ALT Critical Role of Skin in Pathogenesis: Bidirectional Crosstalk Between Skin and Multiple Organs

ALT Schematic illustration of SC-Exos-based nanotherapeutics for inflammatory diseases. SC-Exos can influence immune cells such as macrophages and T lymphocytes, inhibit inflammatory signaling pathways, and suppress the production of pro-inflammatory cytokines, thereby alleviating various inflammatory diseases. Studies have been conducted in a range of diseases, including periodontitis, osteoarthritis, inflammatory bowel disease, and metabolic diseases. Furthermore, the acquisition, characterization, and engineering technologies of SC-Exos have laid the foundation for the mechanistic research and clinical trials of SC-Exos. Created in https://BioRender.com. NALFD, nonalcoholic fatty liver disease; NF-κB, nuclear factor-kappa B.

ALT Integrating AI in medical education enhances teaching but requires careful oversight. While AI enables personalized materials and up-to-date curricula, collaboration between students and educators is essential to ensure quality and accuracy. Overreliance and academic integrity remain concerns, making honesty and accountability essential in training. Institutions should train educators, promote transparency, and emphasize AI as a complement, not a replacement.

ALT The metabolic alteration in vancomycin-intermediate Staphylococcus aureus (VISA) is associated with ccpA upregulation. WalKR and GraSR TCSs control the expression of CcpA, which orchestrates GlmS to form a CcpA-GlmS cascade to drive carbon and nitrogen flow for the biosynthesis of cell wall precursor in VISA. Inhibition of CcpA-GlmS cascade is a promise for the therapeutic efficiency of vancomycin against VISA infections.

ALT Platelets are crucial in cancer progression and immune response, impacting tumor metastasis and diagnosis while offering potential targets for antitumor therapies. This review provides a multidimensional perspective and reference for platelet-related cancer treatment strategies and diagnosis.

ALT This review provides an overview of traditional and modern methods for protein structure prediction and their characteristics and introduces the groundbreaking network features of the AlphaFold family model. We summarize the applications of different bioinformatics databases and various models based on deep learning architectures (convolutional neural network, recurrent neural network, Transformer, etc.) in this field, which may provide directions for future development of predictive models such as prediction of the structure of large protein complexes and integration of protein language models.