To address a growing concern about drug-induced liver injury (DILI), a nationwide study was performed to investigate the significance of DILI in Korea. From May 2005 to May 2007, cases of DILI (alanine transferase >3 × upper normal limit or total bilirubin >2 × upper normal limit) from 17 referral university hospitals were prospectively enrolled. Adjudication by the seven review boards was considered for the confirmation of causality and the Roussel Uclaf Causality Assessment Method (RUCAM) scale was used. A total of 371 cases were diagnosed with DILI. The extrapolated incidence of hospitalization at university hospital in Korea was 12/100,000 persons/year. The causes included “herbal medications” (102, 27.5%), “prescription or non-prescription medications” (101, 27.3%), “health foods or dietary supplements” (51, 13.7%), “medicinal herbs or plants” (35, 9.4%), “folk remedies” (32, 8.6%), “combined” (30, 8.2%), “herbal preparations” (12, 3.2%), and others (8, 2.2%). Nine cases were linked to acetaminophen. The frequencies of hepatocellular, mixed, and cholestatic types were 76.3, 14.8, and 8.9%, respectively. A total of 234 cases met the criteria for Hy's law. Five patients died or underwent transplantation. Twenty-five cases (21 herbs and 4 medications) did not meet the time-to-onset criteria of the RUCAM. DILI appears to be a highly relevant health problem in Korea. “Herbal medications” are the principal cause of DILI. A more objective and reproducible causality assessment tool is strongly desired as the RUCAM scale frequently undercounts the cases caused by herbs owing to a lack of previous information and incompatible time criteria.

Background: The aim of this study was to evaluate the time trend of seropositivity of Helicobacter pylori (H. pylori) over the period of 13 years in an asymptomatic Korean population, and investigate associated risk factors. Methods: This cross-sectional nationwide multicentre study surveyed anti-H. pylori IgG antibodies in 19,272 health check-up subjects (aged [greater than and equal to]16 years) in 2011. Risk factors for H. pylori infection were investigated using logistic regression. Seropositivity in asymptomatic subjects without H. pylori eradication was compared between the years 1998 and 2005. Birth cohort effects were also evaluated. Results: After exclusion of subjects with a history of H. pylori eradication therapy (n = 3,712, 19.3%) and gastric symptoms (n = 4,764, 24.7%), the seroprevalence of H. pylori infection was 54.4% in 10,796 subjects. This was significantly lower than the seroprevalence of 59.6% in 2005 and that of 66.9% in 1998, and this decrease of seropositivity of H. pylori became widespread across all ages and in most areas of the country. This decreasing trend could be explained by cohort analysis. All younger birth cohorts had a lower seroprevalence of H. pylori than older birth cohorts at the same age. Decreased seroprevalence within the same birth cohorts also accounted for this phenomenon. Clinical risk factors of H. pylori infection were higher cholesterol level ([greater than and equal to] 240 mg/dl) (OR = 1.33; 95% CI = 1.14-1.54), male gender, older age, low income, and residence in a rural area. Conclusions: A decreasing trend of H. pylori seroprevalence due to a birth cohort effect requires further studies on its related human host factors as well as socio-economic and hygienic factors. In addition, the relationship between H. pylori infection and high cholesterol level needs more investigation regarding underlying pathogenesis.

Background: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of H pylori infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification. Objective: This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of H pylori infection using endoscopic images. Methods: Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of H pylori infection and with application of AI for the prediction of H pylori infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed. Results: Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of H pylori infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with H pylori infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images. Conclusions: An AI algorithm is a reliable tool for endoscopic diagnosis of H pylori infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome. Trial Registration: PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957