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Densely Packed and Well-Aligned Liquid-Crystalline Scaffolds Drive Controllable Axial Crystal Strain
Claudin-2 extracellular loop 1-mimetic peptides functionally inhibit combined hepatocellular-cholangiocarcinoma cells
Abstract Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare and aggressive liver cancer for which effective targeted therapies have not been established. We analyzed claudin expression in cHCC-CCA cell lines (KMCH-1 and KMCH-2) and found that claudin-2 was markedly overexpressed relative to hepatocellular carcinoma and cholangiocarcinoma cell lines, as well as primary hepatocytes. This upregulation was associated with the Wnt/β-catenin signaling pathway, a major regulator of cell proliferation. Claudin-2 knockdown reduced cell viability in both cHCC-CCA cell lines examined, suggesting that claudin-2 supports viability in these cell line models. To test whether claudin-2 function can be modulated extracellularly, we synthesized peptides mimicking the first extracellular loop (ECL1) of claudin-2. Among them, the C-terminal peptide Ex1C selectively reduced the viability of cHCC-CCA cell lines examined, but not that of other cells lacking claudin-2 overexpression. Ex1C suppressed proliferation without inducing cell death and significantly inhibited adhesion of KMCH-2 and KMCH-1 cells to primary hepatocytes. As Ex1C exhibited homotypic self-association, these effects are consistent with interference with ECL1-dependent claudin-2 interactions. Collectively, our findings identify claudin-2 as a characteristic molecular feature of the examined cHCC-CCA cell lines and support the utility of extracellular loop-mimetic peptides as probes to dissect claudin-dependent regulation of proliferation and cell adhesion.
Reply to ‘Stimulating medicines repurposing in the EU: a pilot project’
Correction: A p.N92K variant of the GTPase RAC3 disrupts cortical neuron migration and axon elongation
Emotionally expressive facial animation driven by EmotionBERT embeddings
ASO sustains microglial neural repair activity
Spectrum, pathobiology, mechanistic insights and diagnostic challenges of post-CAR T cell therapy lymphoproliferative disorders
Food web complexity underlies biodiversity effects on ecosystem functioning
Acid-sensing ion channel 2a assembles with epithelial Na+ channel β and γ subunits to form mechanosensitive ion channels
Overcoming Charge-Carrier Localization in Metal Chalcohalides
Adaptive multi-level graph representation with optimization-aware attention for robust cell association in 5G V2X networks
Abstract Efficient cell association remains a fundamental challenge in fifth-generation (5G) vehicle-to-everything (V2X) systems due to rapid topology changes, heterogeneous deployments, and stringent latency requirements. Conventional learning-based approaches often rely on shallow representations or independent optimization strategies, limiting their adaptability in dense and highly dynamic environments. To address these issues, this study introduces a multi-level graph representation framework that models interactions between vehicles and base stations across hierarchical spatial structures. The proposed approach integrates contextual node embedding with attention-driven graph learning to capture mobility patterns, signal characteristics, and network load dependencies. Additionally, a training-stage optimization mechanism is incorporated to refine attention parameters, improving convergence behavior without increasing inference complexity. The framework is evaluated using a real-world vehicular mobility dataset, demonstrating consistent improvements in association stability, handover reliability, and overall network performance compared with existing deep learning and graph-based methods. Experimental results show gains in accuracy (94.17%) and F1-score (93.93%), indicating enhanced decision robustness under dynamic conditions. Although validation is conducted on an urban dataset, the proposed architecture provides a scalable foundation for adaptive cell selection in next-generation intelligent transportation systems.