D-arabinose induces cell cycle arrest by promoting autophagy via p38 MAPK signaling pathway in breast cancer.

Breast cancer patients often have a poor prognosis largely due to lack of effective targeted therapy. It is now well established that monosaccharide enhances growth retardation and chemotherapy sensitivity in tumor cells. We investigated whether D-arabinose has capability to restrict the proliferation of tumor cells and its mechanism. Here, we report that D-arabinose induced cytotoxicity is modulated by autophagy and p38 MAPK signaling pathway in breast cancer cell lines. The proliferation of cells was evaluated by CCK-8 and Colony formation assay. The distribution of cells in cell cycle phases was analyzed by flow cytometry. Cell cycle, autophagy and MAPK signaling related proteins were detected by western blotting. Mouse xenograft model was used to evaluate the efficacy of D-arabinose in vivo. The proliferation of cells was dramatically inhibited by D-arabinose exposure in a dose-dependent manner, which was relevant to cell cycle arrest, as demonstrated by G2/M cell cycle restriction and ectopic expression of cell cycle related proteins. Mechanistically, we further identified that D-arabinose is positively associated with autophagy and the activation of the p38 MAPK signaling in breast cancer. In contrast, 3-Ma or SB203580, the inhibitor of autophagy or p38 MAPK, reversed the efficacy of D-arabinose. Additionally, D-arabinose in vivo treatment could significantly inhibit xenograft growth of breast cancer cells. Our findings were the first to reveal that D-arabinose triggered cell cycle arrest by inducing autophagy through the activation of p38 MAPK signaling pathway in breast cancer cells.

SH3GL2 and MMP17 as lung adenocarcinoma biomarkers: a machine-learning based approach.

Objective: Using bioinformatics machine learning methods, our research aims to identify the potential key genes associated with Lung adenocarcinoma (LUAD). Methods: We obtained two gene expression profiling microarrays (GSE68571 and GSE74706) from the public Gene Expression Omnibus (GEO) database at the National Centre for Biotechnology Information (NCBI). The purpose was to identify Differentially Expressed Genes (DEGs) between the lung adenocarcinoma group and the healthy control group. The limma R package in R was utilized for this analysis. For the differential gene diagnosis of lung adenocarcinoma, we employed the least absolute shrinkage and selection operator (LASSO) regression and SVM-RFE screening crossover. To evaluate the performance, ROC curves were plotted. We performed immuno-infiltration analysis using CIBERSORT. Finally, we validated the key genes through qRT-PCR and Western-blot verification, then downregulated MMP17 gene expression, upregulated SH3GL2 gene expression, and performed CCK8 experiments. Results: A total of 32 Differentially Expressed Genes (DEGs) were identified. Two diagnostic marker genes, SH3GL2 and MMP17, were selected by employing LASSO and SVM-RFE machine learning methods. In Lung adenocarcinoma cells, the expression of MMP17 was observed to be elevated compared to normal lung epithelial cells in the control group (P < 0.05). In contrast, a down-regulation of SH3GL2 was found in Lung adenocarcinoma cells (P < 0.05). Finally, we downregulated MMP17 and upregulated SH3GL2 gene expression, then the CCK8 showed that the proliferation of both lung cancer cells was inhibited. Conclusion: SH3GL2 and MMP17 are expected to be potential biomarkers for Lung adenocarcinoma.

A Video-Based Augmented Reality System for Human-in-the-Loop Muscle Strength Assessment of Juvenile Dermatomyositis.

As the most common idiopathic inflammatory myopathy in children, juvenile dermatomyositis (JDM) is characterized by skin rashes and muscle weakness. The childhood myositis assessment scale (CMAS) is commonly used to measure the degree of muscle involvement for diagnosis or rehabilitation monitoring. On the one hand, human diagnosis is not scalable and may be subject to personal bias. On the other hand, automatic action quality assessment (AQA) algorithms cannot guarantee 100% accuracy, making them not suitable for biomedical applications. As a solution, we propose a video-based augmented reality system for human-in-the-loop muscle strength assessment of children with JDM. We first propose an AQA algorithm for muscle strength assessment of JDM using contrastive regression trained by a JDM dataset. Our core insight is to visualize the AQA results as a virtual character facilitated by a 3D animation dataset, so that users can compare the real-world patient and the virtual character to understand and verify the AQA results. To allow effective comparisons, we propose a video-based augmented reality system. Given a feed, we adapt computer vision algorithms for scene understanding, evaluate the optimal way of augmenting the virtual character into the scene, and highlight important parts for effective human verification. The experimental results confirm the effectiveness of our AQA algorithm, and the results of the user study demonstrate that humans can more accurately and quickly assess the muscle strength of children using our system.