Development and clinical validation of a novel multiplex PCR test for detection of respiratory pathogens via fluorescence melting curve analysis

利用荧光熔解曲线分析法开发和临床验证一种用于检测呼吸道病原体的新型多重PCR检测方法

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Abstract

Respiratory infections caused by pathogens such as SARS-CoV-2, influenza viruses, and Mycoplasma pneumoniae pose significant diagnostic challenges due to overlapping clinical symptoms, particularly during seasonal outbreaks. Conventional single-pathogen tests and commercial multiplex PCR kits are often costly, time-consuming, and lack flexibility for resource-limited settings. To address these limitations, we developed a novel fluorescence melting curve analysis-based (FMCA-based) multiplex PCR assay for simultaneous detection of six respiratory pathogens, including SARS-CoV-2, influenza A virus (IAV), influenza B virus (IBV), M. pneumoniae, respiratory syncytial virus (RSV), and human adenovirus (hADV). The method was evaluated for its limit of detection (LOD), precision and specificity before clinical validation, followed by a prospective single-center study in patients with presumptive acute respiratory infections. Analytical validation revealed high sensitivity with limits of detection (LOD) between 4.94 and 14.03 copies/µL, exceptional precision (intra-/inter-assay CVs ≤ 0.70% and ≤ 0.50%), and no cross-reactivity. Clinical evaluation using 1005 samples demonstrated 98.81% agreement with RT-qPCR, identifying 51.54% pathogen-positive cases, including 6.07% co-infections. The assay resolved 12 discordant results via sanger sequencing, confirming superior sensitivity in low viral load scenarios. With a turnaround time of 1.5 h and a cost of $5/sample (86.5% cheaper than commercial kits), this scalable platform enables high-throughput screening during outbreaks. Our findings highlight its potential as a cost-effective, rapid diagnostic tool for differentiating respiratory pathogens, guiding clinical management, and enhancing surveillance in diverse healthcare settings. Further adaptations to include emerging variants and sample types could strengthen global preparedness for respiratory epidemics.

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