gastric-mucosa-quantitative-polymerase-chain-reaction-analysis-for

Gastric Mucosa Quantitative Polymerase Chain Reaction Analysis for Detecting <em>Helicobacter pylori</em> and Antibiotic Resistance

Helicobacter pylori is a main pathogen that infects nearly half of the global population and is threatening public health due to its increasing antibiotic ...

Southern Medical University

To study the clinical features of children with carbapenem-resistant  (CRE) infection and the molecular characteristics of isolated strains.

Clinical features and molecular epidemiology of carbapenem-resistant  infection in children.

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;  = 21), carbapenem-resistant K. pneumoniae, (CRKP;  = 50), and carbapenem-sensitive K. pneumoniae (CSKP;  = 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.  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.

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

Scrub typhus is a vector-borne infectious disease caused by . Accurate and timely diagnosis at the early infection stage could save the patients' lives. Traditional technologies were limited to rapidly and successfully detecting  due to poor specificity, especially in the condition of atypical symptoms. The technology of Metagenomic next-generation sequencing (mNGS) is amenable to finding the real pathogen because it holds potential as a diagnostic platform for unbiased pathogen identification and precision medicine. Herein, we reported two clinical case reports relative to the  infection diagnosed by mNGS. We hope these two cases will improve clinical diagnosis.

Metagenomics as New Tool for Diagnosis of Scrub Typhus: Two Case Reports.

Rapid and sensitive RNA detection is of great value in diverse areas, ranging from biomedical research to clinical diagnostics. Existing methods for RNA detection often rely on reverse transcription (RT) and DNA amplification or involve a time-consuming procedure and poor sensitivity. Herein, we proposed a CRISPR/Cas12a-enabled amplification-free assay for rapid, specific, and sensitive RNA diagnostics. This assay, which we termed T7/G4-CRISPR, involved the use of a T7-powered nucleic acid circuit to convert a single RNA target into numerous DNA activators via toehold-mediated strand displacement reaction and T7 exonuclease-mediated target recycling amplification, followed by activating Cas12a -cleavage of the linker strands inhibiting split G-Quadruplex (G4) assembly, thereby inducing fluorescence attenuation proportion to the input RNA target. We first performed step-by-step validation of the entire assay process and optimized the reaction parameters. Using the optimal conditions, T7/G4-CRISPR was capable of detecting as low as 3.6 pM target RNA, obtaining ∼100-fold improvement in sensitivity compared with the most direct Cas12a assays. Meanwhile, its excellent specificity could discriminate single nucleotide variants adjacent to the toehold region and allow species-specific pathogen identification. Furthermore, we applied it for analyzing bacterial 16S rRNA in 40 clinical urine samples, exhibiting a sensitivity of 90% and a specificity of 100% when validated by RT-quantitative PCR. Therefore, we envision that T7/G4-CRISPR will serve as a promising RNA sensing approach to expand the toolbox of CRISPR-based diagnostics.

CRISPR/Cas12a-Powered Amplification-Free RNA Diagnostics by Integrating T7 Exonuclease-Assisted Target Recycling and Split G-Quadruplex Catalytic Signal Output.

Fungal peritonitis, an uncommon complication in continuous ambulatory peritoneal dialysis (CAPD), has recently garnered increased attention due to its incidence and potential mortality. To the best of our knowledge, this is the first confirmed case of  () causing CAPD-related peritonitis in a patient with chronic nephrotic syndrome in Guangzhou, China. After prompt removal of the peritoneal catheter and active antifungal therapy, no clinical manifestations of peritonitis were observed. Our report underscores the importance of enhancing clinical awareness regarding  and ensuring timely diagnosis in cases of CAPD-related fungal peritonitis.

First case of fungal peritonitis caused by  in China.

The rapid sequence typing (ST) of bacterial strains is crucial for effective nosocomial infection control and mitigating the spread of nosocomial pathogens, e.g., . While accurate in identifying  strains, current typing methods are often impractical in clinical settings due to their time-consuming nature. This study developed a novel approach, combining surface-enhanced Raman spectroscopy (SERS) with machine-learning (ML) algorithms, to construct predictive models for  sequence typing based on SERS spectra. The objective was to assess the feasibility of this integrated method for efficient sequence typing of  strains. Clinically isolated  strains ( = 267) were collected from a single hospital between 2013 and 2023. Based on multilocus sequence typing, 39 STs of  were identified. Then, a SERS spectral database for all these strains was constructed, and predictive models based on eight ML algorithms were developed to predict SERS signals to determine their STs, among which the support vector machine (SVM) model had the best performance (fivefold cross-validation = 99.74%). The typing capacity of the SERS-SVM method was compared with that of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) for  sequence typing, confirming the superiority of SERS-SVM over MALDI-TOF mass spectrometer. This pilot study lays the groundwork for employing the SERS-ML method to rapidly identify  strain types in clinical laboratories, aiding in controlling bacterial pathogen transmission. Further studies are warranted to evaluate its potential in nosocomial surveillance systems, especially for rapidly identifying outbreaks within hospitals.

Long Ye

Laboratory Medicine,
Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences),
Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)

Southern Medical University

Gastric Mucosa Quantitative Polymerase Chain Reaction Analysis for Detecting Helicobacter pylori and Antibiotic Resistance

Long Ye

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Laboratory Medicine,Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences),Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)

Southern Medical University

Gastric Mucosa Quantitative Polymerase Chain Reaction Analysis for Detecting Helicobacter pylori and Antibiotic Resistance

Laboratory Medicine,
Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences),
Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences)