The Innovation

ObjectiveTo evaluate long-term antemortem exposure to four pollutants in relation to Alzheimers disease (AD), cerebrovascular, and other dementia-related neuropathologies, measured at autopsy. DesignRetrospective cohort study. SettingIndividual participant data from four Rush Alzheimers Disease Center (RADC) longitudinal cohort studies: Memory and Aging Project, Minority Aging Research Study, Rush Clinical Core, and Latino Core. The cohorts enrolled participants residing in Chicago, Illinois (USA), including its metropolitan area and suburbs, and in outlying areas of Illinois. Participants909 decedents from the four RADC cohorts who underwent brain autopsy. SettingRADC cohorts are drawn from northeastern Illinois (IL) and Chicago metropolitan, suburban, and outlying areas of IL. ParticipantsAll participants were aged >60 years at enrollment. Analyses included 909 decedents with air pollution exposure measures who underwent autopsies prior to 2020 (of 3,579 who enrolled by the end of 2019); all autopsies were from community-based cohorts. ExposuresExposure to fine particulate matter (PM2.5; particles <2.5 m in aerodynamic diameter), nitrogen dioxide (NO2), oxides of nitrogen (NOx; nitrous oxide and NO2 combined), and ground-level ozone (O3) during the five years preceding death. Exposures were estimated with validated models developed for both the conterminous USA and the Chicago metropolitan area. Main Outcomes and MeasuresTwelve dementia-related neuropathologies measured by a neuropathologist at autopsy: Alzheimers disease neuropathology (ADNC), {beta}-amyloid density, tau tangle density, cerebral arteriolosclerosis, cerebral atherosclerosis, cerebral amyloid angiopathy, chronic cerebral infarctions (microscopic and gross), hippocampal sclerosis, Lewy bodies and limbic predominant age-related TDP-43 encephalopathy (LATE-NC). ResultsExposure to PM2.5 and NO2, as measured using Chicago-specific models, were both associated with higher tau tangle density [mean difference per 2.5 {micro}g/m3 PM2.5 = 0.25 tangles/mm2, (95% confidence interval [CI], -0.05 to 0.56); mean difference per 5 ppb NO2 = 0.10 tangles/mm2, (95% CI -0.07 to 0.28)]. PM2.5 exposure was associated with higher prevalence of arteriolosclerosis [prevalence ratio (PR) per 2.5 {micro}g/m3 = 1.51 (95% CI, 1.02 to 2.24)]. Both PM2.5 and NOx exposure were associated with higher prevalence of cerebral atherosclerosis [PR per 2.5 {micro}g/m3 PM2.5 = 1.41 (95% CI, 0.93 to 2.13); PR per 5 ppb NOx = 1.10 (95% CI, 0.98 to 1.23)]. None of the exposures was clearly adversely associated with the other neuropathologic outcomes, including {beta}-amyloid density and ADNC. Conclusion and RelevanceHigher exposure to PM2.5 was associated with cerebral arteriolosclerosis and atherosclerosis at death, consistent with the known vascular toxicity of this pollutant.

Millions of outdoor workers cannot avoid wildfire smoke, likely leading to inequalities in exposure and health risk. We characterized work-related exposure to wildfire PM2.5 for 3,108 contiguous US counties during 2006-2019. Despite experiencing less ambient exposure to wildfire PM2.5, counties with higher portions of non-Hispanic Black and Hispanic Americans experienced higher work-related exposure. We also find suggestive evidence that the effect of ambient smoke fine particulate matter (PM2.5) concentrations on all-cause mortality may differ by workplace exposure. These findings suggest that workplace exposures should be considered in wildfire smoke adaptation measures.

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.

Accurate quantification of individual exposure to air pollutants remains a major challenge in environmental health, as fixed-site monitoring fails to account for mobility, indoor environments, and physiological variability. We deployed TracMyAir, a smartphone-based digital health platform designed to generate time-resolved, personalized exposure and inhaled dose estimates for PM2.5 and ozone under real-world conditions. In an exploratory study of 18 adults contributing more than 1,500 participant-hours, the platform integrated smartphone geolocation, regulatory (AirNow) and community-based (PurpleAir) air quality data, building infiltration modeling, microenvironment classification, and wearable-derived physical activity metrics to compute eight tiers of hourly exposure estimates, culminating in individualized inhaled dose. Hourly dose estimates derived from smartphone-and smartwatch-based step counts were concordant (Spearman correlation p=0.97-0.98), while heart rate-based estimates yielded greater variability and higher mean values (p=0.82-0.92). Exposure explained 51-73% of variance in inhaled dose of PM2.5 and 68-84% of ozone, suggesting that physiological-based modeling approaches improve hyperlocal estimates of personal pollutant burden. Substantial inter-and intra-individual variability reflect dynamic microenvironmental transitions and activity patterns. Modeled doses based on regulatory and community sensor networks were strongly correlated (R=0.84), with community sensors located closer to participants on average, supporting the feasibility of integrating dense, low-cost monitoring networks. No consistent association was observed between outdoor pollutant levels and neighborhood socioeconomic status in this cohort. These findings demonstrate the feasibility of a scalable, smartphone-centered digital health approach for hyperlocal exposure and inhaled dose modeling. By leveraging ubiquitous consumer devices and existing air quality networks, TracMyAir enables personalized environmental exposure assessment with potential applications in epidemiology, population health, and precision environmental medicine.

We utilized traditional aerosol sampling to collect PM1 samples, and further apply redundancy analysis (RDA) to investigate the association of environmental factors (including PM1 chemical composition, oxidative potential, meteorological factors and gaseous pollutants) and airborne bacterial community. Our findings revealed that Bacteroidota positively correlated with Sn and Mn, Firmicutes with local primary pollutants, and Proteobacteria with transportation-related pollutants. Variance partitioning analysis (VPA) showed that PM1 chemical composition, meteorological factors and gaseous pollutants collectively explained up to 43.7% of community variance, and synergistic effects may exist among the three factors. In contrast, oxidative potential had minimal influence. Additionally, to investigate airborne viral presence, we employed a novel bioaerosol sampler targeting SARS-CoV-2 in hospital and campus settings. Viral loads were highest in negative pressure isolation rooms, followed by general hospital and campus areas. Also, the detection rates follow the same pattern, which is 87.5%, 58.3%, and 25.0%, respectively. Notably, detection rates near isolation wards increased during patient admissions, implying possible biocontamination despite containment measures. Peak human traffic flow emerged as a significant factor influencing viral detection. These results highlight how environmental factors shaping airborne microbial communities.

Wildfire smoke (WFS) events are an important public health concern for communities in the Pacific Northwest of the United States. Previous studies of portable air cleaners, including high efficiency particulate air (HEPA) filtration and do-it-yourself (DIY) box fan filters built with MERV 13-rated filters, have indicated that their use in residential settings may be an effective way to reduce indoor exposures to fine particulate matter during WFS episodes. The lower-cost, easy to build instructions and availability of materials of DIY box fan filters have made their distribution by both public health agencies and community groups an attractive approach to improve community preparedness. Here, we describe a low-cost, easy-to-assemble, portable exposure chamber system that can be used to support a variety of community-engaged demonstrations of WFS removal efficiency as well as provide a mechanism to estimate the efficiency of filtration systems in a controlled environment. We conducted experiments using the portable chamber to assess the clean air delivery rate (CADR) of a MERV 13-rated DIY box fan filter, which was found to be 92.2 and 145.2 cfm at low and high fan speeds, respectively. In addition to using the chamber system to evaluate the CADR of DIY box fan filters, we also provide a case-study example, working with a tribal community in Central Washington, who used the tent system for a live demonstration of a DIY box fan filter experiment during their community gathering to promote WFS and air quality intervention knowledge and distribution of box fan filters.

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.

