Search Results (420)

Background: Long-acting monoclonal antibodies have become an important strategy for preventing respiratory syncytial virus (RSV) disease in infants. Nirsevimab is the first product for which substantial post-licensure implementation data are available, whereas real-world evidence on clesrovimab remains limited. Although nirsevimab has demonstrated high efficacy, its uptake varies considerably across countries, healthcare systems, delivery settings, and population subgroups. This World Association for Infectious Diseases and Immunological Disorders (WAidid) consensus document examines the policy, organizational, economic, and equity-related determinants that shape real-world implementation of long-acting monoclonal antibodies for infant RSV prevention. Methods: This study was conducted as a structured narrative review and WAidid expert consensus document. A structured literature search was performed in PubMed and Embase for English-language publications relevant to nirsevimab uptake and implementation, complemented by targeted review of surveillance reports, policy documents, and public health guidance from the ECDC, UKHSA, and CDC, as well as reference lists of selected publications. Eligible sources included observational and real-world implementation studies, systematic reviews and meta-analyses, economic evaluations, guidelines, policy statements, surveillance reports, and relevant narrative reviews. Evidence was synthesized qualitatively according to policy frameworks, financing and reimbursement, delivery pathways, demographic and socioeconomic determinants, and healthcare-system factors influencing uptake. No statistical software was used because no quantitative re-analysis or meta-analysis was performed. Results: Nirsevimab uptake was strongly influenced by national RSV prevention policies, particularly whether countries adopted universal infant monoclonal antibody programs, maternal RSV vaccination strategies, dual maternal–infant approaches, or targeted risk-based models. Universal, publicly funded programs integrated into neonatal care achieved the highest and most homogeneous coverage, especially when administration occurred before hospital discharge and was supported by registry-based recall systems for infants born outside the RSV season. In contrast, fragmented, outpatient-only, insurance-dependent, or partially reimbursed models were associated with lower, delayed, or more variable uptake. Additional determinants included product cost, reimbursement pathways, provider practices, caregiver awareness and health literacy, insurance status, income, race and ethnicity, geographic deprivation, and access to primary pediatric care. Most available evidence comes from high-income countries, limiting generalizability to low- and middle-income settings, where RSV burden is greatest and implementation constraints may differ. Conclusions: Successful implementation of long-acting monoclonal antibodies for infant RSV prevention requires more than regulatory approval and demonstrated efficacy. Equitable uptake depends on clear national recommendations, sustainable public financing, reliable product supply, integration into neonatal and primary pediatric care, proactive identification and recall of eligible infants, and targeted strategies to reduce socioeconomic and geographic disparities. Although many determinants identified in high-income settings are likely relevant globally, their feasibility, relative importance, and impact require dedicated evaluation in low- and middle-income countries. Full article

Fusariumoxysporum f. sp. cubense race 4 (Foc 4) causes Fusarium wilt by penetrating root cell walls, yet the molecular basis of cell wall-mediated resistance remains poorly understood. Here, we investigated the transcriptional, enzymatic, and cellular responses of the resistant banana cultivar Dongjiao No. 1 (DJ) and its susceptible mutant ke2 following Foc 4 infection. RNA sequencing revealed that DJ specifically upregulated a pectin degradation cassette comprising pectin methylesterase (PME3-Like), pectin acetylesterase (PAE1), and polygalacturonases (PG1, PG3, PG5, PG-Like-4) at the bud seedling stage. Immunolocalization further revealed robust, tissue-specific PME deployment, with stable abundance at the primary infection site and differential redistribution in aerial tissues. This enzymatic cascade degraded homogalacturonan, confirmed by the simultaneous loss of pectin epitopes recognized by JIM5 and JIM7 antibodies. Additionally, the upregulation of PTI1, MAPK cascades, calcium-dependent protein kinases (CDPK3, CML31), and respiratory burst oxidase homologs (RBOHs) was also observed in DJ. The differential transcription of salicylic acid signaling (TGA1–PR1), jasmonic acid derepression (TIFY/JAZ), and flavonoid phytoalexin biosynthesis in DJ further reinforced the defense response. In contrast, ke2 failed to activate the pectin degradation machinery, exhibited attenuated immune signaling, and retained intact pectin vulnerable to pathogen exploitation. These findings establish pectin degradation-mediated immunity as a resistance mechanism in banana and provide potential targets for Fusarium wilt resistance breeding. Full article

