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Diabetic retinopathy classification using a multi-attention residual refinement architecture
Self-splicing RNA circularization facilitated by intact group I and II introns
Gastrointestinal neuroprosthesis for motility and metabolic neuromodulation
F-actin disassembly by the oxidoreductase MICAL1 promotes mechano-dependent VWF-GPIbα interaction in platelets
Abstract Mechano-dependent interactions are key to thrombus formation and hemostasis, enabling stable platelet adhesion to injured vessels. The interaction between von Willebrand factor (VWF) and the platelet receptor GPIb-IX-V is central to this process. While GPIbα connects to the actin cytoskeleton, whether actin dynamics are important for GPIbα function under hemodynamic, high shear conditions remains largely unknown. Here, we show that actin disassembly is critical for proper VWF-GPIbα binding under shear. Mechanistically, we identify the oxidoreductase MICAL1 as a shear-activated regulator that promotes local F-actin disassembly around the GPIb-IX-V complex. This enables its translocation to lipid rafts and reinforces VWF binding. MICAL1-deficient platelets display impaired adhesion, increased deformability under shear, and defective thrombus formation in vivo. Thus, MICAL1 drives shear-dependent actin remodeling that supports GPIb-IX-V mechanotransduction and platelet function. These findings uncover a role for actin oxidation in platelet adhesion, providing a connection between cytoskeletal redox control and platelet function during thrombus formation.
Synthesis of piperazine-based benzimidazole derivatives as potent urease inhibitors and molecular docking studies
The ocular shape and retinal structure in children with a history of treated retinopathy of prematurity
A dilemma study of the traffic flow system emerged due to the lane-change by follower’s tailgating effect
Research on temporal-spatial distribution differences and formation mechanisms of NPP in the Lanzhou section of the yellow river mainstream
Natural language processing reveals network structure of pain communication in social media using discrete mathematical analysis
Associations of maternal neighborhood and trauma-related stressors with mitochondrial DNA copy number and telomere length in maternal and cord blood
The impact of predation pressure, natural light, and species-specific factors on the prevalence and intensity of nocturnal singing by diurnal birds
Vehicle-to-everything decision optimization and cloud control based on deep reinforcement learning
Abstract To address the challenges of decision optimization and road segment hazard assessment within complex traffic environments, and to enhance the safety and responsiveness of autonomous driving, a Vehicle-to-Everything (V2X) decision framework is proposed. This framework is structured into three modules: vehicle perception, decision-making, and execution. The vehicle perception module integrates sensor fusion techniques to capture real-time environmental data, employing deep neural networks to extract essential information. In the decision-making module, deep reinforcement learning algorithms are applied to optimize decision processes by maximizing expected rewards. Meanwhile, the road segment hazard classification module, utilizing both historical traffic data and real-time perception information, adopts a hazard evaluation model to classify road conditions automatically, providing real-time feedback to guide vehicle decision-making. Furthermore, an autonomous driving cloud control platform is designed, augmenting decision-making capabilities through centralized computing resources, enabling large-scale data analysis, and facilitating collaborative optimization. Experimental evaluations conducted within simulation environments and utilizing the KITTI dataset demonstrate that the proposed V2X decision optimization method substantially outperforms conventional decision algorithms. Vehicle decision accuracy increased by 9.0%, rising from 89.2 to 98.2%. Additionally, the response time of the cloud control system decreased from 178 ms to 127 ms, marking a reduction of 28.7%, which significantly enhances decision efficiency and real-time performance. The introduction of the road segment hazard classification model also results in a hazard assessment accuracy of 99.5%, maintaining over 95% accuracy even in high-density traffic and complex road conditions, thus illustrating strong adaptability. The results highlight the effectiveness of the proposed V2X decision optimization framework and cloud control platform in enhancing the decision quality and safety of autonomous driving systems.
Reliability of biometric devices for measuring hand grip and finger pinch strength in stroke patients over 50: a prospective observational study
Development and selection of stably expressed reference genes for expression normalization in Ribes odoratum under drought stress
DSAT: a dynamic sparse attention transformer for steel surface defect detection with hierarchical feature fusion
Systematic review and meta analysis of mechanical properties of 3D printed denture bases compared to milled and conventional materials
Abstract Denture base fabrication has advanced with the introduction of computer-aided design and manufacturing (CAD-CAM) techniques, such as subtractive milling and additive 3D printing. However, concerns persist regarding the mechanical performance of 3D-printed denture bases. This systematic review and meta-analysis aimed to evaluate and compare the flexural strength (FS), surface hardness, fracture toughness, and impact strength of 3D-printed denture bases with those produced by milling and conventional methods. A systematic search of PubMed, Scopus, Web of Science, and Cochrane Central was conducted up to March 2025 in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In vitro studies comparing 3D-printed denture bases with milled or conventional heat-polymerized bases in terms of mechanical properties were included. The Joanna Briggs Institute (JBI) checklist for quasi-experimental studies was used. Data was extracted, and quantitative synthesis was performed where possible. Thirty-eight studies were included, comprising 562 specimens for FS and 231 for surface hardness. Meta-analysis revealed that milled denture bases demonstrated the highest flexural strength (MD = -1.11, 95% CI [-1.29, -0.93], p < 0.001) and surface hardness (MD = -26.49, 95% CI [-29.89, -23.10], p < 0.001) compared to 3D-printed bases. Conventional bases outperformed 3D-printed ones in most mechanical properties. Milled denture bases exhibited the highest FS (120–146 MPa), followed by conventional PMMA (95–119 MPa), while 3D-printed bases showed wider variability (28–128 MPa). Surface hardness (VHN), fracture toughness (MPa·m¹/²), and impact strength (kJ/m²) were also superior in milled bases. Statistical heterogeneity was present due to differences in materials, printing orientation, and post-curing protocols. Subgroup analysis based on printing orientation (0°, 45°, and 90°) partially explained this variability, showing higher FS in horizontally printed specimens. Although 3D-printed denture bases offer customization and production efficiency, their mechanical properties remain inferior to milled alternatives. Optimization of resin formulations, printing parameters, and post-processing protocols is essential to enhance their clinical performance. The main limitations were high heterogeneity among included studies, differences in material formulations, variability in testing standards, and the in vitro nature of most included studies. This review was registered in PROSPERO (CRD420250639092). There were no deviations from the registered protocol.