Targeted Repair of Spinal Cord Injury Based on miRNA-124-3p-Loaded Mesoporous Silica Camouflaged by Stem Cell Membrane Modified with Rabies Virus Glycoprotein.

Spinal cord injury (SCI) has no effective treatment modalities. It faces a significant global therapeutical challenge, given its features of poor axon regeneration, progressive local inflammation, and inefficient systemic drug delivery due to the blood-spinal cord barrier (BSCB). To address these challenges, a new nano complex that achieves targeted drug delivery to the damaged spinal cord is proposed, which contains a mesoporous silica nanoparticle core loaded with microRNA and a cloaking layer of human umbilical cord mesenchymal stem cell membrane modified with rabies virus glycoprotein (RVG). The nano complex more readily crosses the damaged BSCB with its exosome-resembling properties, including appropriate size and a low-immunogenic cell membrane disguise and accumulates in the injury center because of RVG, where it releases abundant microRNAs to elicit axon sprouting and rehabilitate the inflammatory microenvironment. Culturing with nano complexes promotes axonal growth in neurons and M2 polarization in microglia. Furthermore, it showed that SCI mice treated with this nano complex by tail vein injection display significant improvement in axon regrowth, microenvironment regulation, and functional restoration. The efficacy and biocompatibility of the targeted delivery of microRNA by nano complexes demonstrate their immense potential as a noninvasive treatment for SCI.

SiSGC: A Drug Repositioning Prediction Model Based on Heterogeneous Simplifying Graph Convolution.

Drug repositioning plays a key role in disease treatment. With the large-scale chemical data increasing, many computational methods are utilized for drug-disease association prediction. However, most of the existing models neglect the positive influence of non-Euclidean data and multisource information, and there is still a critical issue for graph neural networks regarding how to set the feature diffuse distance. To solve the problems, we proposed SiSGC, which makes full use of the biological knowledge information as initial features and learns the structure information from the constructed heterogeneous graph with the adaptive selection of the information diffuse distance. Then, the structural features are fused with the denoised similarity information and fed to the advanced classifier of CatBoost to make predictions. Three different data sets are used to confirm the robustness and generalization of SiSGC under two splitting strategies. Experiment results demonstrate that the proposed model achieves superior performance compared with the six leading methods and four variants. Our case study on breast neoplasms further indicates that SiSGC is trustworthy and robust yet simple. We also present four drugs for breast cancer treatment with high confidence and further give an explanation for demonstrating the rationality. There is no doubt that SiSGC can be used as a beneficial supplement for drug repositioning.

Enhanced Horizontal Dipole Orientation by Novel Penta-Helicene Anthracene-Based Host for Efficient Blue Fluorescent OLEDs.

Due to the relatively low photoluminescence quantum yield (PLQY) and horizontal dipole orientation of doped films, anthracene-based fluorescent organic light-emitting diodes (F-OLEDs) have faced a great challenge to achieve high external quantum efficiency (EQE). Herein, a novel approach is introduced by incorporating penta-helicene into anthracene, presented as linear-shaped 3-(4-(10-phenylanthracen-9-yl)phenyl)dibenzo[c,g]phenanthrene (BABH) and 3-(4-(10-(naphthalen-2-yl)anthracen-9-yl)phenyl)dibenzo[c,g]phenanthrene (NABH). These blue hosts exhibit minimal intermolecular overlap of π-π stacking, effectively suppressing excimer formation, which facilitates the effective transfer of singlet energy to the fluorescent dopant for PLQY as high as 90%. Additionally, the as-obtained two hosts of BABH and NABH have effectively demonstrated major horizontal components transition dipole moments (TDM) and high thermal stability with glass transitional temperature (Tg ) surpassing 188 °C, enhancing the horizontal dipole orientation of their doped films to be 89% and 93%, respectively. The OLEDs based on BABH and NABH exhibit excellent EQE of 10.5% and 12.4% at 462 nm and device lifetime up to 90% of the initial luminance over 4500 h at 100 cd m-2 , which has firmly established them as among the most efficient blue F-OLEDs based on anthracene to date to the best knowledge. This work provides an instructive strategy to design an effective host for highly efficient and stable F-OLEDs.

Nasopharyngeal carcinoma with non-squamous phenotype may be a variant of nasopharyngeal squamous cell carcinoma after inhibition of EGFR/PI3K/AKT/mTOR pathway.

