The Innovation

City parks and other urban green spaces can bring significant benefits to the physical and mental health of city residents. However, there is limited knowledge about the microbial communities inhabiting these urban soils. Here, we applied long-read metagenomic sequencing to 58 urban soil samples from two major cities in China, enabling genome-resolved reconstruction of microbial diversity at unprecedented contiguity. We recovered 7,949 medium- and high-quality metagenome-assembled genomes, comprising 4,171 species-level genome bins, of which over 97% represent previously undescribed species. Long-read assemblies revealed extensive secondary metabolic capacity, including more than 30,000 biosynthetic gene clusters, which were highly contiguous compared with those from fragmented short-read assemblies. Beyond secondary metabolism, we uncovered over 2 million small protein families, including hundreds that are strongly enriched in the neighbourhood of defense systems and mobile genetic elements, highlighting their overlooked role in urban soils. These findings expand our understanding of the functional diversity of urban soil microbiomes and provide new insights with implications for urban public health.

Stroke-associated pneumonia (SAP) is a common, severe complication in acute ischemic stroke (AIS) patients receiving bridging therapy (intravenous thrombolysis + mechanical thrombectomy), worsening prognosis and increasing mortality. Current SAP prediction models rely heavily on subjective clinical factors, limiting accuracy. This study developed an interpretable machine learning (ML) model combining inflammatory biomarkers to stratify SAP risk in AIS patients undergoing bridging therapy. We retrospectively enrolled AIS patients who received bridging therapy, collected baseline clinical data and inflammatory biomarkers, and constructed ML models (including XGBoost, random forest) with SHAP analysis for interpretability. The model integrating inflammatory biomarkers achieved excellent predictive performance (AUC=0.XX, 95%CI: XX-XX), outperforming traditional clinical models. SHAP analysis identified key biomarkers driving SAP risk, enhancing model transparency. This interpretable ML model provides an objective, accurate tool for SAP risk stratification in AIS patients, helping clinicians identify high-risk individuals early and implement targeted interventions to improve outcomes.

Mainland China experienced multiple waves of COVID19 pandemic during 2020 2022, driven by emerging variants and changes in public health and social measures (PHSMs). We developed a hypergraph-based Susceptible Vaccinated Exposed Infectious Recovered Susceptible (SVEIRS) model to reconstruct epidemic dynamics across 31 provinces, capturing transmission heterogeneity associated with clustered contacts. We assessed key characteristics of transmission at national and provincial levels during four outbreak periods: initial, localized predelta, Delta, and widespread Omicron, which accounted for 96.7% of all infections. We found significant diversity in transmission contributions across cluster sizes, with a small fraction of larger clusters responsible for a disproportionate share of infections. Counterfactual analyses showed that reducing clustersize heterogeneity, while holding overall exposure constant, could have lowered national infections by 11.70 to 30.79%, with the largest effects during Omicron period. Ascertainment rates increased over time but remained spatially heterogeneous with a range: (14.40, 71.93)%. Population susceptibility declined following mass vaccination (to 42.49% in Aug 2021, nationally) and rebounded (to 89.89% in Nov 2022) due to waning immunity with variations across the provinces. Effective reproduction numbers displayed marked temporal and spatial variability, with higher estimates during Omicron. Overall, these results highlight critical role of group contact heterogeneity in shaping epidemic dynamics.

Objective: Neuroendoscopy has emerged as a crucial minimally invasive strategy for the treatment of intracranial hemorrhage (ICH). This bibliometric analysis aims to systematically delineate the global research architecture and evolution of neuroendoscopic ICH research over the past two decades. Methods: Relevant publications were retrieved from the Web of Science Core Collection using a reproducible search strategy. Bibliometric tools were applied to analyze contributions from countries, institutions, authors, publications, keywords and journals, enabling the construction of a comprehensive knowledge map and evolutionary framework of this field. Results: A total of 403 articles were identified, involving 2128 authors from 555 institutions across 43 countries. The publication trajectory exhibited fluctuating growth, reflecting the dynamic interplay between clinical demand and technological maturation. China contributed the highest publications and citation impact, followed by the US, jointly anchoring the global influence of the field. The research keywords have evolved from ?intracerebral hemorrhage? and ?initial conservative treatment? to ?augmented reality.? Thematic evolution analysis revealed a clear progression from early emphasis on operative feasibility, safety, and perioperative outcomes toward more rigorous evidence appraisal and the refinement of context-specific clinical indications, accompanied by continuous technological innovation. Conclusion: These findings collectively position neuroendoscopy as a cornerstone of modern ICH management, reshaping clinical strategies toward precision, minimal invasiveness, and multimodal intervention. Future progress will depend on strengthened international collaboration to generate high-quality evidence that supports patient stratification. The integration of emerging technologies, including advanced endoscopic robotics, is expected to further accelerate the translational and clinical landscape of neuroendoscopic ICH therapy.

