A reliable disease model is critical to the study of specific disease mechanisms as well as for the discovery and development of new drugs. Despite providing crucial insights into the mechanisms of neurodegenerative diseases, translation of this information to develop therapeutics in clinical trials have been unsuccessful. Reprogramming technology to convert adult somatic cells to induced Pluripotent Stem Cells (iPSCs) or directly reprogramming adult somatic cells to induced Neurons (iN), has allowed for the creation of better models to understand the molecular mechanisms and design of new drugs. In recent times, iPSC technology has been commonly used for modeling neurodegenerative diseases and drug discovery. However, several technological challenges have limited the application of iN. As evidence suggests, iN for the modeling of neurodegenerative disorders is advantageous compared to those derived from iPSCs. In this review, we will compare iPSCs and iN models for neurodegenerative diseases and their potential applications in the future.

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The efficacy of chemotherapy for colon cancer is limited due to the development of chemoresistance. MicroRNA (miR)-188-5p is downregulated in various types of cancer. The aim of the present study was to explore the molecular role of miR-188 in oxaliplatin (OXA) resistance. An OXA-resistant colon cancer cell line, SW480/OXA, was used to examine the effects of miR-188-5p on the sensitivity of colon cancer cells to OXA. The target of miR-188-5p was identified using a luciferase assay. Cell cycle distribution was also assessed using flow cytometry. The measurement of p21 protein expression, Hoechst 33342 staining and Annexin V/propidium iodide staining was used to evaluate apoptosis. The expression of miR-188-5p significantly increased in SW480/OXA compared with wild-type SW480 cells. The luciferase assay demonstrated that miR-188-5p inhibited Ras GTPase-activating protein 1 (RASA1; also known as p120/RasGAP) luciferase activity by binding to the 3'-untranslated region of RASA1 mRNA, suggesting that miR-188-5p could target RASA1. In addition, miR-188-5p downregulation or RASA1 overexpression promoted the chemosensitivity of SW480/OXA, as evidenced by increased apoptosis and G 1 /S cell cycle arrest. Moreover, RASA1 silencing abrogated the increase in cell apoptosis induced by the miR-188-5p inhibitor. The findings of the present study suggested that miR-188-5p could enhance colon cancer cell chemosensitivity by promoting the expression of RASA1.

Multi-view learning methods have achieved remarkable results in 3D shape recognition. However, most of them focus on the visual feature extraction and feature aggregation, while viewpoints (spatial positions of virtual cameras) for generating multiple views are often ignored. In this paper, we deeply explore the correlation between viewpoints and shape descriptor, and propose a novel viewpoint-guided prototype learning network (VGP-Net). We introduce a prototype representation for each class, including viewpoint prototype and feature prototype. The viewpoint prototype is the average weight of each viewpoint learned from a small support set via Score Unit, and stored in a weight dictionary. Our VGP model self-adaptively learns the view-wise weights by dynamically assembling with the viewpoint prototypes in weight dictionary and performing element-wise operation via view pooling layer. Under the guidance of viewpoint prototypes, important visual features are enhanced, while those negligible features are eliminated. These refined features are effectively fused to generate compact shape descriptor. All the shape descriptors are clustered in feature embedding space, and the cluster center represents the feature prototype of each class. The classification thus can be performed by searching the nearest distance to feature prototypes. To boost the learning process, we further present a multi-stream regularization mechanism in both feature space and viewpoint space. Extensive experiments demonstrate that our VGP-Net is efficient, and the learned deep features have stronger discrimination ability. Therefore, it can achieve better performance compared to state-of-the-art methods.