Zheng-Kang Li

Background: The Xpert MTB/RIF (Xpert) assay is a cornerstone for the rapid detection of rifampicin resistance. However, discordant results between Xpert and phenotypic drug susceptibility testing (pDST) present diagnostic challenges, particularly in regions with genetically diverse Mycobacterium tuberculosis populations. This study aimed to evaluate the performance of Xpert at the probe level to resolve such discrepancies and to inform an optimized diagnostic workflow. Methods: Using a retrospective cohort of 3,360 clinical specimens from a high-burden setting in China, we compared Xpert results with pDST as the reference standard. We then performed a stratified analysis based on specific probe mutation patterns to determine their predictive value for phenotypic resistance. Results: Overall, Xpert demonstrated substantial agreement with pDST ( κ = 0.722, 95% CI: 0.685–0.759), exhibiting a sensitivity of 80.4% and a specificity of 95.8%. However, concordance was highly dependent on the specific probe mutation. Mutations in Probe E were strongly predictive of phenotypic resistance (Positive Predictive Value: PPV: 91.4%). In contrast, The mutations detected by Probe ΔCt and Probe A exhibited a low PPV of only 28.0% and 14.3%, respectively. Based on these findings, we propose a diagnostic triage strategy: results indicating Probe E mutations can reliably guide immediate treatment adjustments, whereas isolated ProbeΔCt resistance, or Probe A mutations, warrant confirmatory pDST before initiating second-line regimens. Conclusion: The clinical interpretation of Xpert results requires a nuanced understanding of probe-specific mutation patterns. Our probe-stratified analysis and the resulting triage algorithm provide a practical framework for optimizing the management of rifampicin-resistant tuberculosis (RR-TB). This approach effectively balances the need for diagnostic speed with therapeutic accuracy in programmatic settings.

Chikungunya fever (CHIKF), an acute viral illness transmitted by mosquitoes and first discovered in Tanzania in 1952, has shown intermittent spread in China, with increasing local outbreaks. By July 2025, Guangdong Province, currently the outbreak's center, had reported 4,824 local cases, including nearly 3,000 new infections in just one week. This rapid surge creates an urgent need for advanced diagnostic technologies. Such tools are critical to support containment efforts on the front lines. We propose a solution for the rapid simultaneous detection of dengue (DENV), chikungunya (CHIKV), and Zika virus (ZIKV) infections to enhance early epidemic prevention and control capabilities. This approach involves developing customized testing protocols for border checkpoints and primary healthcare facilities. Traditional diagnostic methods for CHIKV and DENV often lack sensitivity/specificity in coinfection cases. Since CHIKV, DENV, and ZIKV share similar transmission patterns and symptoms, simultaneous monitoring is crucial. This protocol presents a triplex qPCR method using TaqMan probes to detect these arboviruses in a single tube within 90 min. Compared to viral culture and serological tests, this method improves efficiency by identifying active infections and coinfections with high specificity while simplifying workflow and eliminating specialized equipment requirements. Validation with clinical and External Quality Assessment (EQA) samples confirmed 100% concordance with gold-standard singleplex assays. This method serves as an effective tool for monitoring febrile illnesses in endemic regions, particularly in resource-limited settings where rapid outbreak containment is critical.

Chikungunya fever (CHIKF), an acute viral illness transmitted by mosquitoes and first discovered in Tanzania in 1952, has shown intermittent spread in China, with increasing local outbreaks. By July 2025, Guangdong Province, currently the outbreak's center, had reported 4,824 local cases, including nearly 3,000 new infections in just one week. This rapid surge creates an urgent need for advanced diagnostic technologies. Such tools are critical to support containment efforts on the front lines. We propose a solution for the rapid simultaneous detection of dengue (DENV), chikungunya (CHIKV), and Zika virus (ZIKV) infections to enhance early epidemic prevention and control capabilities. This approach involves developing customized testing protocols for border checkpoints and primary healthcare facilities. Traditional diagnostic methods for CHIKV and DENV often lack sensitivity/specificity in coinfection cases. Since CHIKV, DENV, and ZIKV share similar transmission patterns and symptoms, simultaneous monitoring is crucial. This protocol presents a triplex qPCR method using TaqMan probes to detect these arboviruses in a single tube within 90 min. Compared to viral culture and serological tests, this method improves efficiency by identifying active infections and coinfections with high specificity while simplifying workflow and eliminating specialized equipment requirements. Validation with clinical and External Quality Assessment (EQA) samples confirmed 100% concordance with gold-standard singleplex assays. This method serves as an effective tool for monitoring febrile illnesses in endemic regions, particularly in resource-limited settings where rapid outbreak containment is critical.