Based on the 2023 Global Burden of Disease (GBD) database, this study analyzed the global burden of preterm birth from 1990 to 2023 and predicted its development trend by 2050, while exploring the disparities in disease burden across regions with different Socio-demographic Index (SDI) levels, income groups and countries. A retrospective trend analysis was conducted to collect data on preterm birth incidence, prevalence, death and disability-adjusted life years (DALYs) in 204 countries and regions worldwide from 1990 to 2023 from the GBD 2023 database. ARIMA model (p=2,d=1,q=1) and grey prediction model (GM(1,1)) were combined to predict the preterm birth burden from 2023 to 2050. In 2023, preterm birth was the primary cause of the global neonatal disease burden, with its four core indicators significantly higher than other neonatal diseases. From 1990 to 2023, the global incidence, death and DALYs of preterm birth decreased to 0.91, 0.44 and 0.52 times of the 1990 levels respectively, while the prevalence increased to 1.54 times of the baseline. Projection results showed that by 2050, the incidence, death and DALYs of preterm birth would drop to 0.79, 0.08 and 0.32 times of the 2023 levels, and the prevalence would rise to 1.23 times of 2023. Low SDI regions, lower-middle income countries, as well as India and Nigeria, bore the heaviest disease burden. Over the past three decades, the global acute health burden of preterm birth such as death has decreased notably, but the continuous rise in prevalence and severe regional and age disparities remain prominent public health challenges. The 0-6 days and 6-11 months age groups are the key time windows for preterm birth intervention. It is urgent to implement targeted prevention and control measures for low SDI regions and lower-middle income countries to reduce the global burden of preterm birth.

Comprehensive and responsive interventions are increasingly prioritized to address the diverse and evolving health challenges faced by mothers and children during the first 1,000 days of life. However, evidence remains limited on how such interventions can be operationalized in low-resource settings without overstretching frontline health workers. We developed a comprehensive yet flexible community-based intervention, the Healthy Future program, which integrates a stage-based maternal and child health curriculum with mHealth-enabled infrastructure to deliver targeted, stage-based support through home visits in low-resource settings. We evaluated its impact through a cluster-randomized controlled trial across 119 rural townships in China. The program demonstrated improvements across multiple health, behavioral, and intermediate outcomes, including young child feeding practices, caregiving knowledge, maternal mental health, and perceived social support. Overall, this study illustrates a move beyond stand-alone interventions toward a scalable, multidimensional delivery model capable of providing comprehensive, flexible, and timely support to mothers and children in low-resource communities while remaining feasible for large-scale implementation.

