Rapid diagnosis and precision treatment of Helicobacter pylori infection in clinical settings.

Helicobacter pylori is a gram-negative bacterium that colonizes the stomach of approximately half of the worldwide population, with higher prevalence in densely populated areas like Asia, the Caribbean, Latin America, and Africa. H. pylori infections range from asymptomatic cases to potentially fatal diseases, including peptic ulcers, chronic gastritis, and stomach adenocarcinoma. The management of these conditions has become more difficult due to the rising prevalence of drug-resistant H. pylori infections, which ultimately lead to gastric cancer and mucosa-associated lymphoid tissue (MALT) lymphoma. In 1994, the International Agency for Research on Cancer (IARC) categorized H. pylori as a Group I carcinogen, contributing to approximately 780,000 cancer cases annually. Antibiotic resistance against drugs used to treat H. pylori infections ranges between 15% and 50% worldwide, with Asian countries having exceptionally high rates. This review systematically examines the impacts of H. pylori infection, the increasing prevalence of antibiotic resistance, and the urgent need for accurate diagnosis and precision treatment. The present status of precision treatment strategies and prospective approaches for eradicating infections caused by antibiotic-resistant H. pylori will also be evaluated.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Quantitative Polymerase Chain Reaction (qPCR)-Based Rapid Diagnosis of Helicobacter pylori Infection and Antibiotic Resistance.

Helicobacter pylori is a major human pathogen that infects approximately half of the global population and is becoming a serious health threat due to its increasing antibiotic resistance. It is the causative agent of chronic active gastritis, peptic ulcer disease, and gastric cancer and has been classified as a Group I Carcinogen by the International Agency for Research on Cancer. Therefore, the rapid and accurate diagnosis of H. pylori and the determination of its antibiotic resistance are important for the efficient eradication of this bacterial pathogen. Currently, H. pylori diagnosis methods mainly include the urea breath test (UBT), the antigen test, the serum antibody test, gastroscopy, the rapid urease test (RUT), and bacterial culture. Among them, the first three detection methods are noninvasive, meaning they are easy tests to conduct. However, bacteria cannot be retrieved through these techniques; thus, drug resistance testing cannot be performed. The last three are invasive examinations, but they are costly, require high skills, and have the potential to cause damage to patients. Therefore, a noninvasive, rapid, and simultaneous method for H. pylori detection and drug resistance testing is very important for efficiently eradicating H. pylori in clinical practice. This protocol aims to present a specific procedure involving the string test in combination with quantitative polymerase chain reaction (qPCR) for the rapid detection of H. pylori infection and antibiotic resistance. Unlike bacterial cultures, this method allows for easy, rapid, noninvasive diagnosis of H. pylori infection status and drug resistance. Specifically, we used qPCR to detect rea for H. pylori infection and mutations in the 23S rRNA and gyrA genes, which encode resistance against clarithromycin and levofloxacin, respectively. Compared to routinely used culturing techniques, this protocol provides a noninvasive, low-cost, and time-saving technique to detect H. pylori infection and determine its antibiotic resistance using qPCR.