BackgroundSuboptimal adherence to preventive medications and inadequate management of risk factors are major barriers to effective post-discharge care for individuals at high risk of stroke, particularly in resource-constrained settings. Digital health interventions delivered via widely accessible platforms offer a promising scalable approach. We conducted this trial to evaluate the efficacy of Social Network based patient care in improving medication adherence in high-risk populations for stroke. MethodCOMPLIANCE-MT is a multicenter, prospective, randomized, open-label, parallel-group trial with blinded outcome assessment (PROBE design) assessing the superiority of Social Network-based care versus conventional care in medication adherence for high-risk populations for stroke. A total of 720 participants will be recruited across 33 hospitals in China and randomized in a 1:1 ratio to either a 12-month Social Network (WeChat) based coordinated care program or routine follow-up. The primary outcome is the proportion of patients achieving more than 80% adherence to all indicated vascular prevention medications (antihypertensives, hypoglycemics, lipid-lowering agents, anticoagulants, and antiplatelets) at 12 months. Analysis will be performed on an intention-to-treat approach. DiscussionThe COMPLIANCE trial evaluates a social network-based intervention in improving adherence to five evidence-based vascular prevention medications in both primary prevention and secondary prevention. If proven effective, this model could inform national strategies for preventing strokes in resource-limited settings. Trial registration: ClinicalTrials.gov NCT05963828.

The co-occurrence of per- and polyfluoroalkyl substances (PFAS) and volatile organic compounds (VOCs) in industrial environments poses complex toxicological risks that standard additive models fail to capture. This study elucidates a novel "metabolic blockade" mechanism wherein PFAS competitively inhibits the renal excretion of VOC metabolites, thereby amplifying neurotoxic burdens. Utilizing a Double Machine Learning (DML) framework on data from National Health and Nutrition Examination Survey (2005-2020), we analyzed a final intersectional cohort of 1,975 participants. We identified a robust inhibition of VOC metabolite clearance by serum PFAS. Specifically, PFNA significantly suppressed the excretion of the benzene metabolite URXPMA (Causal {beta}TMLE = -0.219, p < 0.001), with efficacy dependent on perfluorinated chain length. Molecular docking simulations revealed the biophysical basis of this antagonism: long-chain PFNA exhibited superior binding affinity to the Organic Anion Transporter 1 (OAT1) ({Delta}G = -6.333 kcal/mol) compared to native VOC metabolites ({Delta}G = -4.957 kcal/mol), confirming high-affinity competitive inhibition at the renal interface. In a neurocognitive sub-cohort (N = 1,200), this interference translated into functional synergism; high-PFNA exposure magnified VOC-associated cognitive impairment by 1.5-fold and significantly exacerbated the negative association between VOC burden and processing speed ({beta}int = -0.263, p = 0.004). These findings define PFAS as a "metabolic amplifier" of co-contaminant toxicity, necessitating a paradigm shift toward mixture-based hazardous material regulations that account for transporter-level interactions.

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.

BackgroundOutdoor workers are particularly vulnerable to the adverse impacts of heat, but many studies focus on heat exposure in residential settings only. This leads to a limited understanding of the full mortality burden due to occupational heat exposures. Here, we aimed to improve estimates of the total, short-term mortality burden attributable to outdoor occupational heat exposure in the United States (US). MethodsWe developed a panel data set for 3,108 US counties during 2010-2019 by linking all-cause mortality among the working age population, derived from CDC WONDER, with the prevalence of workers exposed to outdoor occupational heat, which integrates data on wet bulb globe temperature, workplace activities, and employment counts. We developed a quasi-Poisson regression model adjusted for ambient temperature, total precipitation, and county and state-year fixed effects to estimate short-term excess deaths attributable to outdoor occupational heat exposure. FindingsNationwide, approximately 3.8% (95% CI: 2.5-5.8%) of all workers were annually exposed to dangerous wet-bulb globe temperatures. This outdoor occupational heat exposure resulted in approximately 9,800 (3,100-17,000) annual excess deaths in the working age population. An estimated 62% of excess deaths occurred in the most socially vulnerable counties despite accounting for 25% of workers. InterpretationThe mortality burden of occupational heat exposure is likely far larger than 39 officially reported annual deaths that the Bureau of Labor Statistics reports for this time period. The workplace should be an explicit focus of heat policies, advocacy, and adaptation measures. FundingUS Centers for Disease Control and Prevention/National Institute for Occupational Safety and Health.