The deployment of deep-learning vulnerability detectors in production remains difficult. Models are large, false-positive rates are high, output is opaque, and a persistent gap separates benchmark performance from real-world utility. The objective of this work is to close part of that gap by combining a learned detector with interpretable rules so that accuracy, efficiency, and actionability are achieved together. A hybrid framework is therefore presented in which a lightweight edge-conditioned GNN of 71,810 parameters, named FastVulnGNN, trained in 96.2 s on a single CPU core, is paired with rule-based heuristic detection for six C/C++ vulnerability classes, namely buffer overflows, format-string defects, null-pointer dereferences, double-free errors, integer overflows, and race conditions. On the MegaVul dataset, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are obtained by the GNN component. On a production codebase of 499 files and 312,758 lines of code, the full hybrid scan completes in 5.5 s, which corresponds to about 57,000 lines per second, without any GPU hardware. Per-file risk tiers and pattern-level explanations are produced, and these are suitable for continuous-integration use. The significance of this work lies in demonstrating that a deployable, explainable detector can be assembled from compact components, and an edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are reported to characterize the approach. Full article

Pediatric acute myeloid leukemia (AML) is a highly heterogeneous malignancy, with cytogenetic and molecular abnormalities playing a critical role in determining prognosis and guiding treatment decisions. Despite therapeutic advances, patients with high-risk genetic mutations and translocations continue to experience suboptimal outcomes. As new targeted therapies emerge, the treatment of pediatric AML could undergo a paradigm shift, where “one-size-fits-all” chemotherapy is no longer the only frontline approach. Identifying genetic markers inform risk stratification and have greater impact on shaping the therapeutic approach, including the integration of targeted therapies such as FLT3 and menin inhibitors into frontline therapy. Furthermore, pediatric AML treatment options are being driven by recent discoveries in adult AML, broadening their clinical trials to include pediatric patients, in part due to the RACE for Children Act that went into effect in August 2020. This review identifies the most prevalent high-risk cytogenetic lesions in pediatric AML, emphasizing their incidence, prognostic significance, and implications for clinical management. By synthesizing current research on these key genetic abnormalities and their associated therapies, we aim to provide an updated perspective on the evolving landscape of high-risk pediatric AML management that can then lead to the establishment of an agile framework to rapidly evaluate, approve, and deploy novel agents. Full article

Background: Atherosclerotic heart disease (AHD) remains a leading cause of global mortality and a major driver of fatal cardiac syndromes. Ischemic myocardial injury from AHD can lead to ventricular remodeling and myocyte loss, progressing to heart failure (HF) and, in advanced stages, cardiogenic shock (CS). However, long-term national mortality trends for deaths involving the combined burden of AHD, HF, and CS remain insufficiently characterized. Methods: Mortality data from 1999 to 2023 were obtained from the CDC WONDER Multiple Cause of Death database. Deaths involving CS (ICD-10 R57.0), HF (I50, I50.1, I50.9), and AHD (I25.1) in adults were identified. Age-adjusted mortality rates (AAMRs) per 1,000,000 population were calculated and stratified by sex, race/ethnicity, and geographic region. Temporal trends were analyzed using Joinpoint regression to estimate annual percent changes (APCs) and average annual percent changes (AAPCs). Autoregressive integrated moving average (ARIMA) modeling was used to forecast mortality trends through 2035. Results: A total of 15,631 deaths were attributed to AHD-associated HF and CS during the study period. Overall AAMRs increased significantly (AAPC 3.75%; 95% CI 3.32–4.29; p < 0.01). Mortality rates declined between 1999 and 2006 (APC −7.55%; 95% CI −13.15 to −5.23; p < 0.01), increased modestly from 2006 to 2013 (APC 3.94%; 95% CI −1.47 to 8.91), and rose sharply from 2013 to 2023 (APC 12.33%; 95% CI 11.27–14.46; p < 0.01). Males had higher AAMRs than females in 2023 (9.26 vs. 3.39 per 1,000,000). Non-Hispanic White individuals accounted for the highest number of deaths (11,878), followed by non-Hispanic Black individuals (1708), while non-Hispanic American Indian populations had the lowest counts (39). Regionally, the West (7.31) and South (5.92) exhibited the highest final AAMRs. State-level AAMRs ranged from 2.49 in Minnesota to 8.27 in Nevada. Conclusions: Mortality involving AHD, HF, and CS has risen substantially in the United States since 2013, with marked demographic and geographic disparities. These findings highlight a growing burden of advanced ischemic heart disease and heart failure and underscore the need for earlier intervention and improved access to advanced HF therapies to prevent progression to cardiogenic shock and death. Full article

