The role of miR-26a in cancer cells seemed controversial in previous studies. Until now, the role of miR-26a in gastric cancer remains undefined. In this study, we found that miR-26a was strongly downregulated in gastric cancer (GC) tissues and cell lines, and its expression levels were associated with lymph node metastasis and clinical stage, as well as overall survival and replase-free survival of GC. We also found that ectopic expression of miR-26a inhibited GC cell proliferation and GC metastasis in vitro and in vivo. We further identified a novel mechanism of miR-26a to suppress GC growth and metastasis. FGF9 was proved to be a direct target of miR-26a, using luciferase assay and western blot. FGF9 overexpression in miR-26a-expressing cells could rescue invasion and growth defects of miR-26a. In addition, miR-26a expression inversely correlated with FGF9 protein levels in GC. Taken together, our data suggest that miR-26a functions as a tumor suppressor in GC development and progression, and holds promise as a prognostic biomarker and potential therapeutic target for GC.
The gray level run length matrix (GLRLM) whose entries are statistics recording distribution and relationship of images pixels is a widely used method for extracting statistical features for medical images, e.g., magnetic resonance (MR) images. Recently these features are usually employed in some artificial neural networks to identify and distinguish texture patterns. But GLRLM construction and features extraction are tedious and computationally intensive while the images are too big with high resolution, or there are too many small or intermediate Regions of Interest (ROI) to process in a single image, which makes the preprocess a time consuming stage. Hence, it is of great importance to accelerate the procedure which is nowadays possible with the rapid development of massively parallel Graphics Processing Unit, i.e. the GPU computing technology. In this article, we propose a new paradigm based on mature parallel primitives for generating GLRLMs and extracting multiple features for many ROIs simultaneously in a single image. Experiments show that such a paradigm is easy to implement and offers an acceleration over 5 fold increase in speed than an optimized serial counterpart.
H2 activates ATP-binding cassette transporter A1-dependent efflux, enhances HDL antiatherosclerotic functions, and has beneficial lipid-lowering effects. The present findings highlight the potential role of H2 in the regression of hypercholesterolemia and atherosclerosis.
Background:
The Hirsch-index (h-index) is a measure of academic productivity that incorporates both the quantity and quality of an author’s output. However, it is still affected by self-citation behaviors. This study aims to determine the research output and self-citation rates (SCRs) in the Journal of Medicine (Baltimore), establishing a benchmark for bibliometrics, in addition to identifying significant differences between stages from 2018 to 2021.
Methods:
We searched the PubMed database to obtain 17,912 articles published between 2018 and 2021 in Medicine (Baltimore). Two parts were carried out to conduct this study: the categories were clustered according to the medical subject headings (denoted by midical subject headings [MeSH] terms) using social network analysis; 3 visualizations were used (choropleth map, forest plot, and Sankey diagram) to identify dominant entities (e.g., years, countries, regions, institutes, authors, categories, and document types); 2-way analysis of variance (ANOVA) was performed to differentiate outputs between entities and stages, and the SCR with articles in Medicine (Baltimore) was examined. SCR, as well as the proportion of self-citation (SC) in the previous 2 years in comparison to SC were computed.
Results:
We found that South Korea, Sichuan (China), and Beijing (China) accounted for the majority of articles in Medicine (Baltimore); ten categories were clustered and led by 3 MeSh terms: methods, drug therapy, and complications; and more articles (52%) were in the recent stage (2020–2021); no significant difference in counts was observed between the 2 stages based on the top ten entities using the forest plot (Z = 0.05, P = .962) and 2-way ANOVA (F = 0.09, P = .76); the SCR was 5.69% (<15%); the h-index did not differ between the 2 collections of self-citation inclusion and exclusion; and the SC in the previous 2 years accounted for 70% of the self-citation exclusion.
Conclusion:
By visualizing the characteristics of a given journal, a breakthrough was made. Subject categories can be classified using MeSH terms. Future bibliographical studies are recommended to perform the 2-way ANOVA and then compare the outputs from 2 stages as well as the changes in h-indexes between 2 sets of self-citation inclusion and exclusion.
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