ABSTRACT
Renal fibrosis is a hallmark of diabetic nephropathy (DN) and is characterized by an epithelial-to-mesenchymal transition (EMT) program and aberrant glycolysis. The underlying mechanisms of renal fibrosis are still poorly understood, and existing treatments are only marginally effective. Therefore, it is crucial to comprehend the pathophysiological mechanisms behind the development of renal fibrosis and to generate novel therapeutic approaches. Acrolein, an α-,ß-unsaturated aldehyde, is endogenously produced during lipid peroxidation. Acrolein shows high reactivity with proteins to form acrolein-protein conjugates (Acr-PCs), resulting in alterations in protein function. In previous research, we found elevated levels of Acr-PCs along with kidney injuries in high-fat diet-streptozotocin (HFD-STZ)-induced DN mice. This study used a proteomic approach with an anti-Acr-PC antibody followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis to identify several acrolein-modified protein targets. Among these protein targets, pyruvate kinase M2 (PKM2) was found to be modified by acrolein at Cys358, leading to the inactivation of PKM2 contributing to the pathogenesis of renal fibrosis through HIF1α accumulation, aberrant glycolysis, and upregulation of EMT in HFD-STZ-induced DN mice. Finally, PKM2 activity and renal fibrosis in DN mice can be reduced by acrolein scavengers such as hydralazine and carnosine. These results imply that acrolein-modified PKM2 contributes to renal fibrosis in the pathogenesis of DN.
ABSTRACT
Objective To establish an artificial intelligence model based on B-mode thyroid ultrasound images to predict central compartment lymph node metastasis(CLNM)in patients with papillary thyroid carcinoma(PTC). Methods We retrieved the clinical manifestations and ultrasound images of the tumors in 309 patients with surgical histologically confirmed PTC and treated in the First Medical Center of PLA General Hospital from January to December in 2018.The datasets were split into the training set and the test set.We established a deep learning-based computer-aided model for the diagnosis of CLNM in patients with PTC and then evaluated the diagnosis performance of this model with the test set. Result The accuracy,sensitivity,specificity,and area under receiver operating characteristic curve of our model for predicting CLNM were 80%,76%,83%,and 0.794,respectively. Conclusion Deep learning-based radiomics can be applied in predicting CLNM in patients with PTC and provide a basis for therapeutic regimen selection in clinical practice.
