Helicobacter pylori is one of the most common pathogenic bacterium worldwide, infecting about 50% of the world’s population. It is a major cause of several upper gastrointestinal diseases, including peptic ulcers and gastric cancer. The emergence of H. pylori resistance to antibiotics has been a major clinical challenge in the field of gastroenterology. In the course of H. pylori infection, some bacteria invade the gastric epithelium and are encapsulated into a self-produced matrix to form biofilms that protect the bacteria from external threats. Bacteria with biofilm structures can be up to 1000 times more resistant to antibiotics than planktonic bacteria. This implies that targeting biofilms might be an effective strategy to alleviate H. pylori drug resistance. Therefore, it is important to develop drugs that can eliminate or disperse biofilms. In recent years, anti-biofilm agents have been investigated as alternative or complementary therapies to antibiotics to reduce the rate of drug resistance. This article discusses the formation of H. pylori biofilms, the relationship between biofilms and drug resistance in H. pylori , and the recent developments in the research of anti-biofilm agents targeting H. pylori drug resistance.

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BackgroundFor patients with locally advanced breast cancer (LABC), conventional TNM staging is not accurate in predicting survival outcomes. The aim of this study was to develop two accurate survival prediction models to guide clinical decision making.MethodsA retrospective analysis of 22,842 LABC patients was performed from 2010 to 2015 using the Surveillance, Epidemiology and End Results (SEER) database. An additional cohort of 200 patients from the Binzhou Medical University Hospital (BMUH) was analyzed. The least absolute shrinkage and selection operator (LASSO) regression was used to screen for variables. The identified variables were used to build a survival prediction model. The performance of the nomogram models was assessed based on the concordance index (C-index), calibration plot, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).ResultsThe LASSO analysis identified 9 variables in patients with LABC, including age, marital status, Grade, histological type, T-stage, N-stage, surgery, radiotherapy, and chemotherapy. In the training cohort, the C-index of the nomogram in predicting the overall survival (OS) was 0.767 [95% confidence intervals (95% CI): 0.751–0.775], cancer specific survival (CSS) was 0.765 (95% CI: 0.756–0.774). In the external validation cohort, the C-index of the nomogram in predicting the OS was 0.858 (95% CI: 0.812–0.904), the CSS was 0.866 (95% CI: 0.817–0.915). In the training cohort, the area under the receiver operator characteristics curve (AUC) values of the nomogram in prediction of the 1, 3, and 5-year OS were 0.836 (95% CI: 0.821–0.851), 0.769 (95% CI: 0.759–0.780), and 0.750 (95% CI: 0.738–0.762), respectively. The AUC values for prediction of the 1, 3, and 5-year CSS were 0.829 (95% CI: 0.811–0.847), 0.769 (95% CI: 0.757–0.780), and 0.745 (95% CI: 0.732–0.758), respectively. Results of the C-index, ROC curve, and DCA demonstrated that the nomogram was more accurate in predicting the OS and CSS of patients compared with conventional TNM staging.ConclusionTwo prediction models were developed and validated in this study which provided more accurate prediction of the OS and CSS in LABC patients than the TNM staging. The constructed models can be used for predicting survival outcomes and guide treatment plans for LABC patients.