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Influenza is an acute viral respiratory infection that generates a substantial clinical and socioeconomic burden, especially among high-risk populations such as older adults. In Spain, influenza accounts for approximately €128 million in direct healthcare costs each season, with 80% of expenditures concentrated in individuals over 45 years of age. Vaccination remains the most effective intervention to prevent complications and reduce healthcare pressure. However, current coverage rates are consistently below international targets and continue to decline. In this context, there is an urgent need to strengthen vaccination uptake, especially among high-risk groups. Increasing vaccination uptake is essential to lessen the clinical, societal, and economic burden of influenza, since modest improvements in coverage have a significant impact on public health outcomes and economic resilience. In fact, previously published modeling analyses indicate that a 1% reduction in national vaccination coverage could result in over 6400 additional influenza cases and an additional economic burden of €1.54 million per season. Furthermore, increasing coverage to 75% in Spain could prevent approximately 180,300 additional cases-equivalent to 20% of the total-which would translate into potential savings of €43 million (€26 million in direct medical costs and €17 million in work absenteeism costs). This manuscript provides an overview of the influenza burden and vaccination landscape in Spain, focusing on clinical, societal, and economic implications of suboptimal coverage, as well as the current challenges and opportunities to improve uptake. Available evidence indicates that influenza vaccination reduces severe outcomes and healthcare burden, suggesting that mildly improving coverage in Spain could yield substantial health and economic benefits. Taken together, these findings support influenza vaccination as a public health priority and a relevant investment for health systems.

In addition to the already positive outcomes of initiating home mechanical ventilation (HMV) at home, we aimed to explore the economic impact of implementation of initiating HMV at home versus traditional clinical settings, focusing on the Dutch healthcare system. An Excel-based cost calculator model was used adhering to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) budget impact analysis guidelines and based on the estimated population of HMV patients from 2023 to 2027 in the Netherlands. We compared home initiation of HMV against inpatient initiation in two scenarios based on patient eligibility and assessed financial impact from both health payer and societal perspectives. Substantial cost savings in both scenarios were observed, with significant reductions in hospital admission days and in waiting time among patients who initiated treatment at home. The main scenario (55% of patients initiated at home) indicated savings of €8.4 million for health payers and €10.0 million from a societal perspective over 5 years. The high-uptake scenario (85% of patients initiated at home) showed even greater savings: €13.8 million for health payers and €16.4 million from a societal perspective over 5 years. Tornado diagrams highlighted the low risk of unsuccessful implementation and potential for greater benefits than projected. Initiating HMV at home yields substantial budget savings for payers and society in the Netherlands without compromising previously demonstrated clinical outcomes. These findings support broader implementation of home-based HMV initiation and may inform reimbursement reform and wider adoption of home-based HMV initiation.

Despite widespread receptivity to risk-based quality management (RBQM) principles, adoption has been limited as drug development stakeholders have lacked quantitative evidence demonstrating return on investment (ROI). Two assessments were conducted to estimate the ROI of RBQM. (1) Clinical trial level ROI was derived by analyzing monitoring cost efficiencies and clinical phase duration reductions under a 10% source data verification (SDV) scenario. (2) Program-level ROI was derived using an expected net present value (eNPV) modeling approach. Both analyses included data based on actual RBQM use in 18 recently completed oncology trials, as well as published and proprietary industry benchmarks. RBQM use was associated with reductions of 8-19% in clinical phase durations and monitoring cost reductions of up to 18% under a 10% SDV scenario. Total clinical trial ROIs ranged from $3.2 million (phase 1) to $18.9 million (phase 3), corresponding to ROI multiples of 6× to 23×. Program-level eNPV gains ranged from $3.8 million in phase 1 to $13.8 million in phase 3 corresponding to ROI multiples of 4× to 14×. The ROI demonstrated at the individual clinical trial level and at the development program level provides a compelling business case for adopting RBQM particularly given alignment with ICH E6 R3 guidelines encouraging risk-based approaches supporting clinical trial planning and execution. Study findings indicate that RBQM-supported oversight not only improves data quality and risk detection but also represents a strategic investment that can help optimize resource allocation, increase development efficiency and speed, and enhance portfolio productivity.

