quantitative-polymerase-chain-reaction-qpcr-based-rapid-diagnosis

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

Helicobacter pylori is a major human pathogen that&#160;infects approximately half of the global population and is becoming a serious health threat due to ...

comparative-analysis-automatic-fecal-analyzer-versus-direct-wet-smear

Comparative Analysis of Automatic Fecal Analyzer versus Direct Wet Smear Microscopy for Detecting Parasitic Infections in Stool Samples

With socio-economic development, the prevalence of intestinal parasitic diseases has significantly decreased year by year. However, parasitic infections ...

rapid-detection-staphylococcus-aureus-methicillin-resistant

Rapid Detection of Staphylococcus aureus and Methicillin-Resistant Staphylococcus aureus by Multiplex Real-Time Fluorescent PCR

Staphylococcus aureus (S.aureus) is a common Gram-positive human pathogen that causes community-acquired and nosocomial infections, with diverse clinical ...

Southern Medical University

 is one of the most common causes of hospital-acquired infections, especially due to the emergence of the hypervirulent  (hvKp) strains. Multiple methods have been developed to discriminate hvKp strains from classical  (cKp) strains, such as the presence of candidate genes (, -344, , and ), high level of siderophore production, hypermucoviscosity phenotype,  Although the string test is commonly used to confirm the hypermucoviscosity of  strains, it is a method lacking rigidity and accuracy. Surface-enhanced Raman spectroscopy (SERS) coupled with machine learning algorithms has been widely used in discriminating bacterial pathogens with different phenotypes. However, the technique has not be applied to identify hypermucoviscous  (hmvKp) strains. In this study, we isolated a set of  strains from clinical samples, among which hmvKp strains ( = 10) and cKP strains ( = 10) were randomly selected to collect SESR spectra. Eight machine learning algorithms were recruited for model construction and spectral prediction in this study, among which support vector machine (SVM) outperforms all other algorithms with the highest prediction accuracy of hmvKp strains (5-fold cross validation = 99.07%). Taken together, this pilot study confirms that SERS, combined with machine learning algorithms, can accurately identify hmvKp strains, which can facilitate the fast recognition of hvKP strains when combined with relevant methods and biomarkers in clinical settings in the near future.

ArticleTotal : 3	Year
Publication title Cited by 8	2023
Publication title	2025
Publication title Cited by 1	2025

Guang-Hua Li

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