Helicobacter pylori infection is a risk factor for constipation in patients with Parkinson's disease: A multicenter prospective cohort study.

BACKGROUND AND AIMS: Constipation is one of the most common nonmotor symptoms (NMSs) of Parkinson's disease (PD). The infection rate of Helicobacter pylori (HP) is greater in PD patients. This study was a multicenter prospective cohort study in which propensity score matching (PSM) was used to determine whether HP infection was a risk factor for constipation in patients with PD. METHODS: A total of 932 PD patients with 13C-urea breath test for HP were included in the study. The PSM was estimated with the use of a nonparsimonious multivariate logistic regression model, with HP infection as the dependent variable and all the baseline characteristics as covariates. A total of 697 patients composed the study cohort, including 252 (36.2 %) patients in the HP-positive (HPP) group and 445 (63.8 %) patients in the HP-negative (HPN) group. Before PSM, there were differences in several of the baseline variables between the two groups. After PSM, 250 HPP patients were matched with 250 HPN patients and the standardized differences were less than 0.1 for all variables. RESULTS: The present results demonstrate that HP infection is a risk factor for constipation in patients with PD [RR (95 % CI) 1.412 (1.155-1.727), P < 0.001]. Subgroup analyses revealed that HP infection was both a risk factor for constipation in Hoehn-Yahr scale (1,1.5) group and Hoehn-Yahr scale (2-5) group [OR (95 % CI) 1.811 (1.079-3.038), P < 0.025; OR (95 % CI) 2.041 (1.177-3.541), P < 0.011]. CONCLUSIONS: The results of our prospective cohort study suggest that Helicobacter pylori infection is a risk factor for constipation in patients with PD. TRIAL REGISTRATION: ChiCTR2300071631.

U-Net deep learning model for endoscopic diagnosis of chronic atrophic gastritis and operative link for gastritis assessment staging: a prospective nested case-control study.

Background: The operative link for the gastritis assessment (OLGA) system can objectively reflect the stratification of gastric cancer risk in patients with chronic atrophic gastritis (CAG). Objectives: We developed a real-time video monitoring model for the endoscopic diagnosis of CAG and OLGA staging based on U-Net deep learning (DL). To further validate and improve its performance, we designed a study to evaluate the diagnostic evaluation indices. Design: A prospective nested case-control study. Methods: Our cohort consisted of 1306 patients from 31 July 2021 to 31 January 2022. According to the pathological results, patients in the cohort were divided into the CAG group and the chronic non-atrophic gastritis group to evaluate the diagnostic evaluation indices. Each atrophy lesion was automatically labeled and the atrophy severity was assessed by the model. Propensity score matching was used to minimize selection bias. Results: The diagnostic evaluation indices and the consistency between OLGA staging and pathological diagnosis of the model were superior to those of endoscopists [sensitivity (89.31% versus 67.56%), specificity (90.46% versus 70.23%), positive predictive value (90.35% versus 69.41%), negative predictive value (89.43% versus 68.40%), accuracy rate (89.89% versus 68.89%), Youden index (79.77% versus 37.79%), odd product (79.23 versus 4.91), positive likelihood ratio (9.36 versus 2.27), negative likelihood ratio (0.12 versus 0.46)], areas under the curves (AUC) (95% CI) (0.919 (0.893-0.945) versus 0.749 (0.707-0.792), p < 0.001) and kappa (0.816 versus 0.291)]. Conclusion: Our study demonstrated that the DL model can assist endoscopists in real-time diagnosis of CAG during gastroscopy and synchronous identification of high-risk OLGA stage (OLGA stages III and IV) patients. Trial registration: ChiCTR2100044458.

PsRGL1 negatively regulates chilling- and gibberellin-induced dormancy release by PsF-box1-mediated targeting for proteolytic degradation in tree peony.

Tree peony bud endodormancy is a common survival strategy similar to many perennial woody plants in winter, and the activation of the GA signaling pathway is the key to breaking endodormancy. GA signal transduction is involved in many physiological processes. Although the GA-GID1-DELLA regulatory module is conserved in many plants, it has a set of specific components that add complexity to the GA response mechanism. DELLA proteins are key switches in GA signaling. Therefore, there is an urgent need to identify the key DELLA proteins involved in tree peony bud dormancy release. In this study, the prolonged chilling increased the content of endogenously active gibberellins. PsRGL1 among three DELLA proteins was significantly downregulated during chilling- and exogenous GA3-induced bud dormancy release by cell-free degradation assay, and a high level of polyubiquitination was detected. Silencing PsRGL1 accelerated bud dormancy release by increasing the expression of the genes associated with dormancy release, including PsCYCD, PsEBB1, PsEBB3, PsBG6, and PsBG9. Three F-box protein family members responded to chilling and GA3 treatments, resulting in PsF-box1 induction. Yeast two-hybrid and BiFC assays indicated that only PsF-box1 could bind to PsRGL1, and the binding site was in the C-terminal domain. PsF-box1 overexpression promoted dormancy release and upregulated the expression of the dormancy-related genes. In addition, yeast two-hybrid and pull-down assays showed that PsF-box1 also interacted with PsSKP1 to form an E3 ubiquitin ligase. These findings enriched the molecular mechanism of the GA signaling pathway during dormancy release, and enhanced the understanding of tree peony bud endodormancy.

