Mesenchymal stromal cells (MSCs) are attractive candidates for treating hepatic disorders given their potential to enhance liver regeneration and function. The paracrine paradigm may be involved in the mechanism of MSC-based therapy, and exosomes (Exo) play an important role in this paracrine activity. Hypoxia significantly improves the effectiveness of MSC transplantation. However, whether hypoxia preconditioned MSCs (Hp-MSCs) can enhance liver regeneration, and whether this enhancement is mediated by Exo, are unknown. In this study, mouse bone marrow-derived MSCs (BM-MSCs) and secreted Exo were injected through the tail vein. We report that Hp-MSCs promote liver regeneration after partial hepatectomy in mice through their secreted exosomes. Interestingly, MSC-Exo were concentrated in liver 6 h after administration and mainly taken up by macrophages, but not hepatocytes. Compared with normoxic MSC-Exo (N-Exo), hypoxic MSC-Exo (Hp-Exo) enhanced M2 macrophage polarization both in vivo and in vitro. Microarray analysis revealed significant enrichment of microRNA (miR)-182-5p in Hp-Exo compared with that in N-Exo. In addition, miR-182-5p knockdown partially abolished the beneficial effect of Hp-Exo. Finally, Hp-MSCderived exosomal miR-182-5p inhibited theprotein expression of forkhead box transcription factor 1 (FOXO1) in macrophages, which inhibited toll-like receptor Research), Grant/Award Number: 2018YFA0109800; China Postdoctoral Science Foundation, Grant/Award Number: 2019M652328 4 (TLR4) expression and subsequently induced an anti-inflammatory response. These results highlight the therapeutic potential of Hp-Exo in liver regeneration and suggest that miR-182-5p from Hp-Exo facilitates macrophage polarization during liver regeneration by modulating the FOXO1/TLR4 signaling pathway.
Purpose
To investigate the expression of corneal epithelium–derived netrin-1 (NTN-1) and its immunoregulatory function in dry eye disease (DED) using a DED mouse model.
Methods
We generated DED mouse models with desiccating stress under scopolamine treatment. RNA sequencing was performed to identify differentially expressed genes (DEGs) in the corneal epithelium of DED mice. NTN-1 expression was analyzed via real-time PCR, immunofluorescence staining, and immunoblotting. The DED mice were then treated with recombinant NTN-1 or neutralizing antibodies to investigate the severity of the disease, dendritic cell (DC) activation, and inflammatory cytokine expression.
Results
A total of 347 DEGs (292 upregulated and 55 downregulated) were identified in the corneal epithelium of DED mice: corneal epithelium–derived NTN-1 expression was significantly decreased in DED mice compared to that in control mice. Topical recombinant NTN-1 application alleviated the severity of the disease, accompanied by restoration of tear secretion and goblet cell density. In addition, NTN-1 decreased the number of DCs, inhibited the activation of the DCs and Th17 cells, and reduced the expression of inflammatory factors in DED mice. In contrast, blocking endogenous NTN-1 activity with an anti–NTN-1 antibody aggravated the disease, enhanced DC activation, and upregulated the inflammatory factors in the conjunctivae of DED mice.
Conclusions
We identified decreased NTN-1 expression in the corneal epithelium of DED mice. Our findings elucidate the role of NTN-1 in alleviating DED and impeding DC activation, thereby indicating its therapeutic potential in suppressing ocular inflammation in DED.
The rapid development of marine ranching in recent years provides a new way of tackling the global food crisis. However, the uncontrolled expansion of coastal aquaculture has raised a series of environmental problems. The fast and accurate detection of raft will facilitate scientific planning and the precise management of coastal aquaculture. A new deep learning-based approach called RaftNet is proposed in this study to extract the coastal raft aquaculture in Sansha Bay using Landsat 8 OLI images accurately. To overcome the issues of turbid water environments and varying raft scales in aquaculture areas, we constructed the RaftNet by modifying the UNet network with dual-channel and residual hybrid dilated convolution blocks to improve the extraction accuracy. Meanwhile, we adopted the well-known semantic segmentation networks (FCN, SegNet, UNet, UNet++, and ResUNet) as the contrastive approaches for the extraction. The results suggested that the proposed RaftNet model achieves the best accuracy with a precision of 84.5%, recall of 88.1%, F1-score of 86.30%, overall accuracy (OA) of 95.7%, and intersection over union (IoU) of 75.9%. We then utilized our RaftNet to accurately extract a raft aquaculture area in Sansha Bay from 2014 to 2018 and quantitatively analyzed the change in the raft area over this period. The results demonstrated that our RaftNet is robust and suitable for the precise extraction of raft aquaculture with varying scales in turbid coastal waters, and the Kappa coefficient and OA can reach as high as 88% and 97%, respectively. Moreover, the proposed RaftNet will unleash a remarkable potential for long time-series and large-scale raft aquaculture mapping.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.