Résumé
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease worldwide and macrophage polarization plays an important role in its pathogenesis. However, which molecule regulates macrophage polarization in NAFLD remains unclear. Herein, we showed NAFLD mice exhibited increased 17β-hydroxysteroid dehydrogenase type 7 (17β-HSD7) expression in hepatic macrophages concomitantly with elevated M1 polarization. Single-cell RNA sequencing on hepatic non-parenchymal cells isolated from wild-type littermates and macrophage-17β-HSD7 knockout mice fed with high fat diet (HFD) for 6 weeks revealed that lipid metabolism pathways were notably changed. Furthermore, 17β-HSD7 deficiency in macrophages attenuated HFD-induced hepatic steatosis, insulin resistance and liver injury. Mechanistically, 17β-HSD7 triggered NLRP3 inflammasome activation by increasing free cholesterol content, thereby promoting M1 polarization of macrophages and the secretion of pro-inflammatory cytokines. In addition, to help demonstrate that 17β-HSD7 is a potential drug target for NAFLD, fenretinide was screened out from an FDA-approved drug library based on its 17β-HSD7 dehydrogenase inhibitory activity. Fenretinide dose-dependently abrogated macrophage polarization and pro-inflammatory cytokines production, and subsequently inhibited fat deposition in hepatocytes co-cultured with macrophages. In conclusion, our findings suggest that blockade of 17β-HSD7 signaling by fenretinide would be a drug repurposing strategy for NAFLD treatment.
Résumé
Transfer learning is provided with potential research value and application prospect in motor imagery electroencephalography (MI-EEG)-based brain-computer interface (BCI) rehabilitation system, and the source domain classification model and transfer strategy are the two important aspects that directly affect the performance and transfer efficiency of the target domain model. Therefore, we propose a parameter transfer learning method based on shallow visual geometry group network (PTL-sVGG). First, Pearson correlation coefficient is used to screen the subjects of the source domain, and the short-time Fourier transform is performed on the MI-EEG data of each selected subject to acquire the time-frequency spectrogram images (TFSI). Then, the architecture of VGG-16 is simplified and the block design is carried out, and the modified sVGG model is pre-trained with TFSI of source domain. Furthermore, a block-based frozen-fine-tuning transfer strategy is designed to quickly find and freeze the block with the greatest contribution to sVGG model, and the remaining blocks are fine-tuned by using TFSI of target subjects to obtain the target domain classification model. Extensive experiments are conducted based on public MI-EEG datasets, the average recognition rate and Kappa value of PTL-sVGG are 94.9% and 0.898, respectively. The results show that the subjects' optimization is beneficial to improve the model performance in source domain, and the block-based transfer strategy can enhance the transfer efficiency, realizing the rapid and effective transfer of model parameters across subjects on the datasets with different number of channels. It is beneficial to reduce the calibration time of BCI system, which promote the application of BCI technology in rehabilitation engineering.