Background Ambient air pollution is a leading global health risk and disproportionately affects populations of Low- and Middle-Income Countries (LMICs). In 2021, WHO revised its Air Quality Guidelines (AQG), lowering recommended annual limits for Particulate Matter 2.5 (PM2.5) and Nitrogen Dioxide (NO2). We estimated the potential health and economic impacts of achieving WHO Interim Target 3 (IT3) and AQG concentrations across LMICs. Methods We conducted a health impact assessment across 136 LMICs to quantify one-year changes in all-cause and cause-specific mortality (chronic obstructive pulmonary disease [COPD], ischaemic heart disease [IHD], and stroke) and disease incidence (COPD, dementia, IHD, and stroke) under WHO IT3 and AQG counterfactual scenarios for PM2.5 and NO2. Concentration-response functions were applied at 1km x 1km resolution. Economic welfare impacts of mortality risk reductions were estimated using country-adjusted values of a statistical life (VSL, Int$ PPP-adjusted 2021). Direct medical and productivity-related costs associated with incident cases were estimated using a cost-of-illness (COI) framework. Uncertainty intervals (UI) reflect uncertainty in concentration-response functions. Results Attainment of WHO IT3 and AQG concentrations for PM2.5 was associated with an estimated 16.04% reduction (6.58million, UI: 6.10-7.07million) and 22.97% reduction (9.43million, UI: 8.75-10.11million) in annual deaths, respectively. Corresponding VSL-based estimates of deaths averted were Int$5.5 trillion (7.0% of aggregate LMIC GDP) and Int$8.4 trillion (10.6% of GDP), respectively. For NO2, IT3 and AQG scenarios were associated with estimated reductions of approximately 1.06% (approximately 435,000 deaths, UI: 388,000-483,000) and 2.79% (435,000 deaths; UI: 388,000-483,000), yielding gains of Int$0.6 trillion (0.7% of GDP) and Int$1.5 trillion (1.9% of GDP). Disease-specific mortality reductions were most prominent for IHD and stroke in Asia and Africa. Under the PM2.5 AQG scenario, an estimated 2.82million (1.67-2.97) COPD, 1.10million (0.83-1.37) dementia, 7.3million (6.41-8.19) IHD, and 2.3million (2.19-2.41) stroke cases could be delayed or averted in one year. Associated reductions in direct medical and productivity-related costs were greatest for IHD, COPD, and stroke. NO2-related morbidity reductions were smaller across all outcomes. All estimates represent one-year changes in risk relative to counterfactual exposure and may reflect delayed rather than permanently avoided events. Discussion Achieving both WHO IT3 and AQG values in LMICs could yield substantial reductions in premature mortality and disease incidence, particularly for cardiovascular and respiratory conditions, alongside large, monetised welfare gains from reduced mortality risk. These findings underscore the considerable societal value of air quality improvements and support accelerated action toward meeting WHO guideline levels in regions bearing the highest pollution burden.

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

African swine fever (ASF) is a highly pathogenic disease caused by the African swine fever virus (ASFV) infection, which can affect pigs of all ages and breeds, posing significant threat to the global pig farming industry. The ASFV p30 protein is an early-expressed viral structural protein; however, its function is not fully understood. In this study, the interaction of viral p30 with host TRIM21 was identified. The ectopic TRIM21 inhibited ASFV replication, while knockdown or knockout of TRIM21 promoted ASFV replication. Further, p30 was found to interact with RIG-I-like receptor (RLR) signaling adaptor MAVS, and during ASFV infection, p30-TRIM21-MAVS interacted with each other. Mechanistically, TRIM21 activated the K27 polyubiquitination of MAVS to induce IRF3 mediated type I interferon (IFN) production, whereas p30 counteracted TRIM21 activated MAVS K27 polyubiquitination to evade RLR signaling mediated antiviral IFN induction. In summary, our study revealed a novel function of ASFV p30, and provided new insights into the immune evasion of ASFV.

BackgroundChronic Kidney Disease of unknown etiology is a growing health concern in low-and middle-income countries. While occupational heat stress is recognized as a potential contributor to kidney dysfunction among agricultural workers, the causal relationship between heat stress, core body temperature (Tc), and kidney function remains unclear. MethodsWe conducted an observational study over two harvest seasons in Guatemala, following 148 male sugarcane workers across six months. Heat stress was measured using heat index (HI) and Tc with ingestible telemetric temperature pills. Particulate matter (PM) exposure was measured using personal breathing zone samplers worn during the work shift. We evaluated changes in kidney function using pre-and post-shift estimated glomerular filtration rate (eGFR). We applied G-computation to estimate causal effects and modeled hypothetical policy interventions reducing HI, Tc, and PM exposure, simulating occupational heat reduction strategies. ResultsThe average daily HI was 37.4 {degrees}C (SD: 2.0) with an average Tc increase of 1.16 {degrees}C (SD: 0.48) per shift. Both HI and Tc were associated with declines in eGFR across the work shift. At an HI of 34 {degrees}C, workers experienced an average eGFR decline of about 5 mL/min/1.73 m{superscript 2}, while at 40 {degrees}C the decline exceeded 16 mL/min/1.73 m{superscript 2}. High HI early in the season and elevated Tc later in the season contributed to kidney decline. A simulated intervention reducing HI exposure by 5% improved eGFR change by 1.46 mL/min/1.73 m{superscript 2}. PM exposure did not have a significant impact on eGFR decline. ConclusionReducing workday heat exposure may mitigate acute kidney function decline. These findings support the development of policy interventions aimed at reducing external heat exposure and internal heat strain to protect kidney health. More research is needed to investigate the potential contribution of other environmental factors, including PM exposure.

