The Innovation

Background and Purpose: Ambient air pollution is an established risk factor for incident stroke, but whether post-discharge pollutant exposure influences stroke recurrence remains unknown. We investigated the association between post-discharge exposure to six ambient air pollutants and stroke recurrence in patients with acute ischemic stroke. Methods: We analyzed data from 27,346 patients in the CRCS-K-NIH nationwide multicenter registry of acute ischemic stroke patients (2014-2021) with confirmed ischemic stroke, residential address data, and matched air quality records. The primary exposure was the 3-month post-discharge average concentration of PM10, PM2.5, NO2, SO2, CO, and O2, assessed at the district level using inverse-distance weighted interpolation. The primary outcome was stroke recurrence from 3 to 15 months post-discharge. Cause-specific Cox proportional hazards models accounting for the multilevel data structure were used, with all-cause mortality as a competing risk. Restricted cubic splines assessed nonlinear dose-response relationships. Results: During follow-up (median 364.8 days), 765 patients experienced stroke recurrence and 471 died. Among the six pollutants, only SO2 showed a statistically significant association with recurrence (P for overall association in the restricted cubic spline analysis = 0.024). A potential threshold was identified at approximately 8.2 ppb, above which recurrence risk increased progressively (P for non-linearity = 0.095). The association was numerically stronger among older adults ([≥]75 years; P for interaction = 0.051) and women (P for interaction = 0.062). The highest SO2 concentrations were observed in harbor cities (Incheon, Ulsan, Busan), consistent with maritime shipping emissions. No significant associations were observed for the other five pollutants. Conclusions: Elevated post-discharge SO? exposure is associated with increased stroke recurrence risk, particularly in harbor regions and among older adults and women. These findings support incorporating ambient air quality monitoring into secondary stroke prevention strategies.

Open air burn pits were used extensively during military operations in Iraq and Afghanistan, potentially exposing millions of US Veterans to toxic airborne hazards. Many of the airborne toxins released have been shown to induce lung inflammation and lung injury and are mutagenic. This is the first large-scale study of associations between self-reported burn pit exposures and the development of cancer. Using data from the Airborne Hazards and Open Burn Pit Registry, we found that Veterans reporting burn pit exposures are associated with a higher odds of developing cancer. However, investigations into the development of specific type of cancer and into a burn pit exposure dose-response effect were inconclusive.

Human papillomavirus 18 (HPV18) preferentially infects cervical stem cell-like cells and is strongly associated with adenocarcinoma. However, the mechanisms underlying differentiation into cervical adenocarcinoma remain unclear due to the lack of appropriate experimental models. We aimed to establish a model of HPV18-associated cervical adenocarcinoma and elucidate its molecular and cellular differentiation mechanisms. HPV18 E6/E7 were introduced into induced pluripotent stem cell-derived reserve cell-like cells (iRCs) to generate tumor models. Spatial transcriptomics and single-cell multi-omics analyses were performed to integrate histological and molecular data. A distinct component (Gland_A) exhibited morphological and immunohistochemical features of cervical adenocarcinoma and was efficiently induced in iRC-18 tumors. Gland_A showed increased chromatin accessibility and elevated expression of FOXA1, FOXA2, and ALDH1A1. Analysis of clinical samples confirmed enrichment of ALDH1A1 in HPV-associated adenocarcinomas. This model recapitulates key features of HPV18-associated cervical adenocarcinoma and provides insights into its differentiation mechanisms.

