Idiopathic pulmonary fibrosis (IPF) is a progressive disease lacking effective therapy. Metformin, an antidiabetic medication, has shown promising therapeutic properties in preclinical fibrosis models; however, its precise cellular targets and associated mechanisms in fibrosis resolution remain incompletely defined. Most research on metformin's effects has focused on mesenchymal and inflammatory responses with limited attention to epithelial cells. In this study, we utilized Sftpc lineage-traced and Fgfr2b conditional knockout mice, along with BMP2/PPARγ and AMPK inhibitors, to explore metformin's impact on alveolar epithelial cells in a bleomycin-induced pulmonary fibrosis model and cell culture. We found that metformin increased the proliferation and differentiation of alveolar type 2 (AT2) cells, particularly the recently identified injury-activated alveolar progenitors (IAAPs)-a subpopulation characterized by low SFTPC expression but enriched for PD-L1. Single-cell RNA sequencing revealed a reduction in apoptosis among mature AT2 cells. Interestingly, metformin's therapeutic effects were not significantly affected by BMP2 or PPARγ inhibition, which blocked the lipogenic differentiation of myofibroblasts. However, Fgfr2b deletion in Sftpc lineage cells significantly impaired metformin's ability to promote fibrosis resolution, a process linked to AMPK signaling. In conclusion, metformin alleviates fibrosis by directly activating AT2 cells, especially the IAAPs, through a mechanism that involves AMPK and FGFR2b signaling, but is largely independent of BMP2/PPARγ pathways.

Objective:To evaluate the feasibility and clinical value of pre-treatment non-enhanced chest CT radiomics features and machine learning algorithm to predict the mutation status and subtype (19Del/21L858R) of epidermal growth factor receptor (EGFR) for patients with non-small cell lung cancer (NSCLC).Methods:This retrospective study enrolled 280 NSCLC patients from first and second affiliated hospital of University of South China who were confirmed by biopsy pathology, gene examination, and have pre-treatment non-enhanced CT scans. There are 136 patients were confirmed EGFR mutation. Primary lung gross tumor volume was contoured by two experienced radiologists and oncologists, and 851 radiomics features were subsequently extracted. Then, spearman correlation analysis and RELIEFF algorithm were used to screen predictive features. The two hospitals were training and validation cohort, respectively. Clinical-radiomics model was constructed using selected radiomics and clinical features, and compared with models built by radiomics features or clinical features respectively. In this study, machine learning models were established using support vector machine (SVM) and a sequential modeling procedure to predict the mutation status and subtype of EGFR. The area under receiver operating curve (AUC-ROC) was employed to evaluate the performances of established models.Results:After feature selection, 21 radiomics features were found to be efffective in predicting EGFR mutation status and subtype and were used to establish radiomics models. Three types models were established, including clinical model, radiomics model, and clinical-radiomics model. The clinical-radiomics model showed the best predictive efficacy, AUCs of predicting EGFR mutation status for training dataset and validation dataset were 0.956 (95% CI: 0.952-1.000) and 0.961 (95% CI: 0.924-0.998), respectively. The AUCs of predicting 19Del/L858R mutation subtype for training dataset and validation dataset were 0.926 (95% CI: 0.893-0.959), 0.938 (95% CI: 0.876-1.000), respectively. Conclusions:The constructed sequential models based on integration of CT radiomics, clinical features and machine learning can accurately predict the mutation status and subtype of EGFR.