Precision neuroimaging aims to deliver individualized assessments of brain health, yet a single structural MRI does not yield a multidimensional, quantitative summary of an individual's current health or future risk. Existing approaches optimize task-specific objectives, yielding representations entangled with cohort- or disease-specific signals rather than capturing biologically grounded patterns of anatomical variation. Here, we introduce NeuroFM, a foundation model trained exclusively on 100,000 healthy synthetic volumes to predict morphometric and demographic targets. Without exposure to diagnostic labels, NeuroFM organizes brain MRIs into population-level patterns that encode meaningful brain health differences. These representations transfer across five neuroscience domains without adaptation and support simple linear readouts for clinical, cognitive, developmental, socio-behavioural, and image quality control. Evaluated on 136,361 real volumes spanning multiple cohorts, NeuroFM generalizes across domains and enables individual-level brain health profiling, estimating future dementia risk years before diagnosis. Together, these findings establish a disease-naive foundation model paradigm for precision neuroimaging.

Sleep disturbances are pervasive, debilitating non-motor symptoms of Parkinson's disease (PD), where sleep spindle deficits directly drive cognitive decline and disease progression. Current adaptive deep brain stimulation (aDBS) for PD is largely limited to motor symptom management, with no established technical foundation for sleep spindle-targeted closed-loop modulation. The functional role of the basal ganglia in human sleep spindle regulation remains incompletely characterized, and no robust cross-subject pipeline exists to decode these transient events from clinically implanted DBS electrodes. Here, we developed a connectomics-guided meta-learning framework for cross-subject sleep spindle decoding and anticipatory prediction, using whole-night synchronized basal ganglia local field potential and polysomnography data from 17 PD patients with bilateral DBS implants. Our framework achieved 92.63% accuracy for concurrent spindle decoding and 83.44% accuracy for 2-second-ahead prediction, with optimal signals localized to the limbic subthalamic nucleus and <50 ms total latency meeting real-time closed-loop requirements. This work defines the neuroanatomical substrate of basal ganglia spindle signaling in PD, establishes the cross-subject spindle decoding pipeline for clinical DBS systems, and provides a critical translational foundation for sleep-targeted closed-loop aDBS to mitigate PD non-motor burden.

Background: Although the relaxation of COVID-19 containment measures in China has altered the transmission dynamics of respiratory pathogens, regional data on post-pandemic epidemiological characteristics remain limited.Objective: This study aimed to investigate the pathogen spectrum and epidemiological characteristics of acute respiratory infections (ARIs) in Quzhou City from 2023 to 2024, providing a scientific basis for local prevention and control strategies.Methods: A total of 2,800 respiratory specimens were collected from November 2023 to July 2024, comprising 1,960 influenza-like illness (ILI) cases from outpatient/emergency departments and 840 severe acute respiratory infection (SARI) cases from inpatient departments. All samples were tested for 13 common respiratory pathogens using multiplex fluorescence quantitative PCR. Etiological and epidemiological analyses were performed based on detection results and case information. Results: The overall ARI positivity rate was 59.28% (1,660/2,800), with a male-to-female ratio of 1.07:1 (1,447/1,353). The three most prevalent pathogens were influenza virus (Flu, 23.21%, 650/2,800), Streptococcus pneumoniae (SP, 13.14%, 368/2,800), and adenovirus (ADV, 8.39%, 235/2,800). Single pathogen infections accounted for 73.55% (1,221/1,660) of positive cases, while co-infections with two or more pathogens accounted for 26.45% (439/1,660), yielding an overall co-infection rate of 15.68% (439/2,800). No significant gender difference was observed in detection rates. However, significant differences were found across case types, temporal periods, age groups, and geographic regions (P < 0.01). Children aged [&le;]5 years exhibited the highest positivity rate (78.00%, 378/525), while adults aged [&ge;]65 years showed the lowest (34.53%, 144/417). Among surveillance regions, Kaihua County had the highest positivity rate (72.47%), and Changshan County the lowest (40.55%). Conclusions: Multiple respiratory pathogens and co-infections are prevalent in Quzhou City, with distinct age-specific and seasonal patterns. These findings underscore the need for continuous multi-pathogen surveillance and integrated prevention strategies for influenza and other respiratory infectious diseases in the post-pandemic era.

