BackgroundCircular RNAs(circRNAs) have been reported as a diverse class of endogenous RNA that regulate gene expression in eukaryotes. Recent evidence suggested that many circular RNAs can act as oncogenes or tumor suppressors through sponging microRNAs. However, the function of circular RNAs in gastric cancer remains largely unknown.Materials and methodsThe circRNA levels in gastric carcinoma tissues and plasmas were detected by real-time quantitative reverse transcription-polymerase chain reaction. The correlation between the expression of circRNA and clinic pathological features was analyzed. Rate of inhibiting of proliferation was measured using a CCK-8 cell proliferation assay. Clone formation ability was assessed with a clone formation inhibition test. We used the bioinformatics software to predict circRNA-miRNA and miRNA-mRNA interactions. Relative gene expression was assessed using quantitative real-time polymerase chain reaction and relative protein expression levels were determined with western blotting. CircRNA and miRNA interaction was confirmed by dual-luciferase reporter assays.ResultsWe characterized that one circRNA named circ-SFMBT2 showed an increased expression level in gastric cancer tissues compared to adjacent non-cancerous tissues and was associated with higher tumor stages of gastric cancer. Silencing of circ-SFMBT2 inhibited the proliferation of gastric cancer cells significantly. Importantly, we demonstrated that circ-SFMBT2 could act as a sponge of miR-182-5p to regulate the expression of CREB1 mRNA, named as cAMP response element binding protein 1, and further promote the proliferation of gastric cancer cells.ConclusionOur study reveals that circ-SFMBT2 participates in progression of gastric cancer by competitively sharing miR-182-5p with CREB1, providing a novel target to improve the treatment of gastric cancer. mutation-analysis-of-beta-thalassemia-in-east-western-indian-populatio-peer-reviewed-article-TACG for an example.

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Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be trained directly on full mammogram images because of the loss of image details from resizing at input layers. Instead, our classifiers are trained on labelled image patches and then adapted to work on full mammogram images for localizing the abnormalities. State-of-the-art deep convolutional neural networks are compared on their performance of classifying the abnormalities. Experimental results indicate that VGGNet receives the best overall accuracy at 92.53% in classifications. For localizing abnormalities, ResNet is selected for computing class activation maps because it is ready to be deployed without structural change or further training. Our approach demonstrates that deep convolutional neural network classifiers have remarkable localization capabilities despite no supervision on the location of abnormalities is provided.

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.