This study presents a multi-tier framework for deploying multi-modal Conversational AI on edge devices, spanning from constrained ultra-low-power systems to high-performance edge workstations. Utilizing an automated model discovery process and a modular benchmarking testbed, the research demonstrates that real-time, fully edge AI execution is feasible through strategic model selection and hardware acceleration. Key outcomes from the performance analysis are as follows. Speech-to-Text: Fun-ASR-Nano achieved the highest transcription accuracy with a Word Error Rate of 0.026, while Moonshine Tiny was the most efficient, recording a Real-Time Factor of 0.036 on the CPU. Scaling up to the high-performance tier, Whisper Large-V3 Turbo demonstrated high speed and robustness on a dedicated GPU, achieving an RTF of 0.093. Language Modeling: The Qwen 2.5 (1.5B Instruct) model, optimized for the Intel edge NPU, delivered robust constrained edge performance with an average generation speed of 20.15 tokens per second and a high semantic accuracy score of 0.86. The non-transformer Liquid LFM-24B model showcased server-level reasoning capabilities on the high-performance edge, reaching an impressive 39.2 tokens per second when fully offloaded to a dedicated GPU, despite its massive VRAM requirements. Text-to-Speech: Piper TTS emerged as the most efficient model for constrained environments (RTF of 0.034). However, Kokoro TTS redefined high-fidelity zero-shot synthesis on the GPU tier, achieving a groundbreaking RTF of 0.024 and far outperforming larger autoregressive audio models like OuteTTS, which remained too slow for real-time use without significant acceleration. Hardware Acceleration and Energy Efficiency: The use of Intel OpenVINO 2026.0 for hardware offloading significantly reduced energy consumption; for example, Whisper Large-V3 Turbo’s energy per audio second dropped from 52.68 Joules on the CPU to just 3.24 Joules on the integrated GPU. Furthermore, dedicated GPU acceleration revealed a critical “race-to-sleep” paradigm, where higher peak wattage is offset by drastically reduced processing times. The study concludes by identifying two optimal cascaded pipelines: a constrained edge tier (Moonshine, Qwen 1.5B, Piper) running on a Khadas NUC (Khadas Technology, Shenzhen, China powered by an Intel processor (Intel Corporation, Santa Clara, CA, USA) maximizing energy efficiency, and a high-performance tier (Whisper V3 Turbo, Liquid LFM-24B, Kokoro) running on an NVIDIA 5060ti, delivering uncompromising accuracy and subsecond latency for privacy-preserving, advanced edge AI. Full article

