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.

Clinical Distribution and Drug Resistance of Pseudomonas aeruginosa in Guangzhou, China from 2017 to 2021.

The aim of the current study was to analyse the distribution of antimicrobial drug resistance (AMR) among Pseudomonas aeruginosa (P. aeruginosa, PA) isolates from Guangdong Provincial People's Hospital (GDPH) from 2017 to 2021, and the impact of the COVID-19 outbreak on changes in the clinical distribution and drug resistance rate of P. aeruginosa to establish guidelines for empiric therapy. Electronic clinical data registry records from 2017 to 2021 were retrospectively analysed to study the AMR among P. aeruginosa strains from GDPH. The strains were identified by VITEK 2 Compact and MALDI-TOF MS, MIC method or Kirby-Bauer method for antibiotic susceptibility testing. The results were interpreted according to the CLSI 2020 standard, and the data were analysed using WHONET 5.6 and SPSS 23.0 software. A total of 3036 P. aeruginosa strains were detected in the hospital from 2017 to 2021, and they were primarily distributed in the ICU (n = 1207, 39.8%). The most frequent specimens were respiratory tract samples (59.6%). The detection rate for P. aeruginosa in 5 years was highest in September, and the population distribution was primarily male(68.2%). For the trend in the drug resistance rate, the 5-year drug resistance rate of imipenem (22.4%), aztreonam (21.5%) and meropenem (19.3%) remained at high levels. The resistance rate of cefepime decreased from 9.4% to 4.8%, showing a decreasing trend year by year (p < 0.001). The antibiotics with low resistance rates were aminoglycoside antibiotics, which were gentamicin (4.4%), tobramycin (4.3%), and amikacin (1.4%), but amikacin showed an increasing trend year by year (p = 0.008). Our analysis indicated that the detection rate of clinically resistant P. aeruginosa strains showed an upwards trend, and the number of multidrug-resistant (MDR) strains increased year by year, which will lead to stronger pathogenicity and mortality. However, after the outbreak of COVID-19 in 2020, the growth trend in the number of MDR bacteria slowed, presumably due to the strict epidemic prevention and control measures in China. This observation suggests that we should reasonably use antibiotics and treatment programs in the prevention and control of P. aeruginosa infection. Additionally, health prevention and control after the outbreak of the COVID-19 epidemic (such as wearing masks, washing hands with disinfectant, etc., which reduced the prevalence of drug resistance) led to a slowdown in the growth of the drug resistance rate of P. aeruginosa in hospitals, effectively reducing the occurrence and development of drug resistance, and saving patient's treatment costs and time.