Background: Helicobacter pylori (H. pylori), 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.Methods: A cross-sectional study was conducted from June 4 to June 9, 2023, enrolling 341 participants through systematic sampling in Bortala Prefecture. Diagnostic procedures combined non-invasive urea capsule sampling with genotypic detection techniques for simultaneous H. pylori identification and antimicrobial resistance profiling. Multivariate logistic regression analysis was performed on demographic and behavioral variables obtained through standardized questionnaires.Results: The study revealed an H. pylori infection prevalence of 29.03% (99/341; 95% CI: 24.13–34.34%). Genotypic resistance analysis demonstrated resistance rates of 51.52% (51/99; 95% CI: 41.72–61.32%) for clarithromycin and 46.46% (46/99; 95% CI: 36.66–56.26%) for levofloxacin. Female participants showed a marginally elevated infection risk (OR = 1.27, p = 0.35), while communal cup-sharing behavior demonstrated borderline significance as a risk factor (OR = 1.95, p = 0.09). No statistically significant independent risk factors were identified in the multivariate analysis.Conclusion: The Bortala Prefecture exhibits alarming genotypic resistance patterns to first-line antibiotics, despite a moderate H. pylori infection prevalence. These findings underscore the critical need for region-specific antimicrobial stewardship and evidence-based treatment protocols guided by local resistance profiles. Future research should prioritize stratified sampling methodologies to delineate ethnicity-specific risk profiles and validate potential risk factors in larger cohorts.

Staphylococcus aureus (S.aureus) is a common Gram-positive human pathogen that causes community-acquired and nosocomial infections, with diverse clinical manifestations ranging from local superficial lesions and food poisoning to fatal systemic infections. The discovery of antibiotics significantly reduced the mortality rate, but the problem of drug resistance has since become increasingly prominent. Since the first identification of MRSA in 1960, this strain has emerged as a global public health threat. MRSA is a major pathogen of nosocomial infections, capable of causing various severe diseases such as endocarditis, chronic osteomyelitis, pneumonia, pyogenic arthritis, and bacteremia. Therefore, rapid and accurate detection of S. aureus and its drug resistance is crucial for guiding clinical treatment. Current routine detection methods for S.aureus and MRSA have significant limitations. The traditional bacterial culture method, serving as the "gold standard" for decades, can provide definitive species identification and drug sensitivity results, but the process is time-consuming, taking 48 to 72 h. Furthermore, this method is susceptible to contamination and relies on specialized laboratory facilities and skilled technicians. Serological testing achieves non-invasive diagnosis by detecting S.aureus antibodies in patient serum, but it cannot distinguish between active infections and past infections, nor can it identify drug-resistant strains (such as MRSA). This study focuses on developing a novel multiplex real-time fluorescence PCR detection method to overcome the above-mentioned limitations. This method designs specific primers and TaqMan fluorescent probes targeting the species-specific nuc gene of S.aureus and the mecA gene mediating methicillin resistance, enabling simultaneous amplification and detection of S.aureus and MRSA in a single reaction system. This technology greatly reduces the detection time, providing a rapid, accurate, and cost-effective solution for S.aureus and MRSA detection. This innovative approach greatly improves clinical diagnostic efficiency and facilitates the early implementation of targeted antibiotic therapy, making important contributions to controlling drug-resistant bacterial infections.

Staphylococcus aureus (S.aureus) is a common Gram-positive human pathogen that causes community-acquired and nosocomial infections, with diverse clinical manifestations ranging from local superficial lesions and food poisoning to fatal systemic infections. The discovery of antibiotics significantly reduced the mortality rate, but the problem of drug resistance has since become increasingly prominent. Since the first identification of MRSA in 1960, this strain has emerged as a global public health threat. MRSA is a major pathogen of nosocomial infections, capable of causing various severe diseases such as endocarditis, chronic osteomyelitis, pneumonia, pyogenic arthritis, and bacteremia. Therefore, rapid and accurate detection of S. aureus and its drug resistance is crucial for guiding clinical treatment. Current routine detection methods for S.aureus and MRSA have significant limitations. The traditional bacterial culture method, serving as the "gold standard" for decades, can provide definitive species identification and drug sensitivity results, but the process is time-consuming, taking 48 to 72 h. Furthermore, this method is susceptible to contamination and relies on specialized laboratory facilities and skilled technicians. Serological testing achieves non-invasive diagnosis by detecting S.aureus antibodies in patient serum, but it cannot distinguish between active infections and past infections, nor can it identify drug-resistant strains (such as MRSA). This study focuses on developing a novel multiplex real-time fluorescence PCR detection method to overcome the above-mentioned limitations. This method designs specific primers and TaqMan fluorescent probes targeting the species-specific nuc gene of S.aureus and the mecA gene mediating methicillin resistance, enabling simultaneous amplification and detection of S.aureus and MRSA in a single reaction system. This technology greatly reduces the detection time, providing a rapid, accurate, and cost-effective solution for S.aureus and MRSA detection. This innovative approach greatly improves clinical diagnostic efficiency and facilitates the early implementation of targeted antibiotic therapy, making important contributions to controlling drug-resistant bacterial infections.