Oncolytic virotherapy is an innovative approach to cancer treatment that uses replication-competent viruses to selectively target and destroy cancer cells while leaving healthy tissues largely unaffected. Zika virus (ZIKV), a neurotropic orthoflavivirus, has recently gained attention as a potential oncolytic agent due to its ability to infect neural-derived cells and suppress tumor growth in preclinical models. Although existing studies have examined ZIKVs oncolytic effects, the mechanisms underlying these effects remain largely unexplored. Additionally, the roles of individual ZIKV proteins and their interactions with host factors remain incompletely understood. Here, we used RNA sequencing, affinity purification-mass spectrometry, and functional assays to uncover previously unidentified mechanisms underlying ZIKVs oncolytic activity in pediatric neural tumors. We found that the ZIKV non-structural proteins NS4A and NS5 exert oncolytic effects, reducing tumorsphere size. ZIKV-host protein-protein interaction networks were characterized and showed that integrin 3 (gene: ITGA3), a mediator of cell-matrix adhesion, interacts with ZIKV NS2B and NS4A. Integrin 3 was further shown to be involved in ZIKV- and NS4A-induced tumorsphere size reduction, while ITGA3 knockdown and ZIKV infection additively inhibited 3D invasion. These findings provide critical mechanistic insights that could inform the rational design of ZIKV-based virotherapies and highlight opportunities for combination treatment strategies.

IntroductionMagnetic resonance imaging (MRI) is central to neurological care, yet access remains profoundly inequitable in low- and middle-income countries, especially in rural health facilities where high costs and fragile electricity supply limit services. Ultra-low-field (ULF) portable MRI offers a way to expand access, but deployment in weak-grid settings requires robust affordable power. We characterized the power needs of a 0.064T portable ULF MRI system and assessed the feasibility of a solar-powered MRI-capable facility in a rural Zimbabwean clinic, which we believe to be the first of its kind in the world. MethodsWe measured the power draw of an ultra-low-field MRI session from a portable photovoltaic (PV) battery kit in the UK, quantifying scan, standby and energy use. We then monitored a PV-battery micro-grid supplying a protected circuit at an MRI-capable clinic in Shurugwi, Zimbabwe. Inverter telemetry was used to derive PV generation, load, battery state of charge (SoC) and grid import for working days in October-November 2025, spanning the end of the dry season and onset of the rainy season. ResultsIn the portable configuration, a 64-minute MRI session consumed [~]0.21 kWh, with standby demand of [~]1.44 kWh per 24 hours. In clinic, mean PV generation was 9.10 kWh (SD=1.34) and load 9.91 kWh, with zero recorded grid import and minimum daily SoC typically [≥]60%, including during the early rainy season. ConclusionAn affordable PV-battery micro-grid can reliably support ULF MRI and associated research power loads in a rural, weak-grid clinic, offering a reproducible blueprint to narrow diagnostic equity gaps in resource-limited settings.

The Pamir Plateau is a transboundary water tower whose source lakes serve as critical biogeochemical hubs with implications for downstream freshwater security. However, it remains unclear how environmental shifts in these high-altitude lakes reshape the microbial communities that drive ecosystem functioning and water safety. Here, we conducted a multi-omics survey across 20 lakes spanning Chinese and Tajikistani Pamir. Our results revealed that prokaryotes exhibited lower diversity but higher among-lake connectivity in China, while eukaryotes showed higher diversity but stronger dispersal limitation. These contrasting biogeographic responses triggered profound rewiring of microbial associations. Under intensified anthropogenic pressures, Chinese cross-kingdom networks decoupled from environmental constraints and became more centralized and complex. Conversely, Tajikistani lakes maintained more modular networks governed by hydrochemical filtering. Critically, this rewiring mediated a trade-off between multifunctionality and potential biosafety risk, with higher element cycling abundances in Chinese lakes, whereas Tajikistani lakes harbored larger biosafety burden dominated by virulence, pathogen, and toxic-algae potential. Incorporating network topology also substantially improved the prediction of these ecological consequences. These findings highlight the importance of network-informed monitoring and management strategies to safeguard ecosystem sustainability in transboundary Pamir lakes under global change.

