An FGF2-Derived Short Peptide Attenuates Bleomycin-Induced Pulmonary Fibrosis by Inhibiting Collagen Deposition and Epithelial-Mesenchymal Transition via the FGFR/MAPK Signaling Pathway

Following the COVID-19 pandemic, the prevalence of pulmonary fibrosis has increased significantly, placing patients at higher risk and presenting new therapeutic challenges. Current anti-fibrotic drugs, such as Nintedanib, can slow the decline in lung function, but their severe side effects highlight the urgent need for safer and more targeted alternatives. This study explores the anti-fibrotic potential and underlying mechanisms of an endogenous peptide (P5) derived from fibroblast growth factor 2 (FGF2), developed by our research team. Using a bleomycin-induced pulmonary fibrosis mouse model, we observed that P5 alleviated fibrosis by inhibiting collagen deposition, as confirmed by CT scans and histological staining. In TGF-β-induced cell models, P5 effectively suppressed collagen deposition and epithelial-mesenchymal transition (EMT). Transcriptome analysis highlighted pathways related to receptor binding, extracellular matrix organization, and cell adhesion, with KEGG analysis confirming FGFR/MAPK signaling inhibition as the primary mechanism underlying its anti-fibrotic effects. In summary, our study demonstrates that P5 significantly attenuates pulmonary fibrosis through the inhibition of EMT, collagen deposition, and FGFR/MAPK signaling, providing a promising therapeutic approach for fibrosis.

LBRT: Local-Information-Refined Transformer for Image Copy-Move Forgery Detection

The current deep learning methods for copy-move forgery detection (CMFD) are mostly based on deep convolutional neural networks, which frequently discard a large amount of detail information throughout convolutional feature extraction and have poor long-range information extraction capabilities. The Transformer structure is adept at modeling global context information, but the patch-wise self-attention calculation still neglects the extraction of details in local regions that have been tampered with. A local-information-refined dual-branch network, LBRT (Local Branch Refinement Transformer), is designed in this study. It performs Transformer encoding on the global patches segmented from the image and local patches re-segmented from the global patches using a global modeling branch and a local refinement branch, respectively. The self-attention features from both branches are precisely fused, and the fused feature map is then up-sampled and decoded. Therefore, LBRT considers both global semantic information modeling and local detail information refinement. The experimental results show that LBRT outperforms several state-of-the-art CMFD methods on the USCISI dataset, CASIA CMFD dataset, and DEFACTO CMFD dataset.