Background: The role of Helicobacter pylori (H. pylori) infection in the development of non-alcoholic fatty liver disease (NAFLD) remains controversial. The exact relationship requires further investigation. This study aimed to determine the association between them in China. Methods: A retrospective study was conducted on 71,633 participants who underwent physical examinations. ¹³C urea breath test (¹³C-UBT) was conducted to detect H. pylori infection, and ultrasonography was used to detect NAFLD. Results: Body mass index (BMI), blood pressure (BP), and triglyceride (TG) levels were higher in participants with H. pylori infection than in those without H. pylori infection. While the levels of high-density lipoprotein cholesterol (HDL-C) for participants with H. pylori infection was lower than without H. pylori infection (P < 0.001). After adjusting for confounding factors (age, sex, BMI, BP, Scr, BUN, LDL-C, HDL-C, triglycerides, FBG and HbA1c), multivariate logistic regression analysis indicated that there was no independent relationship between them (P = 0.574). Subgroup analysis (stratified by sex, age, BMI, hypertension, diabetes and dyslipidemia) showed that H. pylori infection was not included as an independent risk factor for NAFLD. Moreover, the different grades of NAFLD were not related to H. pylori infection. Conclusions: These results indicate that H. pylori infection is not an independent risk factor for NAFLD in China.

CXC chemokine receptor 7 (CXCR7) is frequently overexpressed in a variety of tumors. Nevertheless, whether CXCR7 can be used as a tumor prognosis marker has not been systematically assessed. The current meta-analysis was performed to obtain an accurate evaluation of the relationship between CXCR7 level and the prognosis of cancer patients. Embase, Web of Science, and PubMed were systematically searched according to a defined search strategy up to June 11, 2018. Then, the required data were extracted from all qualified studies which were screened out based on the defined inclusion and exclusion criteria. Finally, the hazard ratios (HR) with 95% confidence intervals (CI) were used to evaluate the prognostic significance of CXCR7 in tumor patients. A total of 28 original research studies comprising 33 cohorts and 5685 patients were included in this meta-analysis. The results showed that CXCR7 overexpression was significantly related to worse overall survival (OS) (HR 1.72; 95% CI 1.49–1.99), disease-free survival (DFS) (HR 5.58; 95% CI 3.16–9.85), progression-free survival (PFS) (HR 2.83; 95% CI 1.66–4.85) and recurrence-free survival (RFS) (HR 1.58; 95% CI 1.34–1.88) in cancer patients. Furthermore, for certain types of cancer, significant associations between higher CXCR7 expression and worse OS of glioma (HR 1.77; 95% CI 1.43–2.19), breast cancer (HR 1.45; 95% CI 1.28–1.63), esophageal cancer (HR 2.72; 95% CI 1.11–6.66) and pancreatic cancer (HR 1.46; 95% CI 1.12–1.90) were found. However, for lung cancer and hepatocellular cancer, there was no significant relationship between CXCR7 expression level and OS, (HR 2.40; 95% CI 0.34–17.07) and (HR 1.37; 95% CI 0.84–2.24) respectively. Increased CXCR7 level could predict poor prognosis of tumor patients and might be regarded as a novel prognostic biomarker for tumor patients.

Purpose: To estimate the incidence, death, disability-adjusted life years (DALYs) and attributable risk factors for respiratory infection and tuberculosis (RIT) in the US from 1990 to 2019. Methods: Following the methodology framework and analytical strategies used in the Global Burden of Disease Study 2019, the incidence, death, DALYs and risk factors of RIT were examined by age, gender and states from 1990 to 2019 in the US. All estimates were calculated as counts, age-standardized rates per 100,000 people and percentage change, with 95% confidence intervals (CIs). Results: In 2019, the age-standardized incidence, death and DALY rates per 100,000 people of RIT were 339,703 (95% CI 303,184 to 382,354), 13.6 (95% CI 12.2 to 14.4) and 384.9 (95% CI 330.6 to 458.6), respectively. Among RIT causes, upper respiratory infection accounted for the large majority of RIT age-standardized incidence rate, while lower respiratory infection constituted the highest proportion of RIT age-standardized death and DALY rates. The age-standardized incidence, death and DALY rates of RIT in 2019 and their temporal trends since 1990 varied widely across states and socio-demographic index. Among all attributable risk factors, smoking was the leading one for age-standardized RIT deaths in 2019, followed by low temperature and alcohol use (the attributable fractions were 17.7%, 15.3% and 6.9%, respectively). Conclusion: Our results suggest that RIT remained a major cause of health burden in the US, with large disparities persisting between US states. Intervention efforts for RIT hotspots, high-risk populations and modifiable risk factors are necessary.

Background: There is a lack of effective therapies for enteric nervous system (ENS) injury. Our previous study showed that transplanted bone marrow-derived mesenchymal stem cells (BMSCs) play a “glia-like cells” role in initiating ENS regeneration in denervated mice. Cellular energy metabolism is an important factor in maintaining the biological characteristics of stem cells. However, how cellular energy metabolism regulates the fate of BMSCs in the ENS-injured microenvironment is unclear.Methods: The biological characteristics, energy metabolism, and histone methylation levels of BMSCs following ENS injury were determined. Then, glutamate dehydrogenase 1 (Glud1) which catalyzes the oxidative deamination of glutamate to α-KG was overexpressed (OE) in BMSCs. Further, OE-Glud1 BMSCs were targeted–transplanted into the ENS injury site of denervated mice to determine their effects on ENS regeneration.Results: In vitro, in the ENS-injured high-glutamate microenvironment, the ratio of α-ketoglutarate (α-KG) to succinate (P < 0.05), the histone demethylation level (P < 0.05), the protein expression of glial cell markers (P < 0.05), and the gene expression of Glud1 (P < 0.05) were significantly increased. And the binding of H3K9me3 to the GFAP, S100B, and GDNF promoter was enhanced (P < 0.05). Moreover, α-KG treatment increased the monomethylation and decreased the trimethylation on H3K9 (P < 0.01) and H3K27 (P < 0.05) in BMSCs and significantly upregulated the protein expression of glial cell markers (P < 0.01), which was reversed by the α-KG competitive inhibitor D-2-hydroxyglutarate (P < 0.05). Besides, overexpression of Glud1 in BMSCs exhibited increases in monomethylation and decreases in trimethylation on H3K9 (P < 0.05) and H3K27 (P < 0.05), and upregulated protein expression of glial cell markers (P < 0.01). In vivo, BMSCs overexpressing Glud1 had a strong promotion effect on ENS regeneration in denervated mice through H3K9/H3K27 demethylation (P < 0.05), and upregulating the expression of glial cell protein (P < 0.05).Conclusions: BMSCs overexpressing Glud1 promote the expression of glial cell markers and ENS remodeling in denervated mice through regulating intracellular α-KG and H3K9/H3K27 demethylation.

Background: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. Methods: The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. Results: In the internal test cohort, the accuracy of CNN model was 92.1–100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45–99.92% and 93.35–99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84–0.98). Conclusions: The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.