Although studies have reported on the impact of the COVID-19 pandemic on colorectal cancer (CRC) screening, data on long-term changes remain limited, particularly during the later phases of the pandemic (post 2021) and among racial and ethnic subgroups. We analyzed data from the 2019, 2021, and 2023 National Health Interview Survey to assess clinician recommendations for CRC screening, past 2-year screening rates, and screening modalities used. Trends were compared across racial/ethnic groups and social determinants of health, including insurance status and income. Our sample represented 95 million individuals in 2019, 97 million in 2021, and 98 million in 2023. Compared with 2019, CRC screening recommendations dropped by 20% in 2021 (P<.0001), with larger decreases among Hispanics (28%) and uninsured individuals (41%). Recommendations remained 11% lower in 2023 (P=.03). In 2021, past 2-year colonoscopy use declined by 8% (P=.001), whereas multitarget stool DNA testing (mt-sDNA) increased by 18% (P=.03) and fecal immunochemical test/fecal occult blood test (FIT/FOBT) use increased by 55% (P<.001). Colonoscopy declines were greatest among Asian individuals (32%), whereas increases in FIT/FOBT use were highest among Hispanic (78%) and Black (92%) individuals. By 2023, colonoscopy use had returned to prepandemic levels (28%), and mt-sDNA use increased by an additional 40%, resulting in an overall 8% increase in past 2-year screening compared with 2019 (P<.001). Early in the pandemic, increased stool-based testing offset reduced colonoscopy uptake. Colonoscopy rates have since recovered, whereas stool-based testing continues to increase, with notable differences across ethnic groups. This sustained shift toward stool-based testing offers an opportunity to improve screening, especially where colonoscopy access is limited.

Early motherhood remains a major public health and equity challenge in low- and middle-income countries, especially in Latin America. While poverty, education, gender norms, and barriers to sexual and reproductive health are well-established determinants, how household income translates into (or fails to translate into) delayed childbearing remains insufficiently understood-particularly in highly unequal settings marked by informality and fragmented social protection. To examine the association between household income and early motherhood in Mexico, and to disentangle direct and indirect socioeconomic pathways linking income to early childbearing. We conducted a population-based, multi-method analysis using harmonised municipal microdata from the 2000, 2010, and 2020 Mexican Population and Housing Censuses (n&#x2009;=&#x2009;3.05&#xa0;million women aged 12-24 years, representing 30.6&#xa0;million individuals). Total household income was imputed using Quantile Random Forests trained on nationally representative income-expenditure surveys. Our analytical strategy combined: (1) machine learning models to identify key correlates and non-linear individual-level patterns of early motherhood; (2) Bayesian spatial models (INLA) to quantify subnational clustering and contextual heterogeneity; and (3) dynamic structural equation modelling to assess direct and lagged relationships among income, schooling-related indicators, union formation, and early motherhood outcomes. Early motherhood prevalence declined modestly from 21.46% (2000) to 19.31% (2020) and remained concentrated in socioeconomically disadvantaged households. In individual-level models, marital status, age, household structure, and dependency ratio were the dominant correlates; income ranked eighth in feature importance. Partial dependence plots indicated a non-linear inverse L-shaped association: predicted risk increased up to approximately Int$ 1,760 (2018 PPP) per adult equivalent, then plateaued and declined slightly. Parity-specific models showed that income's predictive relevance weakened with higher parity, with U-shaped patterns at the lowest income levels for second and &#x2265;third births. Municipal analyses revealed persistent spatial clustering after adjustment for key predictors, and residual "place" effects consistent with unmeasured contextual constraints shaping early motherhood. In dynamic models, the direct income-early motherhood path was not statistically significant. Overall patterns were consistent with predominantly indirect linkages operating through schooling- and union-related indicators and reinforcing disadvantage dynamics at the municipal level. In Mexico, early motherhood remains strongly shaped by intersecting social and territorial inequalities. Income gradients are modest and appear to operate mainly through schooling lag and union-formation pathways rather than as an isolated driver. Equity-oriented strategies are likely to be most effective when they integrate school retention, prevention of early unions, and youth-friendly sexual and reproductive health services, prioritising municipalities with persistently elevated risk.

