found that exposure to fine particulate matter (PM2.5) is associated with metabolic alterations, but the specific effects of its composition on metabolic changes remain unclear. OBJECTIVE:To identify key metabolites associated with PM2.5 exposure and its elemental components, with validation through animal experiments. METHODS:A total of 112 healthy older adults were enrolled in the cross-sectional study between winter 2020 and summer 2021. Individual PM2.5 exposure levels were quantified by personal samplers, and ten trace elements in PM2.5 were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Animal experiments were conducted on male rats to validate the associations between PM2.5, its elemental composition, and metabolomic changes. Fasting venous blood samples from participants and rat heart blood were collected for non-targeted metabolomic analysis using Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) and pathway enrichment analysis via MetaboAnalyst 5.0. RESULTS:PM2.5 exposure was significantly associated with changes in 218 plasma metabolites (p < 0.05). Non-targeted metabolomic analysis identified 29 and 7 metabolic pathways associated with PM2.5 exposure and trace elements, respectively, primarily associated with lipid metabolism, including linoleic acid metabolism. Significant overlaps of metabolomic pathways were observed in chromium (Cr) and PM2.5. A total of 29 human metabolic pathways affected by PM2.5 exposure were identified in the human study, of which 17 common to both humans and rats, including glycerophospholipid metabolism. These metabolites and pathways are related to metabolic disorders, with PM2.5-related metabolites showing associations with diseases like diabetes (P = 0.049). CONCLUSIONS:These findings highlight the important role of Cr in PM2.5-induced metabolic changes, emphasizing the need to monitor specific PM2.5 components for assessing health risks and informing public health policies.

Background:Atmospheric oxidative pollutants, air temperature, and heatwave events pose potential threats to public health. However, the combined effects of these factors on the risk of mortality from circulatory disease remain insufficiently studied. This study aims to evaluate the synergistic effects of ozone (O₃), oxidant (Ox), and nitrogen dioxide (NO₂) with temperature and heat waves, and to explore their impact on circulatory disease mortality, providing evidence to support public health interventions. Methods:Based on the mortality, meteorological, and environmental protection data of residents in Fuzhou City from January 1, 2016, to December 31, 2022, we employed a generalized additive model (GAM) and a distributed lag nonlinear model (DLNM) to assess the effects of atmospheric oxidative pollutants interacting with temperature and heat waves on the risk of death from circulatory diseases, where temperature includes the daily maximum temperature and diurnal temperature range (DTR). A bivariate three-dimensional model was used to visualize their synergistic effects, and stratified analyses were conducted to compare differences between heat wave and non-heat wave periods. Results:O3, Ox, and NO2 exhibit synergistic effects with ambient temperature, and their combined exposure significantly increases the mortality risk of circulatory system diseases, myocardial infarction, and stroke, with some health effects showing a "nonlinear exposure-response relationship with an inverted U-shaped pattern." Under heatwave conditions, the synergistic effect between NO2 and high temperatures is markedly enhanced, leading to a greater increase in mortality risk compared to O3 and Ox, and demonstrating both a same-day lag and a cumulative effect. After introducing other pollutants into the dual-pollution model, NO2 still shows a strong independent health effect on major causes of death during heatwaves, with the most pronounced risk elevation observed for stroke. Conclusion:Atmospheric oxidative pollutants interact with high temperatures, diurnal temperature range, and heatwaves, significantly increasing the risk of mortality. It is essential to integrate air pollution and meteorological factors to strengthen health protection during high-risk periods.

Background:Fine particulate matter (PM2.5) is a well-known air pollutant and has been suggested as a potential vector for airborne viruses, raising public health concerns. This study employed metaviromic sequencing to systematically analyze the composition, temporal-spatial distribution, and environmental influencing factors of viral communities in PM2.5 samples collected from Fuzhou, China, to identify potential high-risk viruses and the key factors influencing their presence. Methods:Three outdoor PM2.5 sampling sites were established in the city center, rural-urban fringe, and rural areas of Fuzhou. Samples were collected from December 2022 to August 2023. The collected PM2.5 samples underwent high-throughput sequencing and viral annotation, and statistical analysis along with multivariate regression analyses were used to investigate the characteristics of viral distribution and its influencing factors. Results:A total of 117 PM2.5 samples were collected. The viral community diversity in PM2.5 exhibited significant seasonal variation (p < 0.05), with the highest number of viral species detected in winter at both the genus and species levels. In terms of regional distribution, the highest number of viruses was found in city center and the lowest in rural areas, while there were slight differences in viral composition among regions, these were not statistically significant. Additionally, analysis of environmental factors revealed that sulfur dioxide (SO2) in the air quality factor and wind speed in the meteorological factor influenced the relative abundance of viruses. Discussion:Urbanization and human activities may affect regional viral patterns, but the overall improved air quality in Fuzhou could have reduced regional disparities. Environmental factors such as SO2 and wind speed may influence viral survival and dispersion, suggesting that non-traditional pollutants warrant closer attention in the context of airborne virus transmission.