Deep learning as a novel method for endoscopic diagnosis of chronic atrophic gastritis: a prospective nested case-control study.

BACKGROUND AND AIMS: Chronic atrophic gastritis (CAG) is a precancerous disease that often leads to the development of gastric cancer (GC) and is positively correlated with GC morbidity. However, the sensitivity of the endoscopic diagnosis of CAG is only 42%. Therefore, we developed a real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net deep learning (DL) and conducted a prospective nested case-control study to evaluate the diagnostic evaluation indices of the model and its consistency with pathological diagnosis. METHODS: Our cohort consisted of 1539 patients undergoing gastroscopy from December 1, 2020, to July 1, 2021. Based on pathological diagnosis, patients in the cohort were divided into the CAG group or the chronic nonatrophic gastritis (CNAG) group, and we assessed the diagnostic evaluation indices of this model and its consistency with pathological diagnosis after propensity score matching (PSM) to minimize selection bias in the study. RESULTS: After matching, the diagnostic evaluation indices and consistency evaluation of the model were better than those of endoscopists [sensitivity (84.02% vs. 62.72%), specificity (97.04% vs. 81.95%), positive predictive value (96.60% vs. 77.66%), negative predictive value (85.86% vs. 68.73%), accuracy rate (90.53% vs. 72.34%), Youden index (81.06% vs. 44.67%), odd product (172.5 vs. 7.64), positive likelihood ratio (28.39 vs. 3.47), negative likelihood ratio (0.16 vs. 0.45), AUC (95% CI) [0.909 (0.884-0.934) vs. 0.740 (0.702-0.778)] and Kappa (0.852 vs. 0.558)]. CONCLUSIONS: Our prospective nested case-control study proved that the diagnostic evaluation indices and consistency evaluation of the real-time video monitoring model for endoscopic diagnosis of CAG based on U-Net DL were superior to those of endoscopists. Trial registration ChiCTR2100044458 , 18/03/2020.

Deep learning model can improve the diagnosis rate of endoscopic chronic atrophic gastritis: a prospective cohort study.

BACKGROUND AND AIMS: Chronic atrophic gastritis (CAG) is a precancerous form of gastric cancer. However, with pathological diagnosis as the gold standard, the sensitivity of endoscopic diagnosis of atrophy is only 42%. We developed a deep learning (DL)-based real-time video monitoring diagnostic model for endoscopic CAG and conducted a prospective cohort study to verify whether this diagnostic model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. METHODS: A U-NET network was used to build a real-time video monitoring diagnostic model for endoscopic CAG based on DL. We enrolled 431 patients who underwent gastroscopy from October 1, 2020, to December 1, 2020. To keep the baseline data of enrolled patient uniform and control for confounding factors, we applied a paired design and included the same patients in both the DL and the endoscopist group. RESULTS: The DL model improved the diagnosis rate of endoscopic CAG compared with that of endoscopists. Compared with diagnoses by endoscopists, the proportions of moderate and severe CAG in the atrophy patients diagnosed by the DL model were significantly larger, the proportion of "type O" CAG was significantly larger, the number of atrophy sites found was significantly increased, and the number of biopsies was significantly decreased. Compared with diagnoses by endoscopists, in the atrophic lesions diagnosed by the DL model, the proportions of severe atrophy and severe intestinal metaplasia were significantly increased. CONCLUSIONS: Our study suggested the DL model could improve the diagnosis rate of endoscopic CAG compared with that of endoscopists. TRIAL REGISTRATION: ChiCTR2100044458, 18/03/2020.

Biopsy in emergency gastroscopy does not increase the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding combined with suspected malignant gastric ulcer: a multicenter retrospective cohort study.