Aims Indoor air pollution resulting from combustion of unclean cooking fuels has been linked to adverse health outcomes, but evidence regarding its association with mental health in low- and middle-income countries remains limited. We investigated the association between household use of unclean cooking fuels, as a proxy for indoor air pollution, and depression symptoms among adults aged 45 years and older in India, and assessed effect modification by age, sex, caste, and rural/urban residence. Methods We conducted a cross-sectional analysis of the first wave (2017-2018) of data from the Longitudinal Aging Study in India (LASI), a nationally representative survey of adults aged [&ge;]45 years. Cooking fuel type was classified as clean or unclean, and depression symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression (CES-D-10) scale. We used logistic regression to estimate odds ratios for depression symptoms, and linear regression to compare mean CES-D-10 scores by cooking fuel type, adjusting for sociodemographic and housing characteristics. Results We included 62,650 respondents. Median age was 57 years (IQR: 50-65), 46.7% were women, 47.6% reported using unclean cooking fuels, and 27.6% screened positive on the CES-D-10. After adjusting for sociodemographic and housing characteristics, use of unclean cooking fuels was associated with higher odds of screening positive on the CES-D-10 (aOR: 1.08; 95% CI: 1.02, 1.15), and higher mean CES-D-10 scores (adjusted mean difference: 0.34; 95% CI: 0.24, 0.44). The association was more pronounced among individuals living in urban areas (aOR: 1.36; 95% CI: 1.21, 1.53). Conclusion Use of unclean cooking fuels was associated with depression symptoms among older adults in India, and especially among those living in urban areas.

Gut microbiome studies in Parkinsons disease (PD) are challenged by high dimensionality, sparsity, compositionality, and substantial between-cohort heterogeneity, all of which complicate robust community typing and disease-status classification. Here, we developed a variational autoencoder (VAE)-based methodology for deep enterotyping and PD diagnosis prediction (i.e., predicting diseased vs. control status) using a harmonized multi-cohort gut microbiome compendium comprising 1,957 16S rRNA samples from six PD case-control cohorts and an independent shotgun metagenomic validation cohort of 725 samples. Compared with conventional enterotyping approaches such as partitioning around medoids (PAM) and Dirichlet multinomial mixture (DMM) modelling, the VAE-derived latent space supported a clearer and more reproducible three-cluster solution. These three enterotype-like community states were biologically interpretable and were annotated as Enterococcus-type, Bacteroides-type, and Ruminococcus-type configurations. The same broad three-enterotype structure was independently recapitulated in the metagenomic dataset, supporting cross-platform robustness. Across the three inferred types, the proportion of PD samples was similar, and both the primary generalized linear mixed-effects model and sensitivity model showed that enterotype assignment was not a significant differentiating factor for PD status and that the lack of association was not dependent on a single modelling strategy. In the supervised branch, VAE-derived representations supported PD case-control classification while also providing a shared latent representation for clustering, enterotype transfer, and downstream interpretation. Collectively, these findings show that deep representation learning can improve the resolution, reproducibility, and interpretability of enterotype inference in heterogeneous microbiome datasets, and provide a practical methodology for organizing broad community structure in PD. In this setting, the main advantage of the VAE method lies in its ability to link unsupervised community typing with supervised prediction through a shared latent representation, even when broad community types do not function as stand-alone disease biomarkers.

Military aviation training noise remains understudied despite its widespread impacts across urban, rural, and wilderness areas. The predominance of low-frequency noise and repetitive training can create pervasive noise pollution, yet past research often fails to capture the full range of health and quality-of-life effects. This study analyzed two complaint datasets related to Whidbey Island Naval Air Station noise: U.S. Navy records (2017-2020) and Quiet Skies Over San Juan County data (2021-2023). We analyzed and mapped sentiment intensity from noise complaints relative to modeled annual noise exposure, developed a typology to classify impacts, and modeled the environmental and operational factors influencing complaints. Findings revealed widespread negative sentiment and anger, often beyond the bounds of estimated noise contours, suggesting that annual cumulative noise models inadequately estimate community impacts. Complaints consistently highlighted sleep disturbance, hearing and health concerns, and compromised home environments due to shaking, vibration, and disruption of daily life. Residents also reported significant social, recreational, and work disruptions, along with feelings of fear, helplessness, and concern for children's well-being. The number of complaints were strongly associated with training schedules, with late-night sessions being the strongest predictor. A delayed response pattern suggests residents reach a frustration threshold before filing complaints. Overall, our findings demonstrate persistent negative sentiment and diverse impacts from military aviation noise. Results highlight the need for improved noise metrics, modeling and operational adjustments to mitigate the most disruptive effects.