This study aimed to investigate the synergistic effects of co-exposure to occupational heat and noise on multi-system health outcomes among Chinese workers, and to examine the mediating role of systemic inflammation, indicated by white blood cell (WBC) count. A cross-sectional analysis was conducted using data from the Fujian Workplace Occupational Hazards Comprehensive Surveillance Program (2020–2022), encompassing 10,275 workers from the manufacturing, petrochemical, mining, and construction industries. Multivariate logistic regression, relative excess risk due to interaction (RERI), random forest with SHAP values, and mediation analysis were used to explore interaction effects and mediating pathways. Co-exposure to heat and noise in the workplace was associated with increased risks of hypertension (OR = 1.94, 95

ObjectiveTo investigate the association between occupational heat exposure and hyperuricemia among petrochemical workers.MethodsWe retrospectively analyzed the association between workplace heat exposure and hyperuricemia by using 10 years of occupational health examination records from 2,312 petrochemical workers in Fujian Province, China. Generalized linear models (GLMs) were employed to estimate the effects of individual exposures. Weighted quantile sum (WQS) regression model was used to evaluate the combined effects of multiple occupational exposures and to identify the relative contribution of each exposure factor. A hyperuricemia risk prediction model was developed using the LightGBM machine-learning algorithm, with feature importance assessed using SHAP (SHapley Additive exPlanations) values.ResultsOccupational heat exposure was significantly associated with an increased risk of hyperuricemia (OR = 1.68, 95% CI: 1.28–2.20). In the GLM analysis, co-exposure to heat with benzene (OR = 1.93, 95% CI 1.05–3.55), H2S (OR = 3.38, 95% CI 1.94–5.88), gasoline (OR = 2.58, 95% CI 1.49–4.48), acid anhydride (OR = 2.21, 95% CI 1.09–4.48) and CO (OR = 2.14, 95% CI 1.16–3.97) further increased the risk (all p < 0.05), suggesting synergistic effects. The WQS analysis indicated that in the mixed occupational hazards exposure, heat exposure (49.2%) contributing nearly half the effect to the overall effect. The LightGBM machine learning model identified length of service, age, BMI, gender, and heat exposure as the main predictors of hyperuricemia. The SHAP analysis confirmed heat exposure as a key independent contributor alongside length of service.ConclusionOccupational heat exposure in petrochemical settings is significantly associated with hyperuricemia, suggesting potential early renal dysfunction risk. Integrating machine learning–based predictive models into workplace health surveillance may facilitate the early identification and management of high-risk workers. However, causal inference remains limited by the retrospective design and potential residual confounding, underscoring the need for prospective studies to validate and extend these findings.

BackgroundEmerging evidence links fine particulate matter (PM2.5) and its polycyclic aromatic hydrocarbon (PAH) components to adverse health outcomes. However, the biological mechanisms driving these associations remain unclear. This study innovatively integrates personal exposure monitoring and untargeted metabolomics in an older adult population to investigate the differential impacts of individual PM2.5-bound PAHs on metabolic pathways and elucidate their roles in health risks.MethodsIn this study, we enlisted the participation of 112 healthy older adults. We employed personal samplers to monitor the concentrations of pollutants throughout the study period. Furthermore, we conducted an untargeted metabolomic analysis of plasma samples using a liquid chromatograph mass spectrometer (LC–MS). A general linear regression model was utilized to investigate the significant relationships between metabolites and pollutants. Metabolic pathway enrichment analysis was performed to reveal the disturbed metabolic pathways related to PM2.5-bound PAHs.ResultsOur study demonstrated that short-term exposure to PM2.5-bound PAHs may induce acute perturbations in plasma metabolites among the older adult population. We found that exposure to LMW PAHs in PM2.5 were correlated with amino acid metabolic pathways, while HMW-PAHs are associated with fatty acid and cholesterol metabolism pathways. While PM2.5 mass was higher in summer, the toxic PAHs component of PM2.5 was substantially higher in winter, contributing to greater observed toxicity.ConclusionThe plasma metabolome presents a promising resource for biomarkers and pathways, elucidating the biological mechanisms of PM2.5-bound PAHs. Our findings suggest that the cholesterol and citric acid metabolites, as well as the cholesterol biosynthesis and citric acid cycle pathways they affect, may play important roles in the health damage caused by PAHs, providing potential insights into the pathogenic processes underlying the impact of PM2.5-bound PAHs.