BACKGROUND: Few studies have reported whether a biopsy in emergency gastroscopy (EG) increased the risk of rebleeding in patients with Forrest I acute nonvariceal upper gastrointestinal bleeding (ANVUGIB) combined with suspected malignant gastric ulcer (SMGU). This study aims to conduct a multicenter retrospective cohort study using propensity score matching to verify whether a biopsy in EG increases the risk of rebleeding in patients diagnosed with Forrest I ANVUGIB combined with SMGU. METHODS: Using the data for propensity-matched patients, logistic regression models were fitted using rebleeding as the dependent variable. Survival time was defined as the length of time the patient experienced from visiting the emergency department to rebleeding. We used the Kaplan-Meier (KM) method to analyze the 30-day survival of the patients with and without a biopsy after matching, and the log-rank test was performed to examine the differences in survival. RESULTS: With the use of propensity score matching, 308 patients who underwent a biopsy in EG were matched with 308 patients who did not. In the five logistic regression models, there were no significant group differences in the risk of rebleeding in patients with Forrest I ANVUGIB combined with SMGU between the biopsy and no-biopsy groups. The probability of survival was not significantly different between the no-biopsy and biopsy groups. CONCLUSIONS: In this multicenter, retrospective propensity score matching cohort study, compared with patients without a biopsy, patients with a biopsy during EG had no increased risk of rebleeding, and there was no significant difference in the rate of rebleeding.

Fecal 16S rRNA Gene Sequencing Analysis of Changes in the Gut Microbiota of Rats with Low-Dose Aspirin-Related Intestinal Injury.

BACKGROUND: The incidence of small intestinal injury caused by low-dose aspirin (LDA) is high, but the pathogenesis and intervention measures of it have not been elucidated. Recent studies have found gut microbiota to be closely associated with onset and development of NSAID-induced intestinal injury. However, studies of the changes in the gut microbiota of rats with LDA-related intestinal injury have been lacking recently. In this study, we investigated fecal 16S rRNA gene sequencing analysis of changes in the gut microbiota of rats with LDA-related intestinal injury. METHODS: Sprague-Dawley (SD) rat models of small intestinal injury were established by intragastric administration of LDA. The small intestinal tissues and the fecal samples were harvested. The fecal samples were then analyzed using high-throughput sequencing of 16S rRNA V3-V4 amplicons. The gut microbiota composition and diversity were analyzed and compared using principal coordinate analysis (PCoA), nonmetric multidimensional scaling (NMDS) analysis, the unweighted pair-group method with arithmetic mean (UPGMA) clustering analysis, multivariate statistical analysis (ANOSIM, MetaStats, and LEfSe), and spatial statistics. RESULTS: The LDA rat model was successfully established. Decreased Firmicutes and increased Bacteroidetes abundances in rats with LDA-induced small intestinal injury were revealed. MetaStats analysis between the before administration of LDA (CG) and after administration of LDA (APC) groups showed that the intestinal floras exhibiting significant differences (P < 0.05, q < 0.1) were Firmicutes, Bacteroides, Cyanobacteria, Melainabacteria, Coriobacteriia, Bacteroidia, Bacteroidales, Eubacteriaceae, and Streptococcaceae. In addition, the bacterial taxa showing significant differences between the control (NS) and APC groups were Firmicutes, Bacteroides, Verrucomicrobiaceae and Peptococcaceae. CONCLUSIONS: The alterations in the gut microbiota composition and diversity of rats with LDA-related intestinal injury were found in the present study. The change of gut microbiota in LDA-related intestinal injury will lay the foundation for further research on the function and signaling pathways of the intestinal flora and promote the use of intestinal flora as drug targets to treat LDA-induced small intestinal injury.

Risk Factors for Acute Coronary Syndrome in Upper Gastrointestinal Bleeding Patients.

BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a common critical disease with a certain fatality rate. Acute coronary syndrome (ACS), another critical ill condition, is a regular occurrence in the UGIB. We identified risk factors for ACS in UGIB. METHODS: 676 patients diagnosed with UGIB were enrolled retrospectively. We assessed the occurrence of ACS in UGIB patients and identified the risk factors for ACS by logistic regression analysis and random forest analysis. RESULTS: After propensity score matching (PSM), the ACS group (n = 69) and non-ACS group (n = 276) were analyzed. Logistic regression analysis showed that syncope (P = 0.001), coronary heart disease history (P = 0.001), Glasgow Blatchford score (P ≤ 0.001), Rockall risk score (P = 0.004), red blood cell distribution width (RDW) (P ≤ 0.001), total bilirubin (TBil) (P = 0.046), fibrinogen (P ≤ 0.001), and hemoglobin (P = 0.001) had important roles in ACS patients. With Mean Decrease Gini (MDG) sequencing, fibrinogen, RDW, and hemoglobin were ranked the top three risk factors associated with ACS. In ROC analysis, fibrinogen (AUC = 0.841, 95% CI: 0.779-0.903) and RDW (AUC = 0.826, 95% CI: 0.769-0.883) obtained good discrimination performance. According to sensitivity > 80%, the pAUC of fibrinogen and RDW were 0.077 and 0.101, respectively, and there was no significant difference (P = 0.326). However, according to specificity > 80%, the pAUC of fibrinogen was higher than that of RDW (0.126 vs. 0.088, P = 0.018). CONCLUSION: Fibrinogen and RDW were important risk factors for ACS in UGIB. Additionally, combination with coronary heart disease, syncope, hemoglobin, and TBil played important roles in the occurrence of ACS. Meanwhile, it was also noted that Rockall score and Glasgow Blatchford score should be performed to predict the risk.

