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, GSK3ß/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. It can also promote the occurrence and development of these diseases. Resultantly, UBE2L3 may become an important target for some diseases. Herein, we review the structure of UBE2L3, and its mechanism in diseases, as well as diseases related to UBE2L3 and discuss the related challenges.

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. This review evaluated the prognostic value of ALDH1 and explored its mechanism of action in GC. Importantly, ALDH1 is an informative biomarker in clinical practice as it has specific relationships with indicators, such as metastasis and overall survival. Additionally, ALDH1 interacts with genes and exhibits properties that mimic stem cell characteristics amongst other mechanisms employed in the occurrence and progression of GC. Our results, therefore, provide evidence of possible clinical utility of ALDH1 as a GC therapeutic target.

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. The purpose of this study was to identify biomarkers, which may predict the risk for progression of chronic atrophic gastritis (CAG) with H. pylori. METHODS: GSE27411 was downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) between H. pylori-infected samples without CAG and H. pylori-infected CAG samples were analyzed. Gene Ontology and pathway enrichment analyses were performed, followed by protein-protein interaction network construction. We used immunohistochemistry analysis to identify DEGs in 20 chronic gastritis, 20 CAG, and 22 gastric cancer (GC) specimens. RESULTS: A total of 303 upregulated and 26 downregulated DEGs were identified. The pathways enriched by upregulated DEGs were mainly related to fat digestion and absorption, peroxisome proliferator-activated receptor signaling pathway, and chemical carcinogenesis. Cytochrome P450, family 3, subfamily A, polypeptide 4 (CYP3A4) had the highest degrees in protein-protein interaction network. Moreover, the positive rates of CYP3A4 protein expression in chronic gastritis, CAG, and GC were 10% (2/20), 55% (11/20), and 77.3% (17/22), respectively (P < 0.001). The Kaplan-Meier analysis revealed that elevated expression of CYP3A4 was significantly associated with worse overall survival and first progression, respectively (P < 0.0001). CONCLUSION: According to the findings of this study, the expression of CYP3A4 might be related to the potential carcinogenic transformation of CAG to GC. Therefore, CYP3A4 may be biomarkers to predict progression of CAG and poor prognosis of gastric cancer.

Molecular mechanism of Helicobacter pylori-induced autophagy in gastric cancer.

Helicobacter pylori (H. pylori) is a gram-negative pathogen that colonizes gastric epithelial cells. The drug resistance rates of H. pylori have dramatically increased, causing persistent infections. Chronic infection by H. pylori is a critical cause of gastritis, peptic ulcers and even gastric cancer. In host cells, autophagy is stimulated to maintain cellular homeostasis following intracellular pathogen recognition by the innate immune defense system. However, H. pylori-induced autophagy is not consistent during acute and chronic infection. Therefore, a deeper understanding of the association between H. pylori infection and autophagy in gastric epithelial cells could aid the understanding of the mechanisms of persistent infection and the identification of autophagy-associated therapeutic targets for H. pylori infection. The present review describes the role of H. pylori and associated virulence factors in the induction of autophagy by different signaling pathways during acute infection. Additionally, the inhibition of autophagy in gastric epithelial cells during chronic infection was discussed. The present review summarized H. pylori-mediated autophagy and provided insights into its mechanism of action, suggesting the induction of autophagy as a novel therapeutic target for persistent H. pylori infection.

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. The experimental results demonstrated the validity of the proposed random forest-based method for recognition of Chinese sign language (CSL) subwords. With the proposed method, 98.25% average accuracy was obtained for the classification of a list of 121 frequently used CSL subwords. Moreover, the random forests method demonstrated a superior performance in resisting the impact of bad training samples. When the proportion of bad samples in the training set reached 50%, the recognition error rate of the random forest-based method was only 10.67%, while that of a single decision tree adopted in our previous work was almost 27.5%. Our study offers a practical way of realizing a robust and wearable EMG-ACC-based SLR systems.