Effective public health planning and intervention strategies necessitate an understanding of the temporal and geographic distribution of disease incidences. This requires robust frameworks for disease incidence forecasting. However, due to variations in cases and temporal dynamics, grasping the distinct patterns of climate-sensitive diseases poses significant challenges, including identifying hotspots, trends, and seasonal variations in disease incidence. Furthermore, although most studies focus on directly predicting future incidence using historical patterns and covariates, a significant gap remains between methodological proliferation marked by diverse architectures, where models are trained and validated on benchmark datasets that are standardized and statistically stable, and epidemiological reality, which is often characterized by irregular, sparse, and highly skewed data, as well as rare but high-magnitude or bimodally distributed incidences. Hence, traditional end-to-end approaches that directly map climate and disease data often fail in these data-scarce settings due to overfitting and poor generalization. To understand disease epidemiology and mitigate the impact of incidence, we analyzed a decade of retrospective datasets in Ethiopia to examine how climate and weather conditions influence the incidence or spread of climate-sensitive diseases, including malaria and dysentery. In this study, we proposed a two-stage hybrid framework, a climate-informed disease prediction model, to forecast the likelihood of disease incidences using decades of climate and weather data. First, deep learning was applied to capture latent weather dynamics. Then, a hurdle model using Extreme Gradient Boosting (XGB) was designed for zero-inflated incidence data, combining XGBClassifier to predict incidence and XGBRegressor to estimate its size, based on weather dynamics to forecast disease incidence. Our proposed multivariate climate-driven disease incidence model incorporates both spatial (elevation, coordinates) and temporal (year, month) factors, along with key weather parameters (precipitation, sunlight, wind, relative humidity, temperature) to predict the likelihood of multiple diseases occurring in each area, serving as a foundation for future disease incidence predictions in the region. Out of 72 evaluated experiments across four categories and six targets, we found that the Transformer model showed highest number of statistically significant wins (n=18, 25.0%) comparison with Long Short-Term Memory (LSTM) (n=9, 12.5%) and the Temporal Convolutional Neural Network (TCN) (n=5, 6.9%) at climate variable forecasting using Pairwise Model Comparison Diebold-Mariano Test. The hurdle model that combines XGBClassifier and XGBRegressor outperformed the baseline in both Malaria and Dysentery forecasting. Error stratification revealed that the hurdle model provided the greatest benefit during incidence periods, as indicated by a substantially lower Mean Average Error (MAE) in both incidence and non-incidence periods than the baseline. Our proposed modular pipeline first forecasts climate variables, then predicts disease incidence, thereby enhancing interpretability and generalization in data-sparse settings. Overall, this approach provides a scalable, climate-aware forecasting tool for public health planning, particularly in regions where these diseases are endemic or where climate change may affect their prevalence, as well as in data-scarce settings.

Benzalkonium chloride (BAC) is widely used as a disinfectant in cleaning products and is frequently detected in indoor dust. In this study, we assessed dust samples, along with information on cleaning product use, from 24 pregnant participants. Dust samples were analyzed for BAC concentration and microbial tolerance. Different chain lengths of BAC (C12, C14, and C16) were quantified using LC-MS/MS, and bacterial isolates were tested for BAC tolerance using minimum inhibitory concentration (MIC) assays. BAC was ubiquitously detected, with C12 and C14 being dominant. Higher BAC concentrations were associated with reported disinfectant use and increased microbial tolerance. These findings suggest that indoor antimicrobial use may promote microbial resistance, highlighting potential exposure risks in indoor environments and the need for further investigation into health and ecological impacts.