Background/Objectives: Cognitive decline and dementia represent a growing global crisis, affecting over 57 million individuals worldwide, projected to exceed 150 million by 2050. The 2024 Lancet Commission identified hearing loss as the single largest modifiable dementia risk factor (~7% population-attributable fraction). Obstructive sleep apnea (OSA), affecting ~936 million adults, is an increasingly recognized contributor yet remains underdiagnosed, especially in low- and middle-income countries (LMICs). This review synthesizes evidence on the global burden of cognitive decline associated with both conditions, evaluates causality debates, and identifies research gaps. Methods: Following SANRA guidelines, a search was conducted across PubMed, Scopus, Web of Science, and the Cochrane Library through February 2026. Original studies, systematic reviews, meta-analyses, and WHO/GBD reports were included; editorials and non-English publications were excluded. After deduplication, 3847 records were screened, and 96 studies met the inclusion criteria. Results: OSA has been linked to cognitive decline through several plausible mechanisms, including intermittent hypoxia, sleep fragmentation, impaired glymphatic clearance, and amyloid-beta accumulation, though the directionality of these associations requires confirmation from longitudinal studies. Hearing loss contributes to cognitive load, social isolation, and cortical reorganization. Both conditions disproportionately affect LMICs, where access to diagnosis and treatment remains limited. CPAP and hearing rehabilitation show cognitive benefits when initiated early, though evidence for reversing established impairment remains limited. A synergistic interaction between the two conditions is biologically plausible but empirically underexplored. Conclusions: OSA and hearing loss are highly prevalent conditions associated with increased dementia risk, though the certainty of causal relationships and the magnitude of intervention effects differ between the two conditions and across the available evidence. Integrated screening and early intervention could yield substantial neuroprotective benefits in high-risk populations and LMICs. Future longitudinal studies should examine combined cognitive trajectories and optimal intervention timing.

To estimate the current costs of non-occupational temporary sick leave attributable to tobacco (SLAT) from the perspective of the Instituto Mexicano del Seguro Social (IMSS), a public social security institution and Latin America's largest insurer. Data were drawn from anonymized IMSS sick leave records (2010-2022) for workers in the formal sector. Selected tobacco-related non-communicable diseases (NCDs) included chronic obstructive pulmonary disease (COPD), lung cancer, acute myocardial infarction, cerebrovascular disease, diabetes, and mental illnesses. Smoking-attributable fractions were applied to estimate SLAT cases. To address uncertainty, 1,000 random samples without replacement were used. Financial costs were derived from IMSS subsidy records and converted to 2023 Mexican pesos (Mx2023$). Broader social costs were estimated using full salary amounts, as IMSS covers only 60% of wages from the fourth day of leave. Reporting follows STROBE recommendations. On average, sick leave totaled 32,547 events per year, 29.5% of which were tobacco-attributable. COPD-related absences increased during the COVID-19 pandemic, while absences due to diabetes and cerebrovascular cases decreased. The median leave duration ranged 3-97 d, with AMI-related durations nearly halving over time. One in every three pesos spent by IMSS on sick leave payments for major NCDs is tobacco-attributable. In 2022, this amounted to Mx$118.1 million (Mx$113.1 direct and Mx$5.0 secondhand smoke). Including total wage losses, the costs reached Mx$205.7 million. Tobacco significantly contributes to NCD-related sick leave costs, highlighting the need for workplace prevention and cessation strategies to reduce the health and financial burden. Sick leave data may constitute a valuable tool for epidemiological surveillance.

The share of the world population living in cities with more than one million people rose from 11% in 1975 to 24% in 2025 (our estimates). Will this trend toward greater concentration in large cities continue or level off? We introduce two new city population datasets that use consistent city definitions across countries and over time. The first covers the world between 1975 and 2025, using satellite imagery. The second covers the United States between 1850 and 2020, using census microdata. We find that urban growth follows a characteristic life cycle. In the early stages of a country's urbanization process, large cities grow faster than smaller ones. At later stages, growth rates equalize across sizes. We use this life cycle to project future population concentration in large cities. Our projections suggest that 38% of the world population will be living in cities with more than one million people by 2100. This estimate is higher than the 33% implied by the well-known theory of proportional growth, but lower than the 42% obtained by extrapolating current trends.

