Aldehyde Dehydrogenase 1 in Gastric Cancer.

Gastric cancer (GC) is a disease that threatens human health. It is thus crucial to clarify the mechanisms involved in GC development and discover diagnostic biomarkers and therapeutics. As a cancer stem cell marker, aldehyde dehydrogenase 1 (ALDH1) is involved in the development, progression, and treatment of GC.

Mechanism and Disease Association With a Ubiquitin Conjugating E2 Enzyme: UBE2L3.

Ubiquitin conjugating enzyme E2 is an important component of the post-translational protein ubiquitination pathway, which mediates the transfer of activated ubiquitin to substrate proteins. UBE2L3, also called UBcH7, is one of many E2 ubiquitin conjugating enzymes that participate in the ubiquitination of many substrate proteins and regulate many signaling pathways, such as the NF-κB, GSK3β/p65, and DSB repair pathways. Studies on UBE2L3 have found that it has an abnormal expression in many diseases, mainly immune diseases, tumors and Parkinson's disease.

Molecular mechanism of -induced autophagy in gastric cancer.

() is a gram-negative pathogen that colonizes gastric epithelial cells. The drug resistance rates of have dramatically increased, causing persistent infections. Chronic infection by is a critical cause of gastritis, peptic ulcers and even gastric cancer.

Prediction of progression of chronic atrophic gastritis with Helicobacter pylori and poor prognosis of gastric cancer by CYP3A4.

Background And Aim: It has been well documented that Helicobacter pylori (H. pylori) infection is a risk factor for aggravating gastric mucosal atrophy. However, the exact molecular mechanism mediating this process is not fully elucidated.

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors.

Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests. This framework was used to recognize Chinese sign language subwords using recordings from a pair of portable devices worn on both arms consisting of accelerometers (ACC) and surface electromyography (sEMG) sensors.