Thromboelastography-derived parameters for the prediction of acute thromboembolism following non-steroidal anti-inflammatory drug-induced gastrointestinal bleeding: A retrospective study.

Efficacy of thromboelastography (TEG)-derived parameters for the prediction of acute thromboembolism (AT) in patients with non-steroidal anti-inflammatory drug (NSAID)-induced gastrointestinal bleeding (GIB) remains to be determined. A retrospective propensity score matching (PSM) study was performed to evaluate this efficacy. Patients with NSAID-induced GIB (98 with AT; 830 without AT) were matched for age, sex and history of cardiovascular and cerebrovascular diseases using PSM. Multivariate logistic regression was used to determine the efficacy of TEG-derived predictors of AT. Mean Decrease Gini (MDG) coefficients were used to rank the importance of the variables from random forest algorithm results. Univariate analysis indicated that the following indexes were significantly different between the two groups: Reaction time (R value), coagulation forming time, solidification angle, maximum amplitude (MA), coagulation index (CI), hemoglobin levels, D-dimer levels, platelet aggregation test (pAgt) results, fibrinogen levels and Acute Physiology and Chronic Health Evaluation II score (all P<0.001). Multifactor logistic regression analysis indicated that the R value (P=0.010), solidification angle (P=0.004), MA (P=0.038), D-dimer levels (P=0.012) and pAgt results (P=0.015) were independent predictors of AT in patients with NSAID-induced GIB, achieving an area under the curve of 0.999 in receiver operating characteristic curve analyses. The five most important parameters according to the MDG scores (MDGS) were: Solidification angle (MDGS=58.14), R value (MDGS=20.42), pAgt results (MDGS=15.61), D-dimer levels (MDGS=12.78) and CI (MDGS=12.61). The results of the present study indicated that TEG-derived parameters including the R value, solidification angle, MA and CI, as well as D-dimer levels and pAgt score were significant predictors of AT in patients with NSAID-induced GIB.

Risk factors associated with nonsteroidal anti-inflammatory drugs (NSAIDs)-induced gastrointestinal bleeding resulting on people over 60 years old in Beijing.

Gastrointestinal (GI) bleeding is an unwanted side effect common to all chemical types of nonsteroidal anti-inflammatory drugs (NSAIDs), particularly in elderly people. However, the risk factors of GI bleeding associated with NSAIDs for elderly people remain unknown. This study aims to evaluate the risks of GI bleeding associated with NSAIDs in 4728 elderly people over 60 years old based on database from a hospital in Beijing.This retrospective hospital-based study included 4728 patients over 60 years old prescribed with NSAIDs, of which 928 patients had GI bleeding and 3800 did not have. Odds ratios (OR) for the risk of GI bleeding associated with NSAIDs were determined by logistic regression analysis. Mean Decrease Gini (MDG) involved in random forest algorithm was used to rank the associated factors with GI bleeding.In multivariate analysis, family history of GI bleeding (OR, 3.348; P = .000), history of peptic ulcers (OR, 4.068; P = .000), history of cardiovascular and cerebrovascular disease (OR, 1.476; P = .001), diabetes mellitus (OR, 1.408; P = .000), antiplatelet drugs (OR, 3.106; P = .000), Helicobacter pylori infection (OR, 1.312; P = .001), cholesterol level (OR, 0.516; P = .000), upper abdominal discomfort (OR, 3.467; P = .000), anorexia (OR, 2.038; P = .000), and NSAIDs used for 0.5 to 3 months (OR, 0.780; P = .000) were associated with GI bleeding. After ranked the MDG of each factor, the top 5 ranked factors associated with GI bleeding were melena, hematemesis, antiplatelet drugs, cholesterol level, and upper abdominal discomfort.We found that family history of GI bleeding, history of peptic ulcers, history of cardiovascular and cerebrovascular disease, diabetes mellitus, antiplatelet drugs, Helicobacter pylori infection, hypocholesterolemia, and NSAIDs used for 0.5 to 3 months were independent risk factors for GI bleeding on people over 60 years old. Meanwhile, upper abdominal discomfort might be the predictor of GI bleeding associated with NSAIDs elderly users.