Background: Lung adenocarcinoma (LUAD) is the most prevalent histologic subtype of non-small cell lung cancer (NSCLC) and exhibits considerable molecular heterogeneity. Among the most clinically significant driver alterations are mutations in EGFR and KRAS, both of which influence treatment selection and oncologic outcomes. The prevalence of these mutations varies by race, yet racial minority populations remain underrepresented in genomic studies. EGFR alterations are more frequently observed in Asian patients, while KRAS mutations predominate in non-Asian cohorts. This study aimed to characterize race-associated differences in driver mutation prevalence among Asian, Black, and White patients with LUAD. Methods: A retrospective secondary cohort analysis was performed using publicly available clinicogenomic data from the Lung Adenocarcinoma Met Organotropism cohort, accessed via cBioPortal, comprising 2653 tumor samples. Patients were stratified by self-reported race into Asian, Black, and White cohorts; cases with missing race data were denoted as either other or unknown. Mutation frequencies for EGFR, KRAS, and TP53 were extracted from OncoPrint cohort study views and compared descriptively across groups. Results: Distinct race-associated differences in driver mutation prevalence were observed. Asian patients exhibited the highest frequency of EGFR alterations (64%), compared with Black (41%) and White (28%) cohorts. In contrast, KRAS mutations were least prevalent in Asian patients (10%) and more frequent in White (33%) and Black (23%) cohorts, indicating an inverse distribution between Asian and non-Asian populations. TP53 mutation prevalence was similar in Asian (52%) and White (53%) cohorts but was notably higher in Black patients (65%). Conclusions: Asian patients with LUAD exhibit a distinct molecular profile characterized by EGFR predominance, with direct implications for eligibility for EGFR-targeted tyrosine kinase inhibitor therapy. Black patients may also benefit from EGFR-based targeted therapies, but lack of large genomic data on Black populations warrants further investigation. White cohorts display a KRAS-dominant mutation pattern, suggesting divergent tumorigenic pathways and the potential need for alternative therapeutic strategies. The elevated TP53 frequency in Black patients remains to be further characterized. These findings support integrating race-associated genomic profiling into precision oncology frameworks to improve treatment selection and reduce disparities in outcomes. Full article

In an IRONMAN-distance triathlon, run performance is a particularly strong correlate of overall race outcome, alongside cycling. Although individual research areas such as pacing, performance prediction, and laboratory physiology have been reviewed separately, they have not been integrated to determine factors contributing to running performance decline or to identify key knowledge gaps. This scoping review aims to map the current evidence on running performance decline in IRONMAN-distance triathlon and identify key gaps, with a particular focus on age-group athletes. Following the Arksey and O’Malley framework and PRISMA-ScR guidelines, we searched PubMed and SPORTDiscus for studies reporting quantitative running performance outcomes in IRONMAN races or bike-to-run transition protocols. Of 896 deduplicated records, 70 studies were included. Findings were organised into five sub-questions addressing the extent of evidence, quantification of decline, physiological and biomechanical changes, underlying mechanisms, and inter-individual variability. Pacing patterns and performance predictors are well described, whereas mechanistic research relies almost entirely on short, controlled protocols. Critically, no study identified by this review quantified the “between-context decline,” defined as the difference between an athlete’s standalone marathon performance and their actual IRONMAN running performance. Addressing these gaps requires observational studies using real IRONMAN race data and individual-level analyses, particularly for age-group athletes. Full article

Background/Objectives: Childhood-onset systemic lupus erythematosus (cSLE) is a chronic, multisystem autoimmune disease that is associated with more severe organ involvement, more intensive drug therapy, and increased long-term organ damage compared with adult-onset disease. The objectives of this study were to evaluate the performance of widely used small area-level multidimensional indicators of neighborhood disadvantage in a mixed urban–rural cSLE cohort against disease outcomes. Methods: This retrospective cohort study utilized electronic health records to identify and evaluate pediatric patients with cSLE across a single center in South Carolina between 1 January 2020 and 31 December 2024. Primary outcomes included disease activity at diagnosis, measured by Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K); the achievement of a low lupus disease activity state (LLDAS) by the last visit; the development of major organ involvement; and the rates of unplanned hospitalizations and emergency department visits. The associations of the census tract-level Area Deprivation Index (ADI), Social Vulnerability Index (SVI), and Childhood Opportunity Index (COI) with clinical presentation and outcomes were estimated using generalized linear models and logistic regression. Results: A total of 85 patients with cSLE were included, of which 76% reported being of Black race, and 28% lived in rural areas. Lupus disease severity was inconsistent across the metrics of area-level social vulnerability, neighborhood deprivation, child opportunity, and rurality. Patients who lived in more socially vulnerable communities were more likely to attain LLDAS during follow-up, while those who lived in lower-opportunity areas were more likely to develop CNS lupus. Baseline disease activity, renal involvement, and healthcare use were not significantly associated with area-level social disadvantages or rurality. Conclusions: In this single-center, mixed urban–rural cohort of children with cSLE, indicators of neighborhood-level disadvantage and rurality alone explained little variation in disease severity and disease control overall. The utility of available small area-level metrics is likely context-dependent and influenced by regional features, population characteristics, and the outcomes being studied. Future work should integrate individual- and area-level factors across diverse settings to better understand the drivers of health disparities. Full article