Nasopharyngeal carcinoma (NPC) is a cancerous tumor that develops in the nasopharynx epithelium and typically has squamous differentiation. The squamous phenotype is evident in immunohistochemistry, with diffuse nuclear positivity for p63 and p40. Nonetheless, a few NPCs have been identified by clinicopathological diagnosis that do not exhibit the squamous phenotype; these NPCs are currently referred to as non-squamous immunophenotype nasopharyngeal carcinomas (NSNPCs). In a previous work, we have revealed similarities between the histological appearance, etiology, and gene alterations of NSNPC and conventional NPC. According to ultrastructural findings, NSNPC still falls under the category of non-keratinized squamous cell carcinoma that is undifferentiated. NSNPC has an excellent prognosis and a low level of malignancy, according to a retrospective investigation. Based on prior research, we investigated the molecular mechanism of NSNPC not expressing the squamous phenotype and its biological behavior. IHC was used to determine the expression of EGFR, PI3K, AKT, p-AKT, mTOR, p-mTOR, Notch, STAT3 and p-STAT3 in a total of 20 NSNPC tissue samples and 20 classic NPC tissue samples. We obtained human NPC cell lines (CNE-2,5-8F) and used EGFR overexpression plasmid and shRNAs to transfect them. To find out whether mRNA and proteins were expressed in the cells, we used Western blotting and qRT-PCR. Cell biological behavior was discovered using the CCK-8 assay, cell migration assay, and cell invasion assay. EGFR, PI3K, p-AKT and p-mTOR proteins were lowly expressed in NSNPC tissues by immunohistochemistry, compared with classical NPC. In the classical NPC cell lines CNE-2 and 5-8F, overexpression EGFR can up-regulate the expression of p63 through the PI3K/AKT/mTOR pathway, and promote the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. At the same time, knockout of EGFR can down-regulate p63 expression through the PI3K/AKT/mTOR pathway, and inhibit the proliferation, migration, and invasion of nasopharyngeal carcinoma cells. The lack of p63 expression in NSNPC was linked with the inhibition of the EGFR/PI3K/AKT/mTOR pathway, and NSNPC may be a variant of classical NPC.

Multi-omics clustering for cancer subtyping based on latent subspace learning.

The increased availability of high-throughput technologies has enabled biomedical researchers to learn about disease etiology across multiple omics layers, which shows promise for improving cancer subtype identification. Many computational methods have been developed to perform clustering on multi-omics data, however, only a few of them are applicable for partial multi-omics in which some samples lack data in some types of omics. In this study, we propose a novel multi-omics clustering method based on latent sub-space learning (MCLS), which can deal with the missing multi-omics for clustering. We utilize the data with complete omics to construct a latent subspace using PCA-based feature extraction and singular value decomposition (SVD). The data with incomplete multi-omics are then projected to the latent subspace, and spectral clustering is performed to find the clusters. The proposed MCLS method is evaluated on seven different cancer datasets on three levels of omics in both full and partial cases compared to several state-of-the-art methods. The experimental results show that the proposed MCLS method is more efficient and effective than the compared methods for cancer subtype identification in multi-omics data analysis, which provides important references to a comprehensive understanding of cancer and biological mechanisms. AVAILABILITY: The proposed method can be freely accessible at https://github.com/ShangCS/MCLS.

Multiview network embedding for drug-target Interactions prediction by consistent and complementary information preserving.

Accurate prediction of drug-target interactions (DTIs) can reduce the cost and time of drug repositioning and drug discovery. Many current methods integrate information from multiple data sources of drug and target to improve DTIs prediction accuracy. However, these methods do not consider the complex relationship between different data sources. In this study, we propose a novel computational framework, called MccDTI, to predict the potential DTIs by multiview network embedding, which can integrate the heterogenous information of drug and target. MccDTI learns high-quality low-dimensional representations of drug and target by preserving the consistent and complementary information between multiview networks. Then MccDTI adopts matrix completion scheme for DTIs prediction based on drug and target representations. Experimental results on two datasets show that the prediction accuracy of MccDTI outperforms four state-of-the-art methods for DTIs prediction. Moreover, literature verification for DTIs prediction shows that MccDTI can predict the reliable potential DTIs. These results indicate that MccDTI can provide a powerful tool to predict new DTIs and accelerate drug discovery. The code and data are available at: https://github.com/ShangCS/MccDTI.