Stroke is the second leading cause of death and the third leading cause of disability worldwide, with global economic costs exceeding US $890 billion annually and a projected 50% rise in deaths between 2020 and 2050. So far, stroke has been addressed within the World Health Organization (WHO) Global Noncommunicable Disease (NCD) Action Plan 2023-2030 and the Intersectoral Global Action Plan on Epilepsy and Other Neurological Disorders 2022-2031, but until now, no World Health Assembly resolution has been specifically dedicated to stroke. To present the first WHO Resolution specifically dedicated to stroke (EB158/CONF./9), describe its content and significance, and analyze its implications for global public health. This article presents and analyzes resolution EB158/CONF./9-"Reducing the burden of stroke: strengthening prevention, acute care, rehabilitation and health-system readiness", as advanced by the WHO Executive Board at its 158th session on 3 February 2026 and adopted by the Member States in the Seventy-ninth World Health Assembly.Key content of the resolution:In February 2026, the 158th session of the WHO Executive Board advanced the first WHO resolution dedicated to stroke, proposed by Egypt and co-sponsored by Chile, Georgia, Palestine, Paraguay, and Tunisia, and adopted by Member States at the Seventy-ninth World Health Assembly. The resolution frames stroke as a continuum of care and sets out 16 operative actions for Member States (OP1), organized under three categories: (1) primary interventions across the prevention spectrum; (2) acute care and rehabilitation; and (3) surveillance and data; all underpinned by a health-system strengthening approach. A further eight requests to the Director-General (OP2) provide WHO's mandate to act, covering normative guidance, technical support, monitoring, research, medicine access, regional cooperation, and progress reporting.Public health implication:The resolution establishes a comprehensive stroke action plan across the full continuum of care. Its legacy will depend not on its adoption in Geneva but on whether political commitment is matched by sustained implementation, investment, robust monitoring, and accountability grounded in stroke-specific indicators.

Fostering collaborative creativity and ensemble skills through virtual ensembles in hybrid music education: A systematic literature review.

Ozone-mediated microbial succession and kinetic for refrigerated grass carp (Ctenopharyngodon idella) fillets.

DiffMCG: A diffusion model with mask-conditioned guiding module for medical image classification.

Wetland ecosystems have suffered from prolonged and severe global degradation. In recent years, wetland restoration has made significant contributions to addressing this issue. However, restoration interventions have induced complex and dynamic shifts in plant community composition. Conventional remote sensing approaches often fail to achieve fine-scale monitoring of wetland plant restoration when access to high-cost remote sensing data, such as hyperspectral imagery, is limited. To address this challenge, this study proposes a fine-scale emergent plant mapping method that integrates spatiotemporal-spectral fusion for resolution enhancement with a transformer-based classifier utilizing high-dimensional features. The study employs a spatiotemporal-spectral fusion model, TemPanSharpening net, to improve the spatial resolution of long-term multispectral image sequences. Subsequently, multiple spectral features are selected and conveyed to a Transformer variant classification model. This approach is applied to map 2 m resolution annual dynamics of emergent plant communities in the Honghu Lake South, China. Compared to conventional approaches, our method significantly enhances mapping granularity with an overall accuracy of 88.21%, and reveals that 9.5% of the carbon storage might be overlooked. This research overcomes the limitations of fine-scale emergent plant monitoring under constrained imaging conditions. It provides technical support for accurately monitoring the effectiveness of wetland plant restoration.

Construction of a scaffold-mediated dual-enzyme display system in Komagataella phaffii with immobilized cells for continuous biocatalysis.

Undergraduate nursing students' perceptions of using holopatient in learning and clinical training: An exploratory-descriptive qualitative study.

Explainable deep learning combined with SERS for simultaneous detection of nicotine and etomidate isomers in E-cigarettes.

