Establishment of droplet digital PCR for the detection of Neisseria gonorrhoeae.

BACKGROUND: Infection with Neisseria gonorrhoeae in adults usually leads to vaginitis and acute urethritis, and infection through the birth canal in newborns can lead to acute neonatal conjunctivitis. In view of certain factors such as a high missed detection rate of N.gonorrhoeae from staining microscopy method, the time-consuming nature and limited sensitivity of bacterial culture method, complicated and inability of absolute quantification from the ordinary PCR method. METHODS: This study aims to establish a ddPCR system to detect N.gonorrhoeae in a absolute quantification, high specificity, high stability and accurate way. We selected the pgi1 gene as the target gene for the detection of N.gonorrhoeae. RESULTS: The amplification efficiency was good in the ddPCR reaction, and the whole detection process could be completed in 94 min. It has a high sensitivity of up to 5.8 pg/µL. With a high specificity, no positive microdroplets were detected in 9 negative control pathogens in this experiment. In addition, ddPCR detection of N.gonorrhoeae has good repeatability, and the calculated CV is 4.2 %. CONCLUSIONS: DdPCR detection technology has the characteristics of absolute quantification, high stability, high specificity and high accuracy of N.gonorrhoeae. It can promote the accuracy of the detecting of N.gonorrhoeae, providing a more scientific basis for clinical diagnosis and treatment.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra.

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Incidence of and risk factors for surgical site infection after colorectal surgery: A multiple-center prospective study of 3,663 consecutive patients in China.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Identification and expression analysis of ceftriaxone resistance-related genes in Neisseria gonorrhoeae integrating RNA-Seq data and qRT-PCR validation.

OBJECTIVES: The aim of the study is to identify ceftriaxone resistance-related genes in Neisseria gonorrhoeae. METHODS: Differences in gene expression were compared between ceftriaxone-susceptible N. gonorrhoeae isolates [minimum inhibitory concentration (MIC)=0.002-0.004mg/L] and isolates with decreased ceftriaxone susceptibility (MIC=0.125-0.5mg/L) using RNA-Seq (RNA sequencing). RESULTS: Total RNA of 10 clinical isolates was used to make libraries and generated an average of 24.07Mb reads per sample; these were assembled into 1871 mRNA genes. Moreover, 21 differentially expressed genes (DEGs) were found between the N. gonorrhoeae isolates with susceptibility and decreased susceptibility to ceftriaxone with a fold change of ≥2 (P<0.05), among which 11 were upregulated and 10 were downregulated. Furthermore, all DEGs were verified by quantitative reverse transcription PCR (qRT-PCR), which detected 25 clinical isolates with decreased ceftriaxone susceptibility and 21 ceftriaxone-susceptible isolates. In addition, seven DEGs revealed relative expression levels by 2-ΔΔCt and showed a statistical significance (P≤0.05). Analysis of Gene Ontology (GO) terms and KEGG pathway for functional enrichment showed that six DEGs were related to the cellular component and one DEG was related to the biosynthesis of antibiotics, and these results might be related to ceftriaxone resistance. CONCLUSIONS: Examining ceftriaxone resistance-related genes in N. gonorrhoeae is necessary owing to the high morbidity and antimicrobial resistance of N. gonorrhoeae, especially its eventual resistance to third-generation extended-spectrum cephalosporins (cefixime and ceftriaxone). Moreover, this report provides a new direction for the study and control of ceftriaxone-resistant N. gonorrhoeae.