Endoscopic mucosal resection with double band ligation versus endoscopic submucosal dissection for small rectal neuroendocrine tumors.

Background: Endoscopic resection remains an effective method for the treatment of small rectal neuroendocrine tumors (NETs) (≤ 10 mm). Moreover, endoscopic mucosal resection (EMR) with double band ligation (EMR-dB), a simplified modification of EMR with band ligation, is an alternative strategy to remove small rectal NETs.

Aim: To evaluate the feasibility and safety of EMR-dB for the treatment of small rectal NETs (≤ 10 mm).

The influence of family support during endoscopic submucosal dissection on patient's anxiety.

Background: Psychological problems may promote peptic ulcers. Ulcer-like wounds can be formed after gastric endoscopic submucosal dissection (ESD). The influence of family support on the healing of gastric ESD-induced ulcers remains largely undetermined.

Interleukin-22 Ameliorates Dextran Sulfate Sodium-Induced Colitis through the Upregulation of lncRNA-UCL to Accelerate Claudin-1 Expression via Sequestering miR-568 in Mice.

Background: Bioactive compound such as interleukin-22 (IL-22) treatment is regarded as a sufficient treatment for ulcerative colitis (UC). It has been found that long noncoding RNAs (lncRNAs) expressed in many inflammatory diseases, including UC. We aimed to verify the treatment effect of bioactive compounds including IL-22 and lncRNAs in UC on colitis mice.

FL-DTD: an integrated pipeline to predict the drug interacting targets by feedback loop-based network analysis.

Drug target discovery is an essential step to reveal the mechanism of action (MoA) underlying drug therapeutic effects and/or side effects. Most of the approaches are usually labor-intensive while unable to identify the tissue-specific interacting targets, especially the targets with weaker drug binding affinity. In this work, we proposed an integrated pipeline, FL-DTD, to predict the drug interacting targets of novel compounds in a tissue-specific manner.

Module Analysis Using Single-Patient Differential Expression Signatures Improves the Power of Association Studies for Alzheimer's Disease.

The causal mechanism of Alzheimer's disease is extremely complex. Achieving great statistical power in association studies usually requires a large number of samples. In this work, we illustrated a different strategy to identify AD risk genes by clustering AD patients into modules based on their single-patient differential expression signatures.