Head and neck cancer (HNC) accounts for nearly 1 million new cases and approximately half a million deaths annually worldwide, representing a substantial global health burden. Despite an overall decline in mortality in many regions, patterns vary across anatomical subsites and countries. To evaluate international and national mortality trends in HNC and its major anatomical subsites from 2001 to 2023. This observational epidemiologic study analyzed population-based mortality data from the World Health Organization (WHO) Mortality Database for 73 countries between 2001 and 2023. Data collection was conducted in May 2025, and the data were analyzed between May and October 2025. Eligible countries had medium- to high-quality vital registration data. Age-standardized mortality rates (ASRs) were calculated using the new WHO World Standard Population. Country-specific trends from 2010 to 2023 were assessed using joinpoint regression among countries with at least 7 years of available data. ASRs and average annual percentage change in HNC mortality overall and by anatomical subsite using population-based analysis. From 2001 to 2023, 2&#x202f;000&#x202f;066 HNC-related deaths (450&#x202f;657&#x2009;females; 1&#x202f;549 409 males) were recorded. Internationally, ASRs decreased by 38.9%, with greater reductions among male individuals than among female individuals. Substantial divergence was observed across subsites: laryngeal cancer mortality decreased markedly (-50.1%), whereas increasing trends in oropharyngeal cancer mortality were observed in many countries in recent years, particularly in high-income countries such as the UK (average annual percentage change, 4.30%) and the US (average annual percentage change, 3.26%). Results of this study suggest that, although overall HNC-related ASRs have declined internationally, marked variation across subsites and countries persists. The contrasting trends between laryngeal and oropharyngeal cancers underscore the need for subtype-specific prevention strategies and continued global surveillance.

In 2024, four histamine poisoning outbreaks associated with the consumption of Scombridae fish were reported in Vietnam, resulting in more than 400 hospitalisations. In response to these incidents, this study assessed the risk of histamine poisoning associated with the consumption of three Scombridae species, namely narrow - barred Spanish mackerel (Scomberomorus sp.), bullet tuna (Auxis sp.), and Indian mackerel (Rastrelliger sp.), in Nghe An Province, where the highest frequency of recent outbreaks was recorded. Histamine concentrations were determined by LC-MS/MS in 540 samples collected from wet markets/fish landing ports, supermarkets/cold storage facilities, and food service establishments. Overall, histamine concentrations were generally low across the three Scombridae species and distribution channels. The highest concentrations were most often observed in samples from wet markets/fish landing ports, with maximum levels of 63.9, 109, and 411&#x2009;mg/kg recorded for Spanish mackerel, fresh Indian mackerel, and bullet tuna, respectively. Grilled Indian mackerel from supermarkets was identified as a product category of potential concern, with a higher proportion of samples exceeding 15&#x2009;mg/kg and a maximum histamine concentration of 319&#x2009;mg/kg, possibly reflecting variation in raw material quality before heat treatment. As expected, the estimated risks of experiencing histamine poisoning symptoms were relatively low for Spanish mackerel and bullet tuna, at 4-7 cases per million servings and 6-14 cases per 10 million servings, respectively. In contrast, grilled Indian mackerel products had the highest estimated risk, with a probability of 3-6 cases per 10,000 servings. These findings highlight the need to strengthen surveillance of grilled Indian mackerel products and to conduct further studies to identify critical stages in the processing and distribution chains where histamine formation may occur.

Measuring human interactions with protected areas is a key need in conservation social science, both to assess the potential for continuing negative impacts on sensitive resources and to support positive relationships with important places. Our paper investigates human activities in California Marine Protected Areas (MPAs) using an emerging approach of large-scale participatory data collection coupled with statistical analysis. Twelve organizations across the state developed, standardized, and implemented a data collection protocol for volunteers to record human activities, called "MPA Watch." From 2012-2020, approximately 1,900 surveyors conducted more than 31,700 surveys, observing more than 1.2 million activities at 104 different sites across California's coast, both inside and outside MPAs. We analyzed these data using generalized linear mixed models (GLMMs) with zero inflation to account for sampling bias associated with volunteer-driven data. We included covariates regarding weather and tides; time of day, week, and year; and beach type and amenities. We found statistically significantly lower consumptive activities inside MPAs with the strictest protections as compared with non-MPAs, and no significant difference between MPAs and non-MPAs for non-consumptive recreational activities. Some consumptive activities significantly declined over time. We also detected expected use patterns (e.g., for all activity categories, weekend counts were significantly higher than weekday counts). In addition, we noted patterns of interest for future study, including much higher incidence of non-consumptive activities than consumptive activities at a statewide level; higher popularity of some activities at certain individual sites; and the beginnings of the effects of the COVID-19 pandemic in 2020. Our analysis demonstrates that consumptive activities are lower inside California MPAs (indicating compliance with MPA rules), and results at particular sites can inform site-specific management strategies. Further, we show the potential of large-scale participatory science, coupled with appropriate statistical modeling, to monitor and inform conservation policy and management for protected areas.

