Aging is a physiological condition accomplished with persistent low-grade inflammation and metabolic disorders. FGF21 has been reported to act as a potent longevity determinant, involving inflammatory response and energy metabolism. In this study, we engineered aging FGF21 knockout mice of 36-40 weeks and observed that FGF21 deficiency manifests a spontaneous inflammatory response of lung and abnormal accumulation of lipids in liver. On one hand, inflamed state in lungs and increased circulating inflammatory cytokines were found in FGF21 knockout mice of 36-40 weeks. To evaluate the ability of FGF21 to suppress inflammation, a subsequent study found that FGF21 knockout aggravated LPS-induced pulmonary exudation and inflammatory infiltration in mice, while exogenous administration of FGF21 reversed these malignant phenotypes by enhancing microvascular endothelial junction. On the other hand, FGF21 knockout induces fatty liver in aging mice, characterized by excessive accumulation of triglycerides within hepatocytes. Further quantitative metabolomics and lipidomics analysis revealed perturbed metabolic profile in liver lacking FGF21, including disrupted glucose and lipids metabolism, glycerophospholipid metabolism, and amino acid metabolism. Taken together, this investigation reveals the protective role of FGF21 during aging by weakening the inflammatory response and balancing energy metabolism.
Introduction: The aim of this work was to determine the feasibility of using a deep learning approach to predict occult lymph node metastasis (OLM) based on preoperative FDG-PET/CT images in patients with clinical node-negative (cN0) lung adenocarcinoma. Materials and Methods: Dataset 1 (for training and internal validation) included 376 consecutive patients with cN0 lung adenocarcinoma from our hospital between May 2012 and May 2021. Dataset 2 (for prospective test) used 58 consecutive patients with cN0 lung adenocarcinoma from June 2021 to February 2022 at the same center. Three deep learning models: PET alone, CT alone, and combined model, were developed for the prediction of OLM. The performance of the models was evaluated on internal validation and prospective test in terms of accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curve (AUCs). Results: The combined model incorporating PET and CT showed the best performance, achieved an AUC of 0.81 [95% confidence interval (CI): 0.61, 1.00] in the prediction of OLM in internal validation set (n = 60) and an AUC of 0.87 (95% CI: 0.75, 0.99) in the prospective test set (n = 58). The model achieved 87.50% sensitivity, 80.00% specificity, and 81.00% accuracy in the internal validation set and achieved 75.00% sensitivity, 88.46% specificity, and 86.60% accuracy in the prospective test set. Conclusion: This study presented a deep learning approach to enable the prediction of occult nodal involvement based on the PET/CT images before surgery in cN0 lung adenocarcinoma, which would help clinicians select patients who would be suitable for sublobar resection.
