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Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classi cation of gastritis for GC.Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classi cation scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.

Research ArticleEllagic acid regulates Wnt/ Ellagic acid regulates Wnt/ Ellagic acid regulates Wnt/β β β---catenin catenin catenin signaling pathway and CDK8 in HCT signaling pathway and CDK8 in HCT signaling pathway and CDK8 in HCT 116 and HT 29 colon cancer cells 116 and HT 29 colon cancer cells 116 and HT 29 colon cancer cells BJP IntroductionColorectal cancer is the second most frequent malignancy and the second leading cause of death due to cancer globally (Walker et al., 2014). Colon cancer carcinogenesis has been reported to be associated with genetic errors in genes involved in apoptosis and cell proliferation (Davies et al., 2005;Watson, 2006;Damaschke et al., 2013;Bharati et al., 2014). Genetic defects triggering aberrant activation of Wnt/ -catenin signalling are common and are reported in over 90% of sporadic cases of colon cancer (Miyaki et al., 1994;Clevers, 2006;Klaus and Birchmeier, 2008;Schon et al., 2014). The key effecttor of Wnt/ -catenin signaling pathway is -catenin that upon activation of Wnt signal, translocates into the nuclear region forming a ternary complex with transcription factors-TCF/Lef (T-cell factor, lymphoid enhancer factor) and activates genes involved in cell pro- Studies have demonstrated that phytochemicals are effective in modulating Wnt/ -catenin signalling pathway (Zhang, et al., 2013;Kim et al., 2014). Ellagic acid, a dimeric derivative of gallic acid occurs naturally in fruits as-strawberry, raspberry, pomegranate, grapes and blackberries and in nuts (Thresiamma and Kuttan, 1996;Talcott and Lee, 2002;Mullen et al., 2003). Anti-

Rationale: Metastases to the duodenum in cervical squamous cell carcinoma are extremely rare, with only 7 cases reported in the published English literature. Patient concerns: We present the case of a 66-year-old woman with duodenal metastasis of cervical squamous cell carcinoma who presented with nausea and vomiting within the past 12 days. Diagnosis: Esophagogastroduodenoscopy revealed a circular narrowed 2nd part of the duodenum with congested and edematous mucosa, which was biopsied for a suspected neoplastic lesion. The pathological diagnosis indicated squamous cell carcinoma identical to the original tumor, confirming duodenal metastasis. Interventions: The patient received total parenteral nutrition on admission, but symptoms of jaundice soon appeared in the following week, suggesting infiltration of carcinoma into the common bile duct. After percutaneous transhepatic cholangial drainage was performed, jaundice eased in the following 3 days, and an uncovered self-expandable metallic stent was subsequently inserted into the stenosis of 2nd and 3rd part of the duodenum. Subsequently, the patient's diet quickly resumed. Outcomes: The patient refused further intervention and was discharged home to continue palliative care at the local hospital. Lessons: Clinicians should be alert to patients’ past medical history to ensure that duodenal metastasis of other tumors is considered in the differential diagnosis. For endoscopists, awareness of such patterns of duodenal stenosis is vital for the accurate recognition of such infrequent diseases.

Background: Changes in gastric mucosa caused by Helicobacter pylori ( H. pylori) infection affect the observation of early gastric cancer under endoscopy. Although previous researches reported that computer-aided diagnosis (CAD) systems have great potential in the diagnosis of H. pylori infection, their explainability remains a challenge. Objective: We aim to develop an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) and giving diagnostic basis under endoscopy. Design: A case–control study. Methods: We retrospectively obtained 47,239 images from 1826 patients between 1 June 2020 and 31 July 2021 at Renmin Hospital of Wuhan University for the development of EADHI. EADHI was developed based on feature extraction combining ResNet-50 and long short-term memory networks. Nine endoscopic features were used for H. pylori infection. EADHI’s performance was evaluated and compared to that of endoscopists. An external test was conducted in Wenzhou Central Hospital to evaluate its robustness. A gradient-boosting decision tree model was used to examine the contributions of different mucosal features for diagnosing H. pylori infection. Results: The system extracted mucosal features for diagnosing H. pylori infection with an overall accuracy of 78.3% [95% confidence interval (CI): 76.2–80.3]. The accuracy of EADHI for diagnosing H. pylori infection (91.1%, 95% CI: 85.7–94.6) was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3) in internal test. And it showed a good accuracy of 91.9% (95% CI: 85.6–95.7) in external test. Mucosal edema was the most important diagnostic feature for H. pylori positive, while regular arrangement of collecting venules was the most important H. pylori negative feature. Conclusion: The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. Plain language summary An explainable AI system for Helicobacter pylori with good diagnostic performance Helicobacter pylori ( H. pylori) is the main risk factor for gastric cancer (GC), and changes in gastric mucosa caused by H. pylori infection affect the observation of early GC under endoscopy. Therefore, it is necessary to identify H. pylori infection under endoscopy. Although previous research showed that computer-aided diagnosis (CAD) systems have great potential in H. pylori infection diagnosis, their generalization and explainability are still a challenge. Herein, we constructed an explainable artificial intelligence system for diagnosing H. pylori infection (EADHI) using images by case. In this study, we integrated ResNet-50 and long short-term memory (LSTM) networks into the system. Among them, ResNet50 is used for feature extraction, LSTM is used to classify H. pylori infection status based on these features. Furthermore, we added the information of mucosal features in each case when training the system so that EADHI could identify and output which mucosal features are contained in a case. In our study, EADHI achieved good diagnostic performance with an accuracy of 91.1% [95% confidence interval (CI): 85.7–94.6], which was significantly higher than that of endoscopists (by 15.5%, 95% CI: 9.7–21.3%) in internal test. In addition, it showed a good diagnostic accuracy of 91.9% (95% CI: 85.6–95.7) in external tests. The EADHI discerns H. pylori gastritis with high accuracy and good explainability, which may improve the trust and acceptability of endoscopists on CADs. However, we only used data from a single center to develop EADHI, and it was not effective in identifying past H. pylori infection. Future, multicenter, prospective studies are needed to demonstrate the clinical applicability of CADs.