This study used archival data from 30,453 students enrolled in 289 adult education programs in the state of Georgia, United States during the 2018–2019 school year. We explored how student-, teacher-, and classroom-level factors predict student attendance and academic performance (reading, language, and math) over time. Results indicated several student-level factors predict attendance and educational performance, including age, gender, race/ethnicity, previous educational attainment, low-income status, special needs status, reported barriers to education, and dual enrollment. Depending on the academic subject, age, race, previous education, special needs, and dual enrollment predicted academic growth over time. Teacher-level factors such as teacher experience predicted educational performance; classroom factors such as whether the course was an Integrated Education and Training (IET) class predicted attendance, and distance classes predicted attendance and performance. Certification predicted growth over time for language, and employment status and designation as a distance class predicted growth over time for math. These findings will help state officials improve the delivery of services to further enhance and improve student outcomes. Full article

Positionality refers to a researcher’s stance within the social context of their research study. It can include information such as gender, race and class, and other characteristics like location. In addition, positionality statements can also include researchers’ personal traits, such as their thoughts, feelings and behaviors. They convey a researcher’s position and are incorporated within research articles to make readers aware of the beliefs and assumptions that a scholar may be bringing into the study. Describing one’s positionality can be particularly beneficial within health professions education (HPE) research, where complex power dynamics often exist between the researcher and participants. However, there is currently limited literature on how positionality statements should be written for HPE research. This paper aims to present an introductory guide on how a novice researcher can write a positionality statement within HPE research, including elements such as reflexivity, location and context, power dynamics, and insider–outsider positioning, as well as the potential risks tied to positionality statements. Additionally, this paper provides readers with a practical step-by-step guide and a real-life example to follow when developing their own positionality statement. By practicing reflexivity and considering how their location and presence impact the research process and participants, a scholar can articulate how their identity and context shape their research approach. Consequently, adding a positionality statement can increase trustworthiness and enhance transparency of a study when they are reflexive, context-specific, ethically constructed and integrated into the research process. Full article

Research on age segregation, defined as the spatial separation between older adults (65+) and younger populations, has lagged behind demographic shifts in the United States (US). Existing studies are often outdated, cross-sectional, and narrowly focused on metropolitan areas, with few examining race-specific patterns or structural drivers over time at the national scale. This exploratory study addresses these gaps by analyzing longitudinal trends and determinants of age segregation in US counties from 2005 to 2024, with attention to racial variation (White, Black, and Other). Using county-level data from the American Community Survey and fixed-effects regression models, we estimate how demographic, socioeconomic, industrial, and housing factors relate to changes in age segregation. Results indicate that overall age segregation declined in the mid-2010s but increased in recent years, which may reflect the responses to the economic recession in the late 2000s and baby boomers’ aging. Race-specific analyses show that age segregation is highest among Black populations, with contributing factors largely similar across groups but more pronounced for Black communities. Racial/ethnic diversity is consistently associated with lower age segregation, suggesting that demographic heterogeneity fosters intergenerational integration. Employment in secondary industries (e.g., construction and manufacturing) is positively associated with age segregation, particularly among Black populations, whereas housing market characteristics emerge as strong predictors of both overall and race-specific age segregation. These findings underscore the importance of considering race-specific and structural factors in understanding age segregation as the US population continues to age. Full article

