Multidrug-resistant (MDR) bacteria are important public health problems. Antibiotic susceptibility testing (AST) currently uses time-consuming culture-based procedures, which cause treatment delays and increased mortality. We developed a machine learning model using Acinetobacter baumannii as an example to explore a fast AST approach using metagenomic next-generation sequencing (mNGS) data.

Surgical site infection (SSI) after colorectal surgery (CRS) remains a significant problem for its negative clinical outcomes. However, it is poorly understood in China. This study aims to investigate the incidence, risk factors and microbiology of SSI after CRS. A nationwide prospective multicenter design was applied. Patients in 19 Chinese hospitals from 2015 to 2018 were prospectively monitored for SSI after CRS. Demographic data, hospital characteristics, and potential perioperative risk factors were collected and analyzed, using univariate and multivariate logistic regression models. Among 3,663 study participants, 134(3.66%) episodes of SSI were identified. The incidence rate of SSI decreased from 5.9 infections per 100 procedures in 2015 to 3.1 infections per 100 procedures in 2018 (incidence rate ratio, 0.52; 95% CI, 0.28-0.94). The SSI rates were 1.88, 4.15, 6.27 and 11.58 per 100 operations for the National Nosocomial Infections Surveillance system (NNIS) risk index categories of 0, 1, and 2 or 3, respectively. Escherichia coli (54/134, 40.3%) and Klebsiella pneumoniae (10/134, 7.5%) were the most frequently isolated microorganisms. A high prevalence of antibiotic resistance were observed in our study, with rates of extended spectrum beta-lactamase-producing or carbapenem-resistant Escherichia coli and Klebsiella pneumonia of 50.0%(27/54) and 30.0%(3/10) respectively. Preoperative hospital stay ≥ 48h (OR=2.28, 95% CI: 1.03-5.02, P=0.042) and contaminated or dirty wound (OR=3.38, 95% CI: 1.88-6.06, P=4.50×10-5) were significantly associated with increasing risk of SSI after CRS. A statistically significant but modest decrease in the incidence rate of CRS SSI over the 4-year study period was observed in this study. Noticeably, the relatively high rates of multidrug-resistant pathogens causing SSI after CRS should be alert, while more studies with large population are needed due to the small number of isolates identified in our survey.

Human infection with Ureaplasma urealyticum(UU) is mainly manifested as non-gonococcal urethritis, where it can lead to cervicitis, premature rupture of membranes and abortion in women, as well as infertility in males, which becomes a major problem in clinical diagnosis and treatment. At present, real-time fluorescence quantitative PCR and culture are the two main methods for detecting UU. The real-time fluorescence quantitative PCR method is cumbersome and cannot accomplish absolute quantification on nucleic acids, while the cultivation method has limitations such as low sensitivity and being time-consuming. The aim of this study is to establish a more rapid and accurate droplet digital PCR(ddPCR) method for the detection of UU. Primers were designed for the ParC gene of UU. Nucleic acids from a standard strain of UU were extracted. Specificity, sensitivity, and repeatability detection was performed using ddPCR, and the detection performance of ddPCR was evaluated. The detection process could be completed in 92 min. It has a high sensitivity of up to 3.8 pg/μL. With a high specificity, no positive microdrop were detected in eight negative control pathogens in this experiment. In addition, ddPCR detection of UU has good repeatability, and the calculated CV is 2.1 %. Our data indicated that ddPCR detection technology has the characteristics of absolute quantification, high stability, high specificity and high sensitivity of UU. It can promote the accurate detection of UU, providing a more scientific basis for clinical diagnosis and treatment.