This cross-sectional study examined the health impacts of occupational dust exposure on workers in Fujian Province, China, using data collected from 2020 to 2021. The primary objective was to assess the associations between occupational dust exposure and several adverse health outcomes, including abnormal chest X-ray (Abn-CXR), abnormal pulmonary function tests (Abn-PFTs), pneumoconiosis (PC), abnormal electrocardiograms (Abn-ECGs), abnormal liver function tests (Abn-LFTs), hypertension (HTN), and hearing loss (HL). logistic regression models were employed to identify significant risk factors. Stratified analyses by age and gender were performed to evaluate demographic differences in health risks. The results showed that workers currently employed, those with over 10 years of dust exposure, and workers exposed to silica, cement, or coal dust had a higher risk of Abn-CXR, Abn-PFTs, PC, Abn-ECGs, Abn-LFTs, HTN, and HL. Stratified analyses further revealed that male workers and individuals over 40 years old experienced a higher risk of abnormal health outcomes. These findings underscore the urgent need for targeted interventions, improved protective measures, and stricter occupational safety regulations to reduce the health burden associated with dust exposure in the workplace.

BackgroundThe impact of heat waves and atmospheric oxidising pollutants on residential mortality within the framework of global climate change has become increasingly important.ObjectiveIn this research, the interactive effects of heat waves and oxidising pollutants on the risk of residential mortality in Fuzhou were examined. Methods We collected environmental, meteorological, and residential mortality data in Fuzhou from 1 January 2016, to 31 December 2021. We then applied a generalised additive model, distributed lagged nonlinear model, and bivariate three-dimensional model to investigate the effects and interactions of various atmospheric oxidising pollutants and heat waves on the risk of residential mortality.ResultsAtmospheric oxidising pollutants increased the risk of residential mortality at lower concentrations, and O3 and Ox were positively associated with a maximum risk of 2.19% (95% CI: 0.74-3.66) and 1.29% (95% CI: 0.51-2.08). The risk of residential mortality increased with increasing temperature, with a strong and long-lasting effect and a maximum cumulative lagged effect of 1.11% (95% CI: 1.01, 1.23). Furthermore, an interaction between atmospheric oxidising pollutants and heat waves may have occurred: the larger effects in the longest cumulative lag time on residential mortality per 10 mu g/m3 increase in O3, NO2 and Ox during heat waves compared to non-heat waves were [-3.81% (95% CI: -14.82, 8.63)]; [-0.45% (95% CI: -2.67, 1.81)]; [67.90% (95% CI: 11.55, 152.71)]; 16.37% (95% CI: 2.43, 32.20)]; [-3.00% (95% CI: -20.80, 18.79)]; [-0.30% (95% CI: -3.53, 3.04)]. The risk on heat wave days was significantly higher than that on non-heat wave days and higher than the separate effects of oxidising pollutants and heat waves.ConclusionsOverall, we found some evidence suggesting that heat waves increase the impact of oxidising atmospheric pollutants on residential mortality to some extent.

Background Studies have shown that chronic exposure to job stress may increase the risk of sleep disturbances and that hypothalamic‒pituitary‒adrenal (HPA) axis gene polymorphisms may play an important role in the psychopathologic mechanisms of sleep disturbances. However, the interactions among job stress, gene polymorphisms and sleep disturbances have not been examined from the perspective of the HPA axis. This study aimed to know whether job stress is a risk factor for sleep disturbances and to further explore the effect of the HPA axis gene × job stress interaction on sleep disturbances among railway workers. Methods In this cross-sectional study, 671 participants (363 males and 308 females) from the China Railway Fuzhou Branch were included. Sleep disturbances were evaluated with the Pittsburgh Sleep Quality Index (PSQI), and job stress was measured with the Effort-Reward Imbalance scale (ERI). Generalized multivariate dimensionality reduction (GMDR) models were used to assess gene‒environment interactions. Results We found a significant positive correlation between job stress and sleep disturbances (P < 0.01). The FKBP5 rs1360780-T and rs4713916-A alleles and the CRHR1 rs110402-G allele were associated with increased sleep disturbance risk, with adjusted ORs (95% CIs) of 1.75 [1.38–2.22], 1.68 [1.30–2.18] and 1.43 [1.09–1.87], respectively. However, the FKBP5 rs9470080-T allele was a protective factor against sleep disturbances, with an OR (95% CI) of 0.65 [0.51–0.83]. GMDR analysis indicated that under job stress, individuals with the FKBP5 rs1368780-CT, rs4713916-GG, and rs9470080-CT genotypes and the CRHR1 rs110402-AA genotype had the greatest risk of sleep disturbances. Conclusions Individuals carrying risk alleles who experience job stress may be at increased risk of sleep disturbances. These findings may provide new insights into stress-related sleep disturbances in occupational populations.