Approximately one-quarter of intensive care unit admissions include a severe maternal morbidity event. We hypothesized that non-severe maternal morbidity intensive care unit admissions would have an increased need for intensive care unit-specific services compared to admissions with severe maternal morbidity. We conducted a retrospective cohort study using the Premier Database to examine delivery hospitalizations that included intensive care unit admissions (10/2015-12/2020). We used ICD-10 codes to identify severe maternal morbidity and charge codes to identify services usually provided in intensive care units. We performed descriptive analyses for intensive care unit service codes and multivariable logistic regression to assess factors associated with admissions in patients with no codes for severe maternal morbidity. Among 4.8 million delivery hospitalizations, 35,837 patients were admitted to the intensive care unit. Of intensive care unit admissions, 9,803 (27.4%) met severe maternal morbidity criteria. Intensive care unit-specific charge codes were more commonly charged to patients with severe maternal morbidity compared to those without severe maternal morbidity: invasive blood pressure monitoring (21.0% vs. 3.8%) and invasive hemodynamic monitoring (2.0% vs. 0.2%). Codes for intravenous fluid amounts suggest that severe maternal morbidity patients received higher volumes of intravenous fluid. Median intensive care unit length of stay was similar between groups. Comorbidities such as cardiomyopathy and preeclampsia were associated with increased non-severe maternal morbidity intensive care unit admission. Intensive care unit admission may be driven by concern for clinical deterioration among patients with high-risk comorbidities rather than the presence of severe maternal morbidity.

Lonicera macranthoides is an important medicinal plant in the genus Lonicera, and its flowers possess significant medicinal value. 'Jincuilei' (JCL), a newly bred variety, is characterized by a long flowering period and persistently closed corollas. To investigate the molecular mechanism underlying the non-dehiscent corolla phenotype in JCL, we performed whole-genome sequencing, transcriptomics, and metabolomics analyses of this variety. The results showed that the L. macranthoides genome size is approximately 879.83&#x2009;Mb, assembled into 439 contigs with a contig N50 of 67.38&#x2009;Mb, which were further scaffolded into 9 pseudochromosomes. Comparative genomics analysis revealed that the divergence time between Lonicera japonica (Japanese honeysuckle) and L. macranthoides occurred approximately 15.1 million years ago (Mya). Within L. macranthoides, comparisons identified 525 expanded and 555 contracted gene families relative to L. japonica. Analysis of endogenous hormone levels in floral organs of JCL and wild-type (WT) plants across different developmental stages revealed highly significant differences in jasmonic acid (JA) content at all stages. Further transcriptomic and metabolomic analyses indicated that both the JA biosynthesis and signaling pathways play key roles in regulating corolla dehiscence. These findings provide valuable insights into Caprifoliaceae evolution and the molecular basis of corolla morphogenesis in L. macranthoides.

Data-driven safety monitoring in general aviation (GA) faces a critical bottleneck: the scarcity of high-quality flight data containing verified anomalies, particularly across heterogeneous aircraft fleets. Existing datasets often lack physical realism or are limited to single aircraft models. Here, we present a comprehensive benchmark dataset derived from 120 real flight sorties (approximately 1 million data points) collected from Cessna 172 (R/S) and Cirrus SR20 (G1/G6) training aircraft. We developed a physics-based synthetic injection framework to generate four types of distinct anomalies: throttle surge, flight path deviation, cylinder misfire, and pitch excursion. The dataset is structured hierarchically by anomaly type and aircraft model, providing paired normal and abnormal samples for direct counterfactual analysis. We validate the dataset's utility through statistical distribution analysis and baseline anomaly detection benchmarks. This resource bridges the gap between simulation and reality, enabling the development of robust, model-agnostic monitoring algorithms for aviation safety.

