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 ...

detection-helicobacter-pylori-infection-antibiotic-resistance-via

Detection of Helicobacter pylori Infection and Antibiotic Resistance via Stool Quantitative Polymerase Chain Reaction Analysis

Helicobacter pylori (H. pylori) is widely prevalent worldwide, with approximately 50% of the global population having a history of H. pylori infection. In ...

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 ...

a-point-care-platform-for-simultaneous-triplex-pcr-detection

A Point-of-Care Platform for Simultaneous Triplex PCR Detection of Chikungunya, Dengue, and Zika Virus Infections

Chikungunya fever (CHIKF), an acute viral illness transmitted by mosquitoes and first discovered in Tanzania in 1952, has shown intermittent spread in ...

Southern Medical University

 and enterotoxigenic  (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that  spp., and .  are very closely related with many common characteristics. Evolutionarily speaking,  spp., are positioned within the phylogenetic tree of . . Therefore, discrimination of  spp., from .  is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of  spp., and . . As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated .  strains and  species (spp.), that is, . , . , . , and . , based on which SERS spectra were generated and characteristic peaks for  spp., and .  were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating  spp., from . , which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

Rapid discrimination of  spp. and  label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of  (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, Roche solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

Identification of  Resistance to Common Antibiotics: An Overview of Current Methods and Techniques.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

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.

Comparison of Raman spectroscopy with mass spectrometry for sequence typing of  strains: a single-center study.

Fungal infections can cause different diseases, and nucleic acid extraction is a crucial step in assisting the diagnosis of fungal infections. Traditional methods and current commercial fungal nucleic acid extraction kits are often time-consuming and involve hazardous chemicals. We developed a nucleic acid extraction kit that is safe, rapid, and efficient, designated as FNA-REK (Fungal Nucleic Acid Extraction Kit), which facilitates the simultaneous extraction of DNA and RNA without the necessity for hazardous chemicals, liquid nitrogen, or glass beads. This kit is user-friendly and achieves complete extraction within a mere 10 min. The RNA from the culture of Cryptococcus neoformans, Candida albicans, Candida krusei, Aspergillus flavus, Aspergillus niger and Aspergillus fumigatus. Meanwhile, the RNA from Candida albicans, Candida glabrata and Candida tropicalis of mid-stream urine, vaginal discharge and sputum samples, respectively, Cryptococcus neoformans, genus Aspergillus and Pneumocystis jirovecii of sputum samples, genus Aspergillus of bronchoscopy rinsing fluid were extracted using both the FNA-REK and the BIO-TEK. The concentration and purity of the extracted RNA were measured by nanodrop photometer. The nucleic acid extracted from Cryptococcus neoformans and Candida albicans using FNA-REK showed higher concentration and purity compared to BIO-TEK following a 10 dilution. The maximum coefficients of variation for intra-run and inter-run replicates were 2.26% and 8.44%, respectively, significantly lower than 10%, indicating the high reproducibility of the FNA-REK. The FNA-REK yielded a lower cycle threshold (Ct value) than the BIO-TEK for cultured Candida krusei, Aspergillus niger and Aspergillus fumigatus obtained from clinical laboratory. Also, FNA-REK performed better than BIO-TEK for Candida albicans, Candida glabrata, Candida tropicalis, and genus Aspergillus in sputum samples from patients with severe pneumonia. The FNA-REK is a sensitive and stable nucleic acid extraction kit, offering an economical, efficient, and environmentally sustainable approach for fungal nucleic acid extraction. Its high extraction efficiency provides significant technical support for both laboratory and clinical applications, positioning it as a strong contender to become the most convenient and fastest fungal nucleic acid extraction kit currently available on the market.

Development and evaluation of a novel rapid fungal nucleic acid extraction kit.

To assess the risk factors associated with Helicobacter pylori infection in the urban Chinese population.

Risk factors associated with Helicobacter pylori infection in the urban population of China: A nationwide, multi-center, cross-sectional study.

Helicobacter pylori (H. pylori) infection has long been a significant global public health concern, with antibiotic resistance becoming increasingly severe. Gastric fluid qPCR detection can provide information on H. pylori infection and antibiotic resistance.

Detection of Helicobacter pylori Infection and Antibiotic Resistance via String Test Coupled With qPCR Method: A Comparative Methodological Study.

 (), a globally prevalent infectious pathogen, has been epidemiologically associated with dyspepsia, peptic ulcer disease, and gastric carcinogenesis. However, comprehensive regional epidemiological data regarding infection prevalence, antimicrobial resistance patterns, and associated risk factors remain insufficient in the Bortala MongolianAutonomous Prefecture of Xinjiang, China.

Current status of  infection and prevalence of resistance-associated gene mutations in Bortala Mongolian Autonomous Prefecture, Xinjiang: a single-center study.

ArticleTotal : 4	Year
Publication title Cited by 8	2023
Publication title	2025
Publication title Cited by 1	2025
Publication title	2025

Article	Year
Rapid discrimination of spp. and label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms. Frontiers in microbiology\| PubMed ID: 36970678	2023
Identification of Resistance to Common Antibiotics: An Overview of Current Methods and Techniques. Infection and drug resistance\| PubMed ID: 38628245	2024
Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-colle... Biosensors & bioelectronics\| PubMed ID: 38943854	2024
Comparison of Raman spectroscopy with mass spectrometry for sequence typing of strains: a single-center study. Microbiology spectrum\| PubMed ID: 39907463	2025
Development and evaluation of a novel rapid fungal nucleic acid extraction kit. Archives of microbiology\| PubMed ID: 39992430	2025
Risk factors associated with Helicobacter pylori infection in the urban population of China: A nationwide, multi-center, cross-sectional study. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases\| PubMed ID: 40096882	2025
Detection of Helicobacter pylori Infection and Antibiotic Resistance via String Test Coupled With qPCR Method: A Comparative Methodological Study. Journal of gastroenterology and hepatology\| PubMed ID: 40170571	2025
Current status of infection and prevalence of resistance-associated gene mutations in Bortala Mongolian Autonomous Prefecture, Xinjiang: a single-center study. Frontiers in microbiology\| PubMed ID: 41178969	2025

Zheng-Kang Li

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