Geothermal springs, characterized by extreme physicochemical conditions, represent ecologically and evolutionarily significant habitats that foster unique microbial communities and drive adaptive evolutionary processes. Despite their importance, the complex microbial interactions and underlying mechanisms governing community assembly in these environments remain poorly understood. In this study, we conducted systematic sampling across 49 geothermal springs in Tengchong, Yunnan, over a six-year period (2016-2021), and performed metagenomic sequencing on 152 samples. We successfully reconstructed 12,789 non-redundant microbial genomes, revealing an exceptionally high level of phylogenetic and functional diversity within the spring microbiomes. Our analyses demonstrate that pH and temperature are the primary deterministic drivers shaping both microbial species composition and functional potential, thereby segregating the communities into three distinct groups: acidic, hyperthermal, and thermal. Furthermore, ecological network analysis revealed that extreme environmental conditions significantly alter network topology, resulting in less complex but more efficient microbial interaction networks. Collectively, this study provides a comprehensive resource and mechanistic insights into the microbial diversity, community structure, and species interactions in geothermal spring ecosystems.

Evidence has shown that tumor progression is associated with the acquisition of growing autonomy and the creation of a complex signaling network through various signal pathways. Which particular signaling pathway is involved in the metastasis of a specific cancer is unclear. Here, we applied metastatic functional screening and identified that one-carbon and SSP metabolism pathways, as well as related genes, are associated with tumor metastasis inhibition. We engineered the cancer cells with poorly or highly metastatic potential to confirm the metabolism pathways regulating the ability to colonize different tissue sites. We also asked whether the restriction of the metabolism pathways by known inhibitors. We then identified three new compounds that can inhibit the expression of these genes and block tumor metastasis. Our findings uncovered a mechanism by which tumor cells reprogram their metabolism to promote migration, invasion, and survival at distant sites in tumor metastasis, offering a rational strategy to guide clinical treatment. The identified novel molecular proteins and pathways represent a promising therapeutic target for metastatic disease.

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.

BackgroundEvidence suggests maternal exposure to ambient air pollution increases the risk of stillbirth, but few studies conducted in the United States have evaluated temporally varying exposures or susceptibility across gestational windows. Moreover, the generalizability of existing findings is often limited by restricted geographic coverage or reliance on selected study populations. MethodsUsing Georgia vital records from 2005 to 2014, we conducted a matched case-control study including 8,384 stillbirths and 33,459 live birth controls matched on maternal county of residence and conception month. We used stratified Cox proportional hazards models with time-varying covariates to estimate hazard ratios (HRs) for ten air pollutants across five exposure windows (first month, weekly, and first, second, and third trimester). Our primary analysis included all stillbirths combined, with subgroup analyses separating second and third trimester losses. ResultsStillbirths had a median gestational age of 27 weeks (IQR: 6.67) compared with 38 weeks for live births (IQR: 2.13). Particulate matter showed strong associations in the second trimester exposure window for all stillbirths (PM10: HR = 1.07; 95% CI: 1.04, 1.11; PM2.5: HR = 1.05; 95% CI: 1.01, 1.09). This pattern was consistent for NO2 and NH4, which also exhibited positive associations across early and entire pregnancy exposure windows (first month, first trimester, weekly), with the strongest associations for the second trimester exposures. Associations were larger for second trimester stillbirths, whereas estimates for third trimester stillbirths were largely null or negative. ConclusionsIn this population-based study in Georgia, time-varying ambient air pollution exposures during pregnancy were associated with increased risk of stillbirth, particularly for second trimester exposures and for stillbirths occurring earlier in pregnancy. These findings highlight the importance of considering gestational timing when evaluating environmental risk factors for stillbirth. What this study addsThis study is the first to evaluate maternal ambient air pollution exposure and stillbirth using time-varying exposures on vital records in the state of Georgia. By examining ten air pollutants across multiple gestational windows and subset analyses by timing of stillbirth, we identified second trimester susceptibility to NO2, PM10, PM2.5, and NH4. These findings highlight periods of vulnerability to ambient air pollution during pregnancy.

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.

Hospital-acquired infections are a known and growing problem worldwide. Far-UVC is a novel disinfection method that inactivates bacteria with limited penetration into human skin or eyes. A clustered, unmatched, randomized control trial (RCT) will be implemented in two Bolivian hospitals. The intervention arm will receive functioning Far-UVC lamps, whereas the control arm will receive identical lamps that do not emit UV light (shams). Based on baseline data, 40 lamp fixtures will be installed above hospital sinks, 10 per arm per hospital. Environmental samples (air and surface swabs) will be collected and analyzed via culture and sequencing. Simultaneously, air chemical monitoring data will be collected.