Gastroenterology & Endoscopy

Open access

ISSN: 2949-7523

Gastroenterology & Endoscopy

Open access

Editors-in-Chief

Jun Cheng
Lianyong Li

Editorial Board

Gastroenterology & Endoscopy aims to deliver up-to-date clinical research and authoritative perspectives on gastroenterology, hepatology and digestive endoscopy. Each issue of Gastroenterology &am...

Gastroenterology & Endoscopy aims to deliver up-to-date clinical research and authoritative perspectives on gastroenterology, hepatology and digestive endoscopy. Each issue of Gastroenterology & Endoscopy will focus on the research of diseases of digestive system, including esophagus, stomach, small intestine, large intestine, liver, pancreas, gallbladder, etc. Gastrointestinal endoscopy is another content we pay special attention to, including clinical research and progress of diagnostic and therapeutic endoscopy.

Topics of interests include, but are not limited to:

  • Esophagus: Barrett's Esophagus, Esophageal Varices, Gastropharyngeal Reflux Disease, Esophageal Malignancy.

  • Stomach: Gastrointestinal Bleeding, Gastroenteritis, Gastric Cancer, Helicobacter Pylori, Gastric MALT Lymphoma.

  • Liver: Hepatitis, Fatty Liver Disease, Drug-Induced Liver Injury, Hepatic Fibrosis, Liver Failure, Transplant, Hepatocellular Carcinoma, Liver Biopsy.

  • Pancreas: Pancreatitis, Pancreatic Necrosis, Pancreatic Pseudocyst, Exocrine Pancreatic Insufficiency, Pancreatic Neuroendocrine Tumour, Pancreatic Adenocarcinoma, Pancreatectomy.

  • Bile: Bile Acid Malabsorption, Biliary Obstruction, Biliary Drainage, Primary Biliary Cirrhosis, Biliary Cancer.

  • Colon: Colonic Ischemia, Colorectal Cancer Prevention, Colorectal Cancer Screening, Colorectal Neoplasia, Colorectal Polyp.

  • Small Intestine: Small Intestinal Bacterial Overgrowth, Benign and Malignancy of Small Intestine.

  • Endoscopy: Therapeutic Endoscopy, Esophagogastroduodenoscopy, Capsule Endoscopy, Endoscopic Ultrasound, Colonoscopy.

Editorial Board

Society affiliation

Asia-Pacific Alliance of Liver Disease, Beijing The APALD is a non-profit social organization voluntarily initiated by dozens of domestic hospitals including the Beijing Ditan Hospital affiliated with Capital Medical University, as well as numerous research institutes and enterprises such as the Beijing Center for Physical and Chemical Analysis, an...

Asia-Pacific Alliance of Liver Disease, Beijing

The APALD is a non-profit social organization voluntarily initiated by dozens of domestic hospitals including the Beijing Ditan Hospital affiliated with Capital Medical University, as well as numerous research institutes and enterprises such as the Beijing Center for Physical and Chemical Analysis, and has been duly registered and approved by the Beijing Municipal Bureau of Social Organizations.

The Beijing Ditan Hospital affiliated with Capital Medical University serves as the primary initiating unit of the alliance, with Professor Jun Cheng as the chairman of the alliance. Currently, the alliance encompasses over 200 cooperative hospitals, academic teams, technology transfer units, and strategic corporate partners, and this number is continuously growing. The alliance aims to integrate domestic and international medical and non-medical resources, demonstrate and promote new concepts and technologies in the diagnosis and treatment of liver diseases, guide the prevention and control capabilities at the grassroots level, thereby providing high-quality, convenient, and safe medical services to a broad range of patients. Establish 100 national regional collaborative medical service centers across all 34 provinces, municipalities, and autonomous regions, form 30 provincial alliances, and create 1,000 county-level remote consultation medical bases for liver diseases. This initiative aims to build a standardized training system for doctors specializing in liver diseases.

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The digestive system under microgravity environment: changes, mechanisms and the prospects of the future

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3. Efficacy and Safety of BoozLiv™ as an add on therapy in patients with alcoholic liver disease: An open-label, randomized controlled trial

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Traditional regression approach outperformed machine learning algorithms in predicting optimal surgical method in patients with submucosal tumors.

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