ABSTRACPorcine respiratory diseases caused by extraintestinal pathogenic Escherichia coli (ExPEC) pose a severe threat to swine production and public health; however, research on respiratory tract-isolated ExPEC remains limited. This study comprehensively analyzed the genomic characteristics and antibiotic resistance gene (ARG) transfer potential of 441 ExPEC strains isolated from porcine lungs across 21 Chinese provinces (including 53 newly isolated strains from 2022-2024 and 388 NCBI-deposited strains). Phylogenetic analysis revealed that 84% of the isolates belonged to phylogroups A, B1, and C, with ST410, ST101, and ST88 as the predominant STs. The strains exhibited extensive ARG diversity, harboring 111 distinct ARG subtypes, with sul2 (81.4%), floR (73.5%), and tet (A) (68.0%) being the most prevalent. Importantly, critical "last-resort" antibiotic resistance genes (e.g., blaNDM-1/5, the mcr family, and tet (X4)) were also detected. Notably, 77.2% of the ARGs presented horizontal transfer potential, with plasmids (especially IncF family replicons) serving as core vectors, followed by integrons and transposons. Cooccurrence network analysis identified aph (3)-Ib, aph (6)-Id, sul2, and floR as core subnetworks driving multidrug resistance dissemination. Pangenomic analysis confirmed an open genome architecture, with core genes accounting for only 6%, reflecting the strains capacity to acquire exogenous genetic material via horizontal transfer. From the One Health perspective, these transferable ARGs can spread to the environment and humans through fecal discharge and the food chain. These findings underscore the importance of monitoring and controlling ExPEC infections in swine, as such strains can as reservoirs of ARGs, pose potential risks to human health, and may even act as sources of pathogenic agents responsible for human infections. IMPORTANCEPorcine respiratory ExPEC-induced diseases threaten swine production and public health, yet respiratory tract-isolated ExPEC research remains scarce. This study comprehensively analyzed 441 porcine lung ExPEC strains across 21 Chinese provinces, uncovering their dominant phylogroups, high ARG diversity (111 subtypes) and the presence of "last-resort" antibiotic resistance genes. We identified 77.2% of ARGs with horizontal transfer potential, plasmids (especially IncF family) as core vectors, and a core ARG subnetwork driving multidrug resistance. The open pangenome (6% core genes) highlights ExPECs strong capacity to acquire exogenous genes. These findings fill the research gap of respiratory ExPEC, clarify ARG transmission mechanisms in swine ExPEC, and provide critical genomic data for One Health-based AMR surveillance and control, guiding targeted strategies to mitigate ARG spread from swine to humans and the environment.

ObjectiveTo investigate associations between long-term environmental exposures, both external (ambient air pollution and built environment) and internal (circulating anthropogenic chemicals), and the human plasma metabolome, with the aim of generating biologically plausible hypotheses about affected metabolic pathways. MethodsWe analyzed plasma from 989 Estonian Biobank participants using untargeted LC-HRMS (Metabolon HD4). External exposures (PM2.5, PM10, NO2, ozone and built-environment metrics) were assigned using spatiotemporally resolved models developed in the EXPANSE project. Internal exposures were defined as ubiquitous anthropogenic compounds detected in the same metabolomics dataset. Associations between exposures and individual metabolites were quantified using left-censored regression models and then mapped to metabolite classes (Metabolon) and KEGG pathways. For enrichment analyses, one-sided Kolmogorov-Smirnov tests were applied to external exposures and Fishers exact tests to internal exposures. False discovery rate was controlled at 1% per exposure and database. ResultsExternal air pollutants exhibited distinct metabolic patterns: Higher NO2 exposure was associated with enrichment of metabolites involved in tyrosine metabolism; higher ozone with monohydroxy and dicarboxylate fatty acids (consistent with lipid peroxidation); and higher PM2.5 with acyl-carnitine subclasses and carbohydrate metabolism (glycolysis / gluconeogenesis / pyruvate). Built-environment associations were heterogeneous across metabolites and pathways. Internal anthropogenic chemicals showed broader metabolic associations than external exposures, involving a larger number of metabolites and metabolic classes. PFAS (PFOA, PFOS) were associated with long-chain polyunsaturated fatty acids (n3/n6) and lysophospho-lipids. Associations with 4-hydroxychlorothalonil, a fungicide, pointed to androgenic steroid metabolites and alpha-linolenic acid metabolism. The phenolic 2,4-di-tert-butylphenol, a plastic associated chemical, showed widespread associations with lipid classes, suggesting disruption of membrane remodeling and fatty acid handling. ConclusionLong-term environmental exposures, both external and internal, are measurably reflected in the human plasma metabolome. Across exposure domains, recurrent signals involved lipid metabolism, membrane composition, and oxidative stress-related pathways, highlighting these as common biological targets of environmental exposures. The findings generate testable hypotheses, including nitrosative stress-related alterations for NO2, lipid peroxidation for ozone, energy-metabolism perturbations for PM2.5, potential endocrine activity for chlorothalonil metabolites, and possible obesogenic effects of 2,4-di-tert-butylphenol.