Helicobacter pylori (H. pylori) is widely prevalent worldwide, with approximately 50% of the global population having a history of H. pylori infection. In China, the infection rate ranges from 40% to 70%. H. pylori is primarily associated with gastrointestinal diseases such as chronic gastritis, gastric ulcers, and duodenal ulcers. Currently, the clinical treatment for H. pylori infection involves either triple or quadruple therapy. However, the extensive use of antibiotics has led to the development of antibiotic resistance in H. pylori. Therefore, detecting both H. pylori and its antibiotic resistance is crucial for guiding clinical treatment. Diagnostic methods for H. pylori include urea breath test (UBT), antigen test, serological antibody test, endoscopy, rapid urease test (RUT), and bacterial culture. While the first three methods are non-invasive, they do not allow for bacterial recovery and, thus, cannot be used for resistance testing. The latter three methods are invasive, expensive, require high technical expertise, and may cause harm to patients. Therefore, a non-invasive, rapid method for simultaneous detection of H. pylori infection and antibiotic resistance is of utmost clinical importance for the effective eradication of H. pylori. This paper aims to introduce a specific protocol that combines quantitative polymerase chain reaction (qPCR) with TaqMan fluorescent probe technology to rapidly detect H. pylori infection and antibiotic resistance. This method provides a convenient, rapid, and non-invasive way to diagnose H. pylori infection and resistance, unlike traditional bacterial culture and other techniques. qPCR is used to identify the infection and detect mutations in the 23S rRNA and gyrA genes, which are linked to resistance to clarithromycin and quinolones, respectively. Compared to conventional culture techniques, this approach offers a non-invasive, cost-effective, and time-efficient method for detecting Helicobacter pylori infection and determining its antibiotic resistance.

Helicobacter pylori (H. pylori) is widely prevalent worldwide, with approximately 50% of the global population having a history of H. pylori infection. In China, the infection rate ranges from 40% to 70%. H. pylori is primarily associated with gastrointestinal diseases such as chronic gastritis, gastric ulcers, and duodenal ulcers. Currently, the clinical treatment for H. pylori infection involves either triple or quadruple therapy. However, the extensive use of antibiotics has led to the development of antibiotic resistance in H. pylori. Therefore, detecting both H. pylori and its antibiotic resistance is crucial for guiding clinical treatment. Diagnostic methods for H. pylori include urea breath test (UBT), antigen test, serological antibody test, endoscopy, rapid urease test (RUT), and bacterial culture. While the first three methods are non-invasive, they do not allow for bacterial recovery and, thus, cannot be used for resistance testing. The latter three methods are invasive, expensive, require high technical expertise, and may cause harm to patients. Therefore, a non-invasive, rapid method for simultaneous detection of H. pylori infection and antibiotic resistance is of utmost clinical importance for the effective eradication of H. pylori. This paper aims to introduce a specific protocol that combines quantitative polymerase chain reaction (qPCR) with TaqMan fluorescent probe technology to rapidly detect H. pylori infection and antibiotic resistance. This method provides a convenient, rapid, and non-invasive way to diagnose H. pylori infection and resistance, unlike traditional bacterial culture and other techniques. qPCR is used to identify the infection and detect mutations in the 23S rRNA and gyrA genes, which are linked to resistance to clarithromycin and quinolones, respectively. Compared to conventional culture techniques, this approach offers a non-invasive, cost-effective, and time-efficient method for detecting Helicobacter pylori infection and determining its antibiotic resistance.

The rapid sequence typing (ST) of bacterial strains is crucial for effective nosocomial infection control and mitigating the spread of nosocomial pathogens, e.g., Acinetobacter baumannii . While accurate in identifying A. baumannii 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 A. baumannii sequence typing based on SERS spectra. The objective was to assess the feasibility of this integrated method for efficient sequence typing of A. baumannii strains. Clinically isolated A. baumannii strains ( N = 267) were collected from a single hospital between 2013 and 2023. Based on multilocus sequence typing, 39 STs of A. baumannii 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 A. baumannii 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 A. baumannii 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. IMPORTANCE: The rapid and accurate sequence typing (ST) of bacterial pathogens is pivotal in controlling transmission within healthcare settings. Acinetobacter baumannii infection, known for its high transmissibility and drug resistance, presents a major challenge in nosocomial infection control. In this study, surface-enhanced Raman spectroscopy (SERS) was used to differentiate A. baumannii strains with distinct STs based on unique Raman spectral profiles. We then constructed and compared eight machine-learning models on SERS spectra to quickly identify bacterial STs. The results showed that the support vector machine model outperformed matrix-assisted laser desorption/ionization time-of-flight mass spectrometer in determining A. baumannii STs. This approach enables rapid identification of A. baumannii variants with different STs, supporting the early detection and control of nosocomial infections by this multidrug-resistant pathogen.