Background: Climate mitigation policies can lower air pollutant concentrations and deliver substantial health co-benefits. The French Ecological Transition Agency (ADEME) proposed four contrasting Transitions 2050 net-zero scenarios. We quantified mortality, morbidity, and health-economic co-benefits from projected PM2.5 and NO2 reductions across all four scenarios in continental France. Methods: Emission projections were input to the CHIMERE chemistry-transport model to estimate PM2.5 and NO2 concentrations for 2030 and 2050. Health impacts were assessed using disease-specific cessation-lag assumptions relative to 2019, covering premature mortality, morbidity, DALYs, and economic benefits across nine outcomes (hypertension, lung cancer, ischaemic heart disease, stroke, COPD, type-2 diabetes, acute lower respiratory infections, and asthma in children and adults). Findings: Population exposure is projected to decline by about 40% for PM2.5 and 70% for NO2 by 2050, with health gains remaining substantial and broadly equivalent across all four scenarios and modest differences between sufficiency-oriented and technology-driven pathways. Under delayed-impact assumptions, avoided premature deaths ranged from 21,300 to 22,100 for PM2.5 and 24,500 to 26,200 for NO2. Morbidity and disability-adjusted life year (DALY) reductions, as well as economic savings, spanned similarly; total avoided morbidity cases were 84,000-88,000, direct medical cost reductions were e1.0-1.1 billion/year, and intangible cost savings of e41-43 billion and e36-39 billion, respectively. Interpretation: Health co-benefits are substantial, consistent across contrasting scenarios, and increase markedly from 2030 to 2050. Explicitly incorporating these co-benefits into climate policy appraisals may strengthen the case for ambitious mitigation and improve decision-maker acceptability.

Background: Lung cancer in never-smokers is rising, with a substantial proportion harboring the EGFR mutation. While fine particulate matter (PM2.5) is a recognized risk factor, other intervenable pollutants and built environmental factors remain unknown. Objectives: To identify urban characteristics associated with EGFR-mutant (vs. wild-type) lung cancer using high-resolution spatiotemporal data. Methods: We analyzed 2,699 lung cancer patients with documented EGFR status treated at a high-volume academic medical center in New York City. Patient residential addresses were linked to high-resolution (300m x 300m) 5-year cumulative exposures to 3 air pollutants and 26 urban features. We developed Light Gradient Boosting Machine (LightGBM) models to classify EGFR status, comparing a basic clinical model with established predictors (Asian, female, never-smoking status, and adenocarcinoma histology) to an extended model with additional urban factors. Predictive performance was assessed based on discrimination (AUC). Results: We included 2,699 patients, of whom 54.1% were female and 25.8% self-identified as Asian, 11.2% as Black, and 7.4% as Hispanic; and 29% had EGFR-mutated cancer. The extended model showed modest improvements in discrimination (AUC: 0.775 [95% CI, 0.739-0.809] vs. 0.768 [0.723-0.811]), compared to the clinical model. Newly identified factors for EGFR-mutant status included black carbon (BC), nitrogen dioxide (NO2), proximity to airports, reduced access to public transportation, elevated noise levels, and lead exposure. Conclusions: Traffic-related pollutants (BC, NO2) from diesel engines and motor vehicles, and proximity to airports, were among the novel spatiotemporal features associated with EGFR-mutant lung cancer. These results may inform policy interventions.

Tau protein aggregation in the brain is a hallmark of Alzheimers disease (AD). Positron emission tomography (PET) is the only in vivo method to visualize tau pathology and estimate both its burden and regional distribution, but the use of tau-PET is constrained by high cost and limited accessibility. Here, we develop a deep learning model to synthesize tau-PET scans from more accessible data: structural magnetic resonance imaging (MRI), demographics, and when available, blood biomarkers. We included 5,191 participants across the AD continuum or with another neurological disorder from 13 cohorts (mean age 70 years, 51% female) and optimized a 3D U-Net neural network with residual and attention units for this task. In held-out test data, synthetic tau-PET reliably modeled tau burden, with correlations of R=0.77-0.86 with true tau-PET across individuals in common AD regions of interest. Spatial similarity between synthetic and true tau-PET was likewise high, with mean regional correlation of R=0.75. Synthetic scans also captured clinically meaningful prognostic information comparable to true tau-PET, including distinction between early (HR=12, p<0.001) and late (HR=45, p<0.001) stages of tau accumulation. These findings demonstrate that clinically informative synthetic tau-PET scans can be generated from widely available modalities using deep learning, potentially offering a scalable and cost-effective approach for estimating tau AD pathology in the brain.

Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is the most common primary lung cancer worldwide. Despite advancements in early detection and treatment, up to 39% of patients develop recurrent tumors following complete resection. Currently, no widely available models exist for reliably predicting early recurrence of LUAD, which is a significant prognostic factor of post-recurrence survival. Models leveraging deep learning (DL) techniques have demonstrated notable utility in cancer recurrence prediction, particularly when used in combination with both clinical and genomic data. We developed a DL-based model, Predicting Lung Adenocarcinoma recurrence via Selective Multimodal Attention (PLASMA), to predict early recurrence using clinical, mRNA expression, and mutation data from patients with primary stage I-III LUAD. Trained on The Cancer Genome Atlas (TCGA) dataset, PLASMA outperformed traditional machine learning models in predicting early recurrence in both the TCGA test set and an external validation set (TRACERx Lung), achieving area under the receiver operating characteristic curve (AUROC) scores of 85.0% and 76.5%, respectively. Our results support the potential of multimodal DL for early LUAD recurrence prediction and risk stratification.

INTRODUCTION: Alzheimers disease (AD) is a multifactorial disorder, yet current research largely focuses on downstream biomarkers with limited attention to environmental contributors. Experimental studies suggest that per and polyfluoroalkyl substances (PFAS) may contribute to neuroimmune and neurodegenerative pathways relevant to AD. OBJECTIVE: To examine associations between PFAS exposure and neuroimmune and AD related plasma biomarkers in cognitively unimpaired rural adults. METHODS: In a cross sectional pilot study (n=48), serum concentrations of 33 PFAS were measured, including four legacy compounds (PFOS, PFHxS, PFOA, PFNA). Plasma neuroimmune related (ITGB2, SMOC1, TREM2, GFAP) and AD related biomarkers (Ab42/40, ptau217) were detected using proteomic analysis. RESULTS: PFOS showed moderate associations with ITGB2, SMOC1, and Ab42/40 in unadjusted analyses, which attenuated after adjustment for age. PFOA and PFNA demonstrated consistent inverse associations with TREM2 before and after adjustment. DISCUSSION: Findings suggest possible compound specific PFAS associations with immune and amyloid related biomarkers, supporting further investigation in longitudinal and PFAS mixture based studies.

Ethylene oxide is a widely used industrial chemical,yet evidence linking its exposure to Parkinsons disease remains limited.Using data from participants in the United States,we examined whether exposure to ethylene oxide is associated with Parkinson's disease.This cross-sectional study included 8,430 adults from the National Health and Nutrition Examination Survey (NHANES) collected between 2013 and 2020.Information on demographic characteristics,socioeconomic factors,lifestyle behaviors,body mass index,sedentary time and major chronic conditions was analyzed. Levels of hemoglobin ethylene oxide adducts,a biomarker of ethylene oxide exposure, were evaluated in relation to Parkinsons disease using statistical modeling approaches.After accounting for potential confounding factors,higher levels of ethylene oxide exposure were associated with an increased likelihood of Parkinson's disease.The association followed a positive and linear pattern.These findings provide new population-based evidence suggesting that ethylene oxide may be linked to Parkinsons disease and highlight the need for further studies to confirm causality and to better understand the biological mechanisms involved.

Introduction Air pollution is the largest environmental risk to human health. Children are disproportionately affected by air pollution and their exposure is amplified during physical activity. Observed concentrations of nitrogen dioxide in 1 in 4 London school playground exceeds the European limit, but the health impacts of air pollution exposure in London school playgrounds remain unexplored. Our study aims to assess and compare the acute changes in lung function and airway inflammation of primary school-aged children exercising in school playgrounds. Methods and analysis 330 children aged 8 to 11 years from ten London schools will be recruited to complete 90 minutes of physical activity and 90 minutes of rest in their school playground in a randomised crossover design. Pre-, post-, and 24-hour post-exposure oscillometry measurements will be performed with airway resistance at 5 Hz (R5) the primary physiological outcome. Nasal lavage samples will be collected pre-exposure and 24-hour post-exposure for analysis of inflammatory, oxidative, and vascular biomarkers, with IL-6 as the primary biological outcome. Mixed-effects regression models will examine associations between estimated pollutant exposures, exercise and physiological responses.