Radiology report generation is an important application of artificial intelligence (AI), as the interpretation of medical images and the production of clinically relevant reports are time-consuming and cognitively demanding tasks, especially in high-volume settings such as chest X-ray screening. Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled substantial progress in automated medical report generation. However, most existing medical foundation models are trained primarily on English datasets, limiting their practicality in non-English-speaking regions such as Japan. Publicly available radiology datasets are overwhelmingly English, while constructing large-scale non-English datasets is costly and difficult because of translation effort and medical data privacy constraints. Existing adaptation methods, such as fine-tuning and continued pre-training, typically require large amounts of in-domain data. This makes them difficult to apply in low-resource medical language settings where large-scale annotated datasets are unavailable. Accordingly, this study asks: how can an accurate Japanese chest X-ray radiology report generator be developed without access to large-scale, curated Japanese medical image-report pairs? To address this challenge, we propose JRadiEvo, a Japanese chest X-ray report generation model built through evolutionary optimization of model merging. By combining pretrained models with complementary strengths in vision-language alignment, medical knowledge, and Japanese generation, JRadiEvo enables data-efficient adaptation without large-scale training. To the best of our knowledge, this is the first attempt to build a non-English medical vision-language model through evolutionary optimization of model merging. Despite using only 50 translated training samples from publicly available data, JRadiEvo outperforms CheXagent, a state-of-the-art model trained on approximately 8.5 million samples, in ROUGE-L and METEOR metrics. These results provide a proof of concept for extreme data-efficient adaptation in low-resource medical languages.

Global armed conflict and population displacement are increasing, yet their association with population health remains poorly understood. We developed and tested four theoretical models linking armed conflict, population displacement, and socioeconomic development to measles burden across 193 countries from 2000 to 2023. We analyzed longitudinal country-level data comprising 4,632 country-year observations, combining fixed-effects panel regression and structural equation modeling (SEM). Observed variables included battle-related deaths (BRDs) and forcibly displaced population sizes, while socioeconomic development was modeled as a latent variable incorporating gross domestic product (GDP) per capita, life expectancy, and mean years of schooling. Outcomes were total measles cases and incidence per million population. All four constructed models demonstrated excellent fit (Comparative Fit Index [CFI] 0.991-0.996; Tucker-Lewis Index [TLI] 0.976-0.989; Root Mean Square Error of Approximation [RMSEA] 0.046-0.062). Higher contemporaneous BRDs were associated with higher measles cases (&#x3b2;&#x2009;=&#x2009;0.17; 95% Confidence Interval [CI] [0.14, 0.20]; p&#x2009;<&#x2009;0.001), adjusting for population displacement and economic development. When prior-year BRDs were included as an observed variable, the direct effect of contemporaneous BRDs was no longer significant (&#x3b2;&#x2009;=&#x2009;0.05; 95% CI [-0.01, 0.11]; p&#x2009;=&#x2009;0.091), while the effect of prior-year BRDs was significantly associated with measles cases (&#x3b2;&#x2009;=&#x2009;0.14; 95% CI [0.08, 0.20]; p&#x2009;<&#x2009;0.001) but not incidence after accounting for displacement (&#x3b2;&#x2009;=&#x2009;0.04; 95% CI [-0.02, 0.11]; p&#x2009;=&#x2009;0.164). Each standard deviation (SD) increase in a country's standardized log-transformed BRDs was associated with an approximately 0.20 SD increase in measles cases, equivalent to 2,500 additional reported cases for every 3,700 BRDs. In all models, BRDs had a slight negative association with socioeconomic development (&#x3b2;&#x2009;= -0.10; 95% CI [-0.13, -0.07]; p&#x2009;<&#x2009;0.001), and each SD increase in displaced population corresponded with a 0.20 SD decline in socioeconomic development (95% CI [-0.23, -0.17]; p&#x2009;<&#x2009;0.001), which was the primary pathway by which displacement was associated with measles. Socioeconomic development, in turn, had a significant direct association with both measles cases and incidence, with each SD increase in socioeconomic development corresponding to a 0.32 to 0.34 and 0.34 to 0.36 SD reduction in cases and incidence, respectively (p&#x2009;<&#x2009;0.001). Key limitations include reliance on national-level annual aggregates, possible under-reporting of both battle-related deaths and measles cases, and the possibility of unmeasured time-varying confounders that preclude causal interpretation. Armed conflict is associated with an increased measles burden, both directly and indirectly through associations with lower socioeconomic development and greater population displacement. These findings suggest that mitigating infectious disease risks in volatile settings requires a dual strategy: preserving the structural foundations of health and education while systematically integrating displaced populations into routine immunization programs. Future research using subnational and higher-frequency data is needed to clarify the precise mechanisms and timing of these associations across other vaccine-preventable diseases.

