YAP in development and disease: Navigating the regulatory landscape from retina to brain.

The distinctive role of Yes-associated protein (YAP) in the nervous system has attracted widespread attention. This comprehensive review strategically uses the retina as a vantage point, embarking on an extensive exploration of YAP's multifaceted impact from the retina to the brain in development and pathology. Initially, we explore the crucial roles of YAP in embryonic and cerebral development. Our focus then shifts to retinal development, examining in detail YAP's regulatory influence on the development of retinal pigment epithelium (RPE) and retinal progenitor cells (RPCs), and its significant effects on the hierarchical structure and functionality of the retina. We also investigate the essential contributions of YAP in maintaining retinal homeostasis, highlighting its precise regulation of retinal cell proliferation and survival. In terms of retinal-related diseases, we explore the epigenetic connections and pathophysiological regulation of YAP in diabetic retinopathy (DR), glaucoma, and proliferative vitreoretinopathy (PVR). Lastly, we broaden our exploration from the retina to the brain, emphasizing the research paradigm of "retina: a window to the brain." Special focus is given to the emerging studies on YAP in brain disorders such as Alzheimer's disease (AD) and Parkinson's disease (PD), underlining its potential therapeutic value in neurodegenerative disorders and neuroinflammation.

Optimized method for segmentation of ancient mural images based on superpixel algorithm.

High-precision segmentation of ancient mural images is the foundation of their digital virtual restoration. However, the complexity of the color appearance of ancient murals makes it difficult to achieve high-precision segmentation when using traditional algorithms directly. To address the current challenges in ancient mural image segmentation, an optimized method based on a superpixel algorithm is proposed in this study. First, the simple linear iterative clustering (SLIC) algorithm is applied to the input mural images to obtain superpixels. Then, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to cluster the superpixels to obtain the initial clustered images. Subsequently, a series of optimized strategies, including (1) merging the small noise superpixels, (2) segmenting and merging the large noise superpixels, (3) merging initial clusters based on color similarity and positional adjacency to obtain the merged regions, and (4) segmenting and merging the color-mixing noisy superpixels in each of the merged regions, are applied to the initial cluster images sequentially. Finally, the optimized segmentation results are obtained. The proposed method is tested and compared with existing methods based on simulated and real mural images. The results show that the proposed method is effective and outperforms the existing methods.

Research on the deep learning-based exposure invariant spectral reconstruction method.

The surface spectral reflectance of an object is the key factor for high-fidelity color reproduction and material analysis, and spectral acquisition is the basis of its applications. Based on the theoretical imaging model of a digital camera, the spectral reflectance of any pixels in the image can be obtained through spectral reconstruction technology. This technology can avoid the application limitations of spectral cameras in open scenarios and obtain high spatial resolution multispectral images. However, the current spectral reconstruction algorithms are sensitive to the exposure variant of the test images. That is, when the exposure of the test image is different from that of the training image, the reconstructed spectral curve of the test object will deviate from the real spectral to varying degrees, which will lead to the spectral data of the target object being accurately reconstructed. This article proposes an optimized method for spectral reconstruction based on data augmentation and attention mechanisms using the current deep learning-based spectral reconstruction framework. The proposed method is exposure invariant and will adapt to the open environment in which the light is easily changed and the illumination is non-uniform. Thus, the robustness and reconstruction accuracy of the spectral reconstruction model in practical applications are improved. The experiments show that the proposed method can accurately reconstruct the shape of the spectral reflectance curve of the test object under different test exposure levels. And the spectral reconstruction error of our method at different exposure levels is significantly lower than that of the existing methods, which verifies the proposed method's effectiveness and superiority.

Platelet-rich plasma attenuates intervertebral disc degeneration via delivering miR-141-3p-containing exosomes.

Oxidative stress mediated apoptotic and pyroptotic cell death contributes to intervertebral disc (IVD) degeneration, and platelet-rich plasma (PRP) exerts protective effects to attenuate IVD degeneration. Hence, the present study aimed to validate this issue and uncover the potential underlying mechanisms. The mice and cellular models for IVD degeneration were established by using puncture method and H2O2 exposure, respectively, and we evidenced that NLRP3-mediated cell pyroptosis, apoptosis and inflammatory responses occurred during IVD degeneration progression in vitro and in vivo. Then, the PRP-derived exosomes (PRP-exo) were isolated and purified, and we noticed that both PRP-exo and ROS scavenger (NAC) reversed the detrimental effects of H2O2 treatment on the nucleus pulposus (NP) cells. Further results supported that PRP-exo exerted its protective effects on H2O2 treated NP cells by modulating the Keap1-Nrf2 pathway. Mechanistically, PRP-exo downregulated Keap1, resulting in the release of Nrf2 from the Keap1-Nrf2 complex, which further translocated from cytoplasm to nucleus to achieve its anti-oxidant biological functions, and H2O2 treated NP cells with Nrf2-deficiency did not respond to PRP-exo treatment. In addition, miR-141-3p was enriched in PRP-exo, and miR-141-3p targeted the 3' untranslated region (3'UTR) of Keap1 mRNA for its degradation, leading to Nrf2 translocation. Furthermore, overexpression of miR-141-3p ameliorated the cytotoxic effects of H2O2 on NP cells, which were abrogated by upregulating Keap1 and silencing Nrf2. Taken together, we concluded that PRP secreted exosomal miR-141-3p to activate the Keap1-Nrf2 pathway, which helped to slow down IVD degeneration.

Downregulation of lncRNA SNHG12 reversed IGF1R-induced osteosarcoma metastasis and proliferation by targeting miR-195-5p.

Long non-coding RNA SNHG12 (lncSNHG12) plays important roles in the onset and progression of various cancers. However, the role of lncSNHG12 in osteosarcoma (OS) remains unclear. Therefore, the aim of the present study was to determine the function of lncSNHG12 in OS. A bioinformatics website was used to predict the downstream targets of lncSNHG12. In addition, qRT-PCR was employed to assess lncSNHG12 expression in OS cells. Cell migration and proliferation in vitro were verified using the transwell migration, clone formation, and CCK8 assays. Tumor metastasis and xenograft formation were monitored in nude mice with or without downregulation of lncSNHG12. The results show that lncSNHG12 was upregulated in OS cell lines. Downregulation lncSNHG12 suppressed the metastasis and proliferation both in vitro and in vivo. Also, lncSNHG12 downregulation suppressed the expression of insulin growth factor 1 receptor (IGF1R) expression through sponging miR-195-5p, which was verified with the luciferase reporter assay and rescue experiments. These findings suggest that downregulation of lncSNHG12 may suppress aggressive OS phenotypes. Moreover, lncSNHG12 silencing inhibited OS metastasis and growth by targeting the miR-195-5p/IGF1R axis, which represents a candidate marker and target for OS treatment and management.