Introduction Air pollution is a leading driver of cardiovascular disease with a growing body of literature implicating this in worse glucose homeostasis. Increases in fine particulate matter air pollution (PM2.5) are associated with increased blood glucose and hemoglobin A1c across the glycemic spectrum from normoglycemia to prediabetes to all forms of diabetes. Despite strong evidence for positive associations of PM2.5 with dysglycemia, it remains unknown if reducing air pollution exposure through air filtration can effect improvements in glucose. This study aims to test the hypothesis that short-term, in-home air pollution reduction using high efficiency particulate air (HEPA) filtration will improve blood sugar in adults with prediabetes. Methods and analysis This trial is a randomized, double-blind, sham-controlled trial of the effects of lowering air pollution exposure using HEPA filtration on cardiometabolic health in adults with prediabetes living in the New York City area. Participants will be randomly assigned to use bedroom air cleaners, or sham air cleaners, while measuring PM2.5 continuously for 1 month. The primary outcomes will be continuous glucose monitoring metrics measured before and after HEPA air filtration. Exploratory outcomes will include insulin resistance measures, serum biomarkers and transcriptomics measured before and after HEPA intervention. We will quantify effects of HEPA filtration with models using treatment arm (true versus sham filtration) as the independent variable. Secondary analyses will model continuous measures of PM2.5 as the independent variable. Ethics and Dissemination This study has undergone peer review; and the work was supported by Grant 2023-0214 from the Doris Duke Foundation, who had no other role in study design or implementation. The study was registered in ClinicalTrials.gov (NCT05994937) prior to recruitment. Clinical Trials Clinical Trials NCT05994937; https://clinicaltrials.gov/study/NCT05994937

Background Increasingly accessible satellite imagery provides scalable measures of the built and natural environment relevant to population health. However, whether such imagery can capture subnational variation in mortality and life expectancy remains unclear. We therefore assessed its predictive value for regional mortality and life expectancy across OECD regions. Methods We conducted an ecological, cross-sectional prediction study using 2023 data from OECD Territorial Level 3 (TL3) regions. Annual cloud-masked composites from the Harmonized Landsat and Sentinel-2 collection were processed in the Google Earth Engine, tiled at 224 x 224 pixels, and encoded with the pretrained Prithvi foundation model to derive region-level satellite embeddings. For each outcome, we trained LightGBM regressors for a country-only baseline, a satellite-only model, a combined model (country + satellite), and a final contextual model that additionally included prespecified socioeconomic and environmental covariates. Performance was evaluated using 10-fold outer cross-validation with held-out test folds; R2 was the primary metric. Results The analytic sample comprised 2,414 OECD TL3 regions across 38 countries, for which 939,959 satellite image tiles were processed. In paired bootstrap comparisons, adding satellite features to country indicators improved predictive performance for all outcomes, with incremental R2 ranging from 0.097 to 0.233. The final contextual model achieved R2 values of 0.78 (95% CI, 0.74-0.81) for crude mortality, 0.87 (0.84-0.89) for age-adjusted mortality, 0.86 (0.82-0.88) for infant mortality, and 0.76 (0.69-0.84) for life expectancy. In SHAP analyses, the aggregated satellite image effect consistently ranked among the top predictors across outcomes. Conclusion Satellite imagery captures subnational environmental heterogeneity relevant to regional mortality and life expectancy beyond country identity alone. Earth observation may therefore provide a scalable, complementary data source for characterizing geographic disparities in population health.

