Although CircRNA_100269 is a biomarker used to predict cancer recurrence, its expression and function in gastric cancer (GC) remain unknown. In this study, the expression of circRNA_100269 and its potential downstream miRNA targets were investigated. The molecular function and regulatory mechanism of circRNA_100269 in GC cell lines were also elucidated. The expression levels of circRNA_100269 and its linear isomer LPHN2 mRNA were found to be downregulated (p<0.01) in GC tissues. The target miRNA was predicted to be miR-630, whose expression was upregulated (p<0.01) and found to be negatively correlated with that of circRNA_100269 (r = -0.688) in GC tissues. Moreover, direct interaction of circRNA_100269 and miR-630 was confirmed through dual-luciferase assays. Overexpressing the circRNA_100269 plasmid inhibited cell proliferation (p<0.05). Furthermore, transfection of miR-630 mimics into cell lines overexpressing circRNA_100269 blocked the function of circRNA_100269 (p<0.05). Thus, circRNA_100269 level was downregulated in GC and correlated negatively with that of miR-630. Taken together, our results suggest that circRNA_100269 and miR-630 comprise a novel pathway that regulates proliferation of GC cells.

Summary: Background: The incidence of elevated liver chemistries and the presence of pre‐existing chronic liver disease (CLD) have been variably reported in COVID‐19.Aims: To assess the prevalence of CLD, the incidence of elevated liver chemistries and the outcomes of patients with and without underlying CLD/elevated liver chemistries in COVID‐19.Methods: A comprehensive search of electronic databases from 1 December 2019 to 24 April 2020 was done. We included studies reporting underlying CLD or elevated liver chemistries and patient outcomes in COVID‐19.Results: 107 articles (n = 20 874 patients) were included for the systematic review. The pooled prevalence of underlying CLD was 3.6% (95% CI, 2.5‐5.1) among the 15 407 COVID‐19 patients. The pooled incidence of elevated liver chemistries in COVID‐19 was 23.1% (19.3‐27.3) at initial presentation. Additionally, 24.4% (13.5‐40) developed elevated liver chemistries during the illness. The pooled incidence of drug‐induced liver injury was 25.4% (14.2‐41.4). The pooled prevalence of CLD among 1587 severely infected patients was 3.9% (3%‐5.2%). The odds of developing severe COVID‐19 in CLD patients was 0.81 (0.31‐2.09; P = 0.67) compared to non‐CLD patients. COVID‐19 patients with elevated liver chemistries had increased risk of mortality (OR‐3.46 [2.42‐4.95, P < 0.001]) and severe disease (OR‐2.87 [95% CI, 2.29‐3.6, P < 0.001]) compared to patients without elevated liver chemistries.Conclusions: Elevated liver chemistries are common at presentation and during COVID‐19. The severity of elevated liver chemistries correlates with the outcome of COVID‐19. The presence of CLD does not alter the outcome of COVID‐19. Further studies are needed to analyse the outcomes of compensated and decompensated liver disease.

Background: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients’ clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia. Methods: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang, Lishui, Lanzhou, Linxia, and Zhenjiang between January 23, 2020 and February 8, 2020. Patients were classified into short-term (≤10 days) and long-term hospital stay (>10 days). CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features from pneumonia lesions in first four centers. The predictive performance was evaluated in fifth center (test dataset) on lung lobe- and patients-level. Results: A total of 52 patients were enrolled from designated hospitals. As of February 20, 21 patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in analysis. The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with COVID-19 pneumonia, with areas under the curves of 0.97 (95% CI, 0.83–1.0) and 0.92 (95% CI, 0.67–1.0) by LR and RF, respectively, in test. The LR and RF model showed a sensitivity and specificity of 1.0 and 0.89, 0.75 and 1.0 in test respectively. As of February 28, a prospective cohort of six discharged patients were all correctly recognized as long-term stay using RF and LR models. Conclusions: The machine learning-based CT radiomics features and models showed feasibility and accuracy for predicting hospital stay in patients with COVID-19 pneumonia.

Metabolic dysfunction–associated steatotic liver disease (MASLD), previously known as NAFLD, is increasingly recognized as a prevalent global burden. Type 2 diabetes mellitus (T2DM), another important metabolic disease, is considered a major contributor to the development of MASLD. MASLD and T2DM have a strong association with each other due to shared pathogenic mechanisms. The co-existence of the 2 diseases increases the risk of liver-related adverse outcomes and imposes a heavier burden on extrahepatic outcomes, representing a substantial public health issue. Effective assessment and management of T2DM combined with MASLD necessitate a multidisciplinary approach. The emergence of numerous RCTs has shed light on the treatment of T2DM combined with MASLD. This review uncovers the epidemiology of the intertwined T2DM and MASLD, offers insights into the evaluation of hepatic fibrosis in patients with T2DM, glucose monitoring in the MASLD population, and provides comprehensive co-management strategies for addressing both diseases.