Background: Flexible flatfoot impairs gait and posture by weakening arch support, potentially leading to musculoskeletal dysfunction. Strengthening exercises, such as the short foot exercise (SFE), have shown promise in correcting this condition. Objectives: This study aimed to investigate the effects of SFE with visual feedback on medial arch height and foot function in adults with flexible flatfoot. Design: Experimental research. Methods: Adults diagnosed with flexible flatfoot were randomly assigned to either an experimental or control group. The experimental group performed SFE with visual feedback, whereas the control group performed the same exercises without feedback. Both groups trained three times per week for five weeks. Outcome measures included the Navicular Drop Test (NDT), YBalance Test (YBT), and Tetrax postural analysis. Results: In the NDT, both groups showed significant improvements (P<.05), while in the YBT, only the experimental group showed a significant improvement (P<.05). In contrast, there were no significant changes in the Weight Distribution Index (WDI) and Stability Test (ST) areas of the Tetrax system in either group (P>.05). Conclusion: SFE effectively improved arch height regardless of visual feedback, though only the visual feedback group showed significant improvements in dynamic balance. However, between-group differences were not statistically significant, suggesting that visual feedback provides subtle rather than substantial additional benefits. Further research with larger samples is needed to establish the clinical value of adding visual feedback to SFE protocols.

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

Lysophosphatidic acid (LPA) is a bioactive lipid messenger involved in the pathogenesis of chronic inflammation and various diseases. Recent studies have shown an association between periodontitis and neuroinflammatory diseases such as Alzheimer’s disease, stroke, and multiple sclerosis. However, the mechanistic relationship between periodontitis and neuroinflammatory diseases remains unclear. The current study found that lysophosphatidic acid receptors 1 (LPAR1) and 6 (LPAR6) exhibited increased expression in primary microglia and astrocytes. The primary astrocytes were then treated using medium conditioned to mimic periodontitis through addition of Porphyromonas gingivalis lipopolysaccharides, and an increased nitric oxide (NO) production was observed. Application of conditioned medium from human periodontal ligament stem cells with or without LPAR1 knockdown showed a decrease in the production of NO and expression of inducible nitric oxide synthase and interleukin 1 beta. These findings may contribute to our understanding of the mechanistic link between periodontitis and neuroinflammatory diseases.