Background Childhood overweight and obesity represent major public health challenges, shaped by socio-economic and environmental factors. This study investigates the mediating and moderating role of urban environmental exposures in socio-economic disparities in childhood excess weight. Methods Data was drawn from a population-based sample of children (2-9 years) and adolescents (10-17 years) living in Geneva, Switzerland. Parents reported household financial situation and children's height and weight, from which excess weight (i.e. overweight or obesity) was derived. Residential exposures to air pollution (PM2.5, NO2), noise (daytime, nighttime), and neighborhood greenness (green areas, canopy coverage) were estimated based on geocoded residential addresses. The association between household financial situation and excess weight was evaluated, as well as the mediating and moderating roles of urban environmental exposures. Results The analysis included 1006 children and 1154 adolescents. Among children, an average-to-poor household financial situation was associated with higher odds of excess weight in children (adjusted odds ratio [aOR]: 1.79, 95% confidence interval [CI]: 1.13; 2.84). Higher noise exposure was associated with excess weight (daytime: aOR: 1.40, 95% CI: 1.10; 1.77, nighttime: aOR: 1.37, 95% CI: 1.08; 1.74), while the association with PM2.5 appeared stronger among socio-economically disadvantaged children, though the interaction did not reach statistical significance (financial situation x PM2.5 interaction: aOR: 1.59, 95% CI: 0.98; 2.59). No significant associations were observed among adolescents. Conclusion These findings highlight the joint influence of social and environmental inequalities on childhood excess weight and stress the need to address these interconnected determinants to design equitable, targeted public health interventions.

Background: Nitrogen dioxide (NO2) is a surrogate for traffic and industrial air pollution associated with adverse respiratory outcomes. Whether elevated NO2 and temperature jointly influence adult-onset asthma (AOA) risk is unclear, especially among subgroups with varying lifestyle and exposure profiles. We investigated further in the prospective All of Us research program. Methods: Among 596,926 U.S. participants who consented to electronic health record release, annual average NO2 concentrations from satellite data were linked to residential locations for 376,535 individuals. We used multivariable Cox regression to estimate associations between NO2, temperature, and incident AOA, adjusting for co-pollutants and potential confounders. We analyzed 4-category cross-classification variables between NO2 (high>75th percentile vs. low<=75th percentile) and maximum or average temperature (high>median vs. low<=median). We also stratified by sex, age, income, and smoking status. Additive interactions were estimated using Relative Excess Risk due to Interaction, Attributable Proportion, and Synergy Index. Results: We identified 10,413 incident AOA cases over an average 4-year follow-up. Participants with the highest categories of NO2 and temperature exposure had significantly higher risk compared to those with the lowest (HRHigh NO2 x High Max. Temp.=1.37, 95%CI:1.26-1.49; HRHigh NO2 x High Average Temp.=1.49, 95%CI:1.38-1.61). The joint association of high NO2 and high maximum temperature was more pronounced among ever-smokers (HR=1.59, 95%CI:1.40-1.81) than never-smokers (HR=1.26, 95%CI:1.13-1.41). Interaction analyses supported super-additive interactions of high NO2 and high average temperature on AOA risk, particularly among ever smokers, lower-income participants, and younger adults. Conclusion: Our findings highlight the respiratory health threat of long-term joint exposure to elevated NO2 and average temperature, particularly among vulnerable subgroups.

Environmental exposures are increasingly examined in relation to mental health, yet large-scale epidemiological analyses remain constrained by fragmented geospatial data, heterogeneous spatial and temporal resolutions, and privacy-preserving linkage requirements, limiting systematic investigation of multiple environmental domains at the population level. We present environMAP, a harmonised set of analysis-ready environmental exposure layers derived from open, global sources. environMAP spans the built environment, green and blue spaces, light exposure (solar radiation and night-time light), terrain, weather and extremes, and air pollution. We document data provenance, spatial buffers, preprocessing, projection alignment, and metadata, and provide a reproducible workflow for privacy-preserving linkage to cohort residential locations. To demonstrate utility, we linked environMAP to >200,000 adults in the German National Cohort (NAKO) and summarised self-reported lifetime doctor-diagnosed depression across exposure gradients using sex-stratified descriptive analyses. Gradients were interpretable and broadly consistent with prior evidence, supporting feasibility, scalability, and hypothesis generation. The framework is adaptable to other outcomes, cohorts, and regions.