The rapid development of recombinase polymerase amplification (RPA) has driven a growing demand for UvsX, the key DNA recombinase for this technique. During bioreactor cultivation for UvsX production inEscherichia coli, biomass and substrate are critical factors affecting its efficient expression. However, the real-time monitoring techniques for these parameters remain immature, leading to delayed process control,hindering stable high-level production and precise process optimization. Using an online Raman spectrometer, this study developed synchronous quantitative prediction models for critical process parameters by partial least squares (PLS). Robust multivariate regression models showed strong correlations between predicted and measured values for biomass (R2 = 0.994), glucose (R2 = 0.979), UvsX (R2 = 0.989), and total product (R2 = 0.983). Based on these predictive models, the study implemented online monitoring and feedback control of critical fermentation parameters, successfully raising UvsX and total product titer to 8.23g/L and 9.17g/L, representing increases of 33.17% and 27.54% compared with conventional strategies. This study achieved a marked improvement in UvsX titer and production efficiency by applying Raman spectroscopy as a simple and rapid tool for process monitoring and optimization, providing dependable technical support for scaling up and quality control of complex recombinant proteins.

A novel anomaly detection method for concrete dam measured data based on an improved MemAE model

Development of Micro-VNIR system and applications for lunar meteorites.

Existing forest smoke detection models face limitations in recognizing small targets, achieving real-time performance, and maintaining high inference efficiency on edge devices. To overcome these challenges, this study proposes a novel lightweight multi-scale You Only Look Once (LM-YOLO) model that enhances detection accuracy and real-time capability while ensuring computational efficiency. The LM-YOLO integrates the backbone feature extraction and multi-scale fusion mechanisms of YOLOv12n. Specifically, we introduce the RFMBlock to enhance the fused representation of shallow and deep features, considering the visual characteristics of forest smoke. Meanwhile, the two-path downsampling module achieves efficient downsampling with minimal spatial information loss. To further improve localization accuracy for small and blurred smoke regions, we employ the Shape-IoU loss function. Extensive experiments on the public try123-v4 forest fire smoke dataset demonstrate that, compared with the baseline YOLOv12n under the same experimental conditions, LM-YOLO reduces the number of parameters by 34.5%, decreases the computational cost by 30.2%, and improves detection precision by 4.7%. In addition, the model achieves 92.7% precision on the public try7 dataset. These results outperform existing methods and highlight the strong adaptability of LM-YOLO for edge deployment, ultimately providing reliable technical support for early forest fire warning using unmanned aerialvehicles.

From biosensing to herbal discovery: A nanozyme cascade platform for acetylcholinesterase monitoring and inhibitor screening in synthetic and natural sources.

The International Agency for Research on Cancer: from global evidence to national action.

Hollow Sn-doped ZnO nanotubes for synergistically enhanced sensing performance toward 3-hydroxy-2-butanone with preliminary exploration of a multi-parameter intelligent system.

Multi-omics unravels synergistic mechanism of mixed-strain fermentation by Pediococcus acidilactici and Staphylococcus xylosus for quality and flavor improvement in low-salt yellowfin tuna dark meat fish sauce.

Integrated microfluidic biochip with electrolytic lysis module for rapid detection of Escherichia coli.

Autonomous driving accelerated evaluation method for independent/dependent variables based on importance sampling.

Activation mechanism and performance of full solid-waste cementitious materials using modified concrete waste slurry as an activator

A novel bacterium with heterotrophic nitrification-aerobic denitrification (HN-AD) capability, designated Pseudomonas sp. L5, was isolated from the activated sludge of an aerobic denitrification reactor in our laboratory and subsequently identified. This strain can efficiently conduct nitrogen metabolism using NH4 +-N, NO3 --N and NO2 --N as nitrogen sources. Single-factor experiments combined with response surface methodology (RSM) were used to optimize the culture conditions of strain L5. The predicted optimal conditions for growth and nitrogen removal were a C/N ratio of 31.29, sodium acetate as the carbon source, a temperature of 31.18°C, and an initial pH of 7.25. Notably, even under mildly alkaline conditions (pH 9.0), the strain maintained high aerobic denitrification efficiency, with a NO3 --N removal rate of 86.236%. The nitrogen metabolism pathway of strain L5 was elucidated through a combined analysis of the expression of key HN-AD genes and enzyme activities. The denitrification capacity of strain L5 under alkaline conditions provides a theoretical basis and technical support for the application of aerobic denitrifying bacteria in high-pH wastewater treatment.