Application of a Super‐Multiplex Microfluidic qPCR System for Detection of Respiratory Pathogens in Patients With Influenza‐Like Illness
Respiratory tract infections (RTIs) pose a major diagnostic challenge, complicated by multiple microbial etiologies. Conventional low-throughput methods may underestimate the prevalence and impact of co-infections. This study aimed to evaluate a novel super-multiplex microfluidic qPCR system for comprehensive 49 target pathogens screening and co-infection analysis in patients with influenza-like illness (ILI). Throat swab samples (n = 288) were collected from febrile patients in Beijing during the spring of 2025. All samples were tested using the super-multiplex microfluidic qPCR chip, with a subset (n = 96) validated by conventional qPCR to determine analytical sensitivity, specificity, and Cohen's kappa. The assay demonstrated an overall pathogen detection rate of 66.32%. Influenza A virus (IAV) was the most prevalent pathogen, identified in 50.00% of all samples and constituting 70.69% of single infections, followed by SARS-CoV-2 (14.24%) and Epstein-Barr virus (EBV, 11.81%). Co-infections were frequent (39.27% of positive cases), predominantly dual (23.04%) and triple (10.47%) infections. Notably, IAV was implicated in 84.0% of co-infections. The microfluidic qPCR showed high concordance with conventional qPCR (sensitivity 91.07%, specificity 92.50%, κ = 0.83, indicating almost perfect agreement). This study reveals a complex respiratory pathogen landscape dominated by IAV and marked by frequent polymicrobial co-infections. The super-multiplex microfluidic qPCR chip is a reliable, high-throughput platform that effectively addresses the need for improved detection of co-infections, offering valuable insights for both clinical decision-making and public health surveillance.
Authors
- Xiaoping Kang (ORCID: https://orcid.org/0000-0002-0587-399X)
- Y Huang
- Tao Jiang (ORCID: https://orcid.org/0000-0003-1908-2926)
- Jing Li (ORCID: https://orcid.org/0000-0002-6860-6135)
- Yi Chen (ORCID: https://orcid.org/0000-0002-2225-690X)
- Gao Y (ORCID: https://orcid.org/0000-0003-2059-3168)
- Ye Feng
- Yuchang Li
- Sen Zhang
- Ganji Song
Institutions
- Academy of Military Medical Sciences (CN)
- Chinese PLA General Hospital (CN)
Publication Details
- Journal
- Journal of Medical Virology
- Published
- 2026-07-27
- DOI
- https://doi.org/10.1002/jmv.71072
- Primary Topic
- Biosensors and Analytical Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00