Endoscopic ultrasonography: an advancing option with duality in both diagnosis and treatment of gastrointestinal oncology

Since their introduction into the clinical practices in 1980s, techniques of endoscopic ultrasonography（EUS） have been rapidly developing and are now in widespread use in gastrointestinal oncology. Evolving from the classical option, EUS today has been much innovated with addition of a variety of novel ideation which makes it a powerful tool with encouraging duality for both diagnostic and therapeutic purposes. There is a dire need for physicians in this field to understand the status quo of EUS as related to the management and detection of gastrointestinal tumors, which is globally reviewed in this paper.

Research community detection from multi-relation researcher network based on structure/attribute similarities

Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.Design/methodology/approach: A heterogeneous researcher network has been constructed by combining multiple relations of academic researchers. Vertex attributes and their similarities were considered and calculated. An approach has been proposed and tested to detect research community in research organizations based on this multi-relation researcher network.Findings: Detection of topologically well-connected, semantically coherent and meaningful research community was achieved.Research limitations: The sample size of evaluation experiments was relatively small. In the present study, a limited number of 72 researchers were analyzed for constructing researcher network and detecting research community. Therefore, a large sample size is required to give more information and reliable results.Practical implications: The proposed multi-relation researcher network and approaches for discovering research communities of similar research interests will contribute to collective innovation behavior such as brainstorming and to promote interdisciplinary cooperation.Originality/value: Recent researches on community detection devote most efforts to singlerelation researcher networks and put the main focus on the topological structure of networks.In reality, there exist multi-relation social networks. Vertex attribute also plays an important role in community detection. The present study combined multiple single-relational researcher networks into a multi-relational network and proposed a structure-attribute clustering method for detecting research community in research organizations.

RNA barcode segments for SARS-CoV-2 identification from HCoVs and SARSr-CoV-2 lineages

Severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pathogen responsible for coronavirus disease 2019(COVID-19),continues to evolve,giving rise to more variants and global reinfections.Previous research has demonstrated that barcode segments can effectively and cost-efficiently identify specific species within closely related populations.In this study,we designed and tested RNA barcode segments based on genetic evolutionary relationships to facilitate the efficient and accurate identification of SARS-CoV-2 from extensive virus samples,including human coronaviruses(HCoVs)and SARSr-CoV-2 lineages.Nucleotide sequences sourced from NCBI and GISAID were meticulously selected and curated to construct training sets,encompassing 1733 complete genome sequences of HCoVs and SARSr-CoV-2 lineages.Through genetic-level species testing,we validated the accuracy and reliability of the barcode segments for identifying SARS-CoV-2.Subsequently,75 main and subordinate species-specific barcode segments for SARS-CoV-2,located in ORF1ab,S,E,ORF7a,and N coding sequences,were intercepted and screened based on single-nucleotide polymorphism sites and weighted scores.Post-testing,these segments exhibited high recall rates(nearly 100%),specificity(almost 30%at the nucleotide level),and precision(100%)performance on identification.They were eventually visualized using one and two-dimensional combined barcodes and deposited in an online database(http://virusbarcodedatabase.top/).The successful integration of barcoding technology in SARS-CoV-2 identification provides valuable insights for future studies involving complete genome sequence polymorphism analysis.Moreover,this cost-effective and efficient identification approach also provides valuable reference for future research endeavors related to virus surveillance.

Inflammation as a Mediator of Microbiome Dysbiosis-Associated DNA Methylation Changes in Gastric Premalignant Lesions

Evidence for the influence of chronic inflammation induced by microbial dysbiosis on aberrant DNA methylation supports a plausible connexion between disordered microbiota and precancerous lesions of gastric cancer(PLGC).Here,a comprehensive study including multi-omics data was performed to estimate the relationships amongst the gastric microbiome,inflammatory proteins and DNA methylation alterations and their roles in PLGC development.The results demonstrated that gastric dysbacteriosis increased the risk of PLGC and DNA methylation alterations in related tumour suppressor genes.Seven inflammatory biomarkers were identified for antrum and corpus tissues,respectively,amongst which the expression levels of several biomarkers were significantly correlated with the microbial dysbiosis index(MDI)and methylation status of specific tumour suppressor genes.Notably,mediation analysis revealed that microbial dysbiosis partially contributed to DNA methylation changes in the stomach via the inflammatory cytokines C-C motif chemokine 20(CCL20)and tumour necrosis factor receptor superfamily member 9(TNFRSF9).Overall,these results may provide new insights into the mechanisms that might link the gastric microbiome to PLGC.