For single-motor Formula Student Electric China (FSEC) race cars, current powertrain design methodologies commonly suffer from the disconnection among parameter matching, dynamic simulation, and structural optimization: gear ratio selection is mostly based on static theoretical calculations, lightweight design does not incorporate full-vehicle dynamic load spectra constraints, and the simulation toolchain (CarSim 2024, OptimumLap version 5, ANSYS 2022) lacks a standardized data closed-loop, leading to prolonged iteration cycles and unquantifiable reliability. To address these issues, this paper takes the Nanning University electric formula race car E66 as the research object and proposes a three-phase integrated design framework of “requirement-driven, multi-simulation co-validation, and lightweight iteration.” The study includes three core contributions: (1) establishing a powertrain parameter matching method based on power boundary calculations and multi-dimensional selection criteria, achieving the integrated selection of the Emrax 228 motor (power density 9.2 kW/kg, Emrax d.o.o., Kamnik, Slovenia) and the Unitek-D3 controller through comparative analysis with the JJE motor (5.7 kW/kg, Jing-Jin Electric Technologies Co., Ltd., Beijing, China); (2) constructing a co-simulation mechanism combining OptimumLap version 5 and CarSim 2024, completing the closed-loop optimization of the gear ratio from the range of 1.6–4.3 to the optimal value of 3.9 under the Hefei NIO track operating conditions, with a 75 m acceleration simulation result of 4.4 s and an endurance lap time of 86 s; (3) introducing ANSYS 2022 topology optimization technology to perform two-iteration lightweight design on the 7075 aluminum alloy main sprocket, achieving 35% mass reduction and 40% volume reduction while maintaining the maximum principal stress at 73.16 MPa (below yield strength). The expected outcome is a replicable development paradigm for single-motor powertrain systems, transforming drivetrain matching from experience-driven to simulation-driven, providing reliable data boundaries for physical vehicle commissioning, and effectively reducing trial-and-error costs. Full article

Heavy background noise inevitably masks the weak fault characteristics of low-speed bearings in offshore wind turbines, severely degrading the fault detection accuracy of conventional spectral amplitude modulation. To address this challenging issue, a novel hybrid diagnosis method integrating fast spectral coherence and spectral amplitude modulation is proposed for low-speed bearing fault detection. Firstly, spectral amplitude modulation is implemented on raw vibration signals to construct modified signal components. Subsequently, the fast spectral coherence algorithm is employed to eliminate complex noise interference and reconstruct high-quality enhanced envelope spectra. Further squaring transformation and amplitude normalization operations are conducted to effectively amplify incipient and weak fault signatures buried in noisy signals. Multiple visual analysis strategies are also adopted to intuitively present the fault diagnosis results. The effectiveness and superiority of the proposed method are comprehensively validated by simulated signals with varying signal-to-noise ratios and experimental data of bearing inner and outer race faults under a low rotating speed of 60 RPM. The results demonstrate that the proposed hybrid method significantly remedies the inherent limitations of traditional diagnosis methods, providing a robust and reliable technical solution for incipient fault monitoring and health assessment of low-speed bearings operating in harsh offshore wind field environments. Full article

Background/Objectives: Environmental exposures may contribute to heterogeneity in cognitive aging, yet population-scale datasets integrating exposure biomarkers with cognitive testing remain underused. We evaluated associations between cognitive test performance and demographic, lifestyle, psychosocial, and pesticide-exposure variables in older U.S. adults. Methods: Using NHANES 2011–2014 data, we analyzed adults aged ≥60 years with complete cognitive assessments, demographic/lifestyle covariates, Patient Health Questionnaire-9 scores, binge drinking status, and urinary concentrations of eight pesticide biomarkers. Least absolute shrinkage and selection operator (LASSO) and ridge regression models were trained to predict CERAD immediate learning composite scores, Animal Fluency, and Digit Symbol Substitution Test (DSST) performance. Models were evaluated using mean absolute error, mean squared error, root mean squared error, and R-squared. Results: The analytic sample included 429 participants. Sex, race/ethnicity, and educational attainment were among the strongest predictors across outcomes. LASSO showed the best overall performance in the full cohort, particularly for DSST (R-squared = 0.4680). DEET and desethyl hydroxy-DEET retained non-zero negative coefficients for DSST, and a reduced model including demographics and these two biomarkers achieved comparable performance (DSST R-squared = 0.475). Age-stratified analyses suggested stronger predictor importance in adults aged 60–69 years. Conclusions: These findings support incorporating exposomic biomarkers alongside sociodemographic factors when modeling cognitive test performance in older adults, while emphasizing that cross-sectional NHANES data cannot establish temporality or causality. Full article