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S. aureus is a widespread Gram-positive conditional pathogen that colonizes the nasal mucosa of healthy individuals1,2. When host immunity is compromised, it can cause local skin infections, deep tissue invasion, or systemic diseases (e.g., pneumonia, endocarditis, osteomyelitis, and bacteremia)3. Methicillin, a semisynthetic penicillin, was clinically applied for S. aureus infections in 1959, and two years later, MRSA emerged in the UK, rapidly spreading to European countries including Denmark, France, and Switzerland4. MRSA resistance to β-lactam antibiotics not only complicates clinical management but also significantly elevates infection risks5. Multiple factors-including uncontrollable colonization/spread, high prevention costs, and antibiotic overuse-have collectively driven up MRSA infection rates6. Owing to its rapid transmission and complex resistance mechanisms, MRSA infections are associated with high case-fatality rates, with nearly 150,000 reported cases and >7,000 deaths annually in EU countries7. According to CHINET surveillance data, MRSA infection rates in China have remained above 30% for the past five years8. The high risk of nosocomial cross-infection further intensifies public health burdens and strains healthcare systems9,10. Therefore, the development of efficient and precise rapid MRSA detection technologies is clinically urgent for achieving early diagnosis, personalized therapy, reducing mortality, and interrupting transmission chains.
Current detection technologies for S.aureus and MRSA span multiple fields including phenotypic detection, genomic detection, transcriptomic detection, proteomic detection, mass spectrometry technology, and bioinformatics-based analyses11.
Phenotypic detection methods, such as the disk diffusion method (KB method) and minimum inhibitory concentration method (MIC method), are recommended as "gold standards" by the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST). These methods have the advantages of no need for specialized instruments, being widely used for resistance spectrum surveillance and epidemiological investigation. However, their disadvantages are also evident: long culture periods (over 24 h), and the results of the KB method for some new antibiotics require validation by MIC values, with results for breakpoint strains being susceptible to human factors12,13.
Among genomic-level detection methods, conventional PCR has the characteristics of high sensitivity and strong specificity, enabling preliminary judgment of resistance and shortening experimental time, with low cost and high efficiency. But its limitations lie in being able to detect only known resistance genes and relying on specialized instruments14. The mechanism of microbial resistance is highly complex, making it crucial to develop technical methods that can cover unknown resistant phenotypes and enable rapid identification. The emerging GoPhAST-R (Combined genotypic and phenotypic AST through RNA detection) technology determines resistance phenotypes by detecting mRNA expression levels, enabling simultaneous detection of different resistance-related genes across multiple pathogens, shortening experimental turnaround time, and achieving 94-99% accuracy when combined with machine learning15.
Among proteomic-level detection methods, the PBP2a latex agglutination method is based on antigen-antibody specific binding, with simple operation and rapid results, having high specificity and sensitivity for MRSA. However, it cannot differentiate between different species of staphylococci, and the detection sensitivity of commercial kits has some decrease, requiring re-testing after induction with oxacillin8.
MALDI-TOF MS-based detection methods, such as the DOT-MGA (DNA Oligonucleotide Tag-Microarray Detection of Genomic Amplification) method, achieve optimal analytical performance at 6-8 hours of incubation, with a detection rate as high as 96.4%, and both sensitivity and specificity are 100%. But the cost is high, and the limited positions on the target plate make it difficult to simultaneously detect hundreds of samples16,17.
Among bioinformatics analysis technologies combined with omics data for resistance prediction, whole-genome sequencing technology stands out with its high resolution and comprehensive information analysis capabilities. Combined with resistance gene analysis databases like Resfinder, it can analyze whether strains carry specific resistance genes, predict resistance potential, detect single-nucleotide polymorphisms (SNPs) and resistance-associated gene mutants, while supporting storage and reanalysis of whole-genome data18. However, this method requires operation by professionals with bioinformatics backgrounds, is rarely used in daily testing, and is commonly used for epidemiological analysis or molecular surveillance of pathogens19.
The aforementioned methodologies are either high in cost, time-consuming, or still in the research and development phase. Clinically, there is an urgent need for a solution balancing "immediacy" and "accuracy" - a novel multiplex fluorescent PCR technology fills this gap by simultaneously amplifying nuc/mecA genes in a single tube, compressing the detection cycle to 1 h. It precisely bridges the time window gap in the traditional "emergency screening - resistance typing" process, providing a more time-efficient molecular diagnostic tool for nosocomial infection prevention and control. This specific PCR assay is primarily utilized for the detection of respiratory specimens, with sputum specimens as a typical example. It exhibits a sensitivity of 500 copies/mL and high specificity, while remaining unaffected by antibacterial agents. Owing to these favorable performance characteristics, this PCR assay holds substantial application value for the diagnosis of respiratory tract infections and the surveillance of nosocomial infections caused by MRSA.