Head and neck squamous cell carcinoma (HNSC) is a prevalent malignancy associated with poor prognosis despite recent therapeutic advances. We hypothesized that a comprehensive understanding of the spatial heterogeneity and organization of the tumor microenvironment (TME) can substantially improve risk stratification and prediction of treatment response in HNSC. As spatial transcriptomics (ST) remains labor-intensive and costly, we developed HEiST (H&E-Inferred Spatial Transcriptomics), a deep learning framework that predicts spatially resolved gene expression profiles directly from routine hematoxylin and eosin (H&E)-stained histology slides. After rigorous validation across two independent external ST cohorts, we applied HEiST to infer spatial transcriptomes across 1,500 HNSC patient tumors spanning two publicly available datasets and two newly generated cohorts, one treated with concurrent chemoradiotherapy (CCRT) and one with immunotherapy. This large-scale analysis uncovered reproducible spatial clusters characterizing the HNSC TME, defining two distinct prognostic Spatiotypes, Immune-Exhausted and Immune-Activated, with significantly distinct survival outcomes. Critically, spatial cluster composition accurately predicts HPV status and yields treatment response predictors for both CCRT/radiotherapy and immunotherapy that outperform costly gene-expression and direct image-based approaches. Notably, the ST cluster-based predictor of immunotherapy response markedly surpasses the performance of commonly used FDA-approved biomarkers, including CPS, TPS, and their combination. To the best of our knowledge, this represents the first virtual spatial profiling effort and the most comprehensive large-scale spatial TME analysis in HNSC to date. HEiST thus introduces a scalable, low-cost, and spatially grounded biomarker discovery for precision oncology in HNSC.

The impact of social parameters on brain health among people with traumatic brain injury (TBI) has been extensively documented. However, translation of this evidence into policy and clinical practice remains limited. This may reflect a lack of coordinated and equity-driven approaches to brain health that integrate diverse stakeholder perspectives, limiting progress toward equity-oriented research and service delivery models. We conducted a convergent parallel mixed-methods study guided by the REporting guideline for PRIority SEtting of health research (REPRISE). We utilized the PROGRESS-Plus framework (Place of residence, Race/ethnicity, Occupation, Gender/sex, Religion, Education, Socioeconomic status, Social capital, and context-specific parameters) to ensure systematic consideration of social parameters in the study. For Objective 1, we synthesized existing evidence on social parameters and brain health outcomes. For Objective 2, we surveyed people with lived experience of TBI, family members/friends, clinicians, researchers, and community leaders across the globe to assess their prioritization of social parameters relevant to brain health. For Objective 3, we integrated evidence synthesis and stakeholder input through a structured Round Robin consensus activity to prioritize actionable areas for feasibility and impact. The activity culminated in the development of a knowledge mobilization agenda designed to inform equity-centred policy, research, and clinical practice. In Objective 1, we identified 59 publications with evidence on the effect of PROGRESS-Plus parameters on brain health outcomes following TBI. Meta-research highlighted that education, age, and country-level indicators are prognostic for brain health after TBI. In Objective 2, the highest-ranked priorities of 113 stakeholders across four continents (North America, Europe, Africa, and Oceania) were education, access to benefits, and income. These priorities were at the centre of discussion in Objective 3, which comprised idea sharing, refinement and thematic clustering, and a final prioritization poll. The resulting final 15 priorities were organized into two tracks: Track A, actions feasible in the short term, and Track B, longer-term implementation priorities. Building on this priority-setting process, co-created with stakeholders around the globe, the findings provide a roadmap for integration of social parameters in TBI research, knowledge exchange, policy, and practice.