Real-time biomechanical feedback during table tennis training demands both low latency and high recognition accuracy, yet existing systems sacrifice one for the other due to cloud-transmission delays and the computational constraints of edge devices. This paper presents EdgeFusionNet, an integrated edge-cloud collaborative architecture that delivers actionable stroke-level feedback within 32&#xa0;ms under realistic network conditions. At its core, a lightweight cross-modal attention fusion network (LCA-FNet) fuses temporally aligned features from high-speed vision, inertial measurement, and surface electromyography streams through shared-projection cross-modal attention and adaptive channel gating, achieving 93.6&#x2009;&#xb1;&#x2009;0.4% recognition accuracy (Macro-F1&#x2009;=&#x2009;0.927&#x2009;&#xb1;&#x2009;0.005, mean&#x2009;&#xb1;&#x2009;SD over five seeds) across seven canonical stroke types with only 1.48 million parameters, and retaining 90.4&#x2009;&#xb1;&#x2009;2.3% accuracy under a strict leave-one-subject-out evaluation. A hardware-triggered synchronization mechanism maintains sub-millisecond cross-modal alignment, while a two-level knowledge distillation strategy recovers 97.8% of the cloud-resident teacher model's accuracy after aggressive structural compression. An adaptive computation offloading agent, trained via Q-learning with explicitly defined state, action and reward spaces, dynamically partitions inference between the edge node and cloud server based on prediction entropy and network quality, sustaining sub-32&#xa0;ms P95 end-to-end latency even at 5 Mbps uplink bandwidth. Field deployment over thirty training sessions with twelve athletes per study arm confirmed ecological validity, yielding 91.8&#x2009;&#xb1;&#x2009;0.7% accuracy under uncontrolled gymnasium conditions, a Cohen's kappa of 0.874 against the consensus of two expert coaches (whose own inter-coach kappa was 0.892), and a 10.3-percentage-point gain in standardized multi-ball hit rate over a matched control group after four weeks (p&#x2009;<&#x2009;0.001). These results demonstrate that principled co-design of multimodal fusion, model compression, and adaptive offloading can bridge the gap between laboratory-grade recognition performance and the stringent latency requirements of live athletic training.

Epilepsy affects more than 50 million people worldwide, and the identification of new antiseizure medications (ASMs) with adequate blood-brain barrier (BBB) penetration remains a major challenge. The pentylenetetrazol (PTZ)-induced zebrafish model is widely used for early ASM screening, but most studies use larvae at 7 days post-fertilization (dpf) or earlier, before later stages of BBB maturation are fully assessed. Here, we evaluated whether staged testing in 7-, 14-, and 21-dpf zebrafish could improve prioritization of CNS-active compounds before mammalian testing. Thirty-one Vitamin K (VK) analogs were screened in 7-dpf zebrafish using PTZ-induced seizure-like hyperlocomotor activity as a phenotypic endpoint. Seventeen compounds significantly reduced PTZ-induced hyperlocomotor activity at 7 dpf. Based on quantitative selection criteria, three compounds were advanced to dose-response studies in older zebrafish, and two compounds retained activity at 14 and 21 dpf. These compounds were then evaluated in the mouse 6 Hz seizure model. Both compounds showed protection at 32 mA, whereas only compound 3d remained active in the higher-intensity 44 mA model. Brain-to-plasma analysis confirmed CNS exposure for 3d and 3l, with ratios of 2.64 and 0.853, respectively. These findings support later-stage zebrafish screening as a practical phenotypic prioritization step for selecting CNS-active compounds for mammalian seizure models, while highlighting the need for direct mammalian validation and further PK/PD characterization.

Ozzy Osbourne, the legendary frontman of Black Sabbath, publicly revealed his diagnosis of Parkinson's disease (PD) in 2020, offering visibility to a complex neurodegenerative condition. His case, later linked to a mutation in the PARK2 (parkin) gene, presented atypically with a later age of onset, contributing to ongoing discussions about the phenotypic variability of genetic forms of PD. Beyond medical narratives, Osbourne's openness and philanthropy-culminating in a benefit concert that raised $190 million for Parkinson's and pediatric charities-played a transformative role in destigmatizing the disease. This article explores the scientific and social impact of Osbourne's disclosure, highlighting the role of PARK2 in mitochondrial homeostasis, synaptic integrity, and tumor suppression. We also examine his pursuit of experimental stem cell therapy, discussing its scientific basis, ethical considerations, and current clinical research landscape.