Application of Broad-Spectrum, Sequence-Based Pathogen Identification in an Urban Population Multiple Pathogen Detection
2007

Broad-Spectrum Pathogen Detection in Urban Populations

Sample size: 424 publication Evidence: high

Author Information

Author(s): Lin Baochuan, Malanoski Anthony P., Wang Zheng, Blaney Kate M., Ligler Adam G., Rowley Robb K., Hanson Eric H., von Rosenvinge Erik, Ligler Frances S., Kusterbeck Anne W., Metzgar David, Barrozo Christopher P., Russell Kevin L., Tibbetts Clark, Schnur Joel M., Stenger David A.

Primary Institution: Center for Bio/Molecular Science and Engineering, Naval Research Laboratory

Hypothesis

Can a broad-spectrum pathogen detection platform improve the identification of respiratory infections in urban populations?

Conclusion

The RPM v.1 microarray demonstrated high sensitivity and specificity for detecting multiple respiratory pathogens, confirming its utility for medical surveillance.

Supporting Evidence

  • The RPM v.1 showed ≥98% overall agreement with reference methods for all detected organisms.
  • Influenza A was the most commonly identified pathogen, with 63.4% positive results.
  • The RPM v.1 identified co-infections in 13.7% of the samples.
  • Detection sensitivity for influenza A was 99% and specificity was 96%.
  • Phylogenetic analysis confirmed the presence of two major circulating strains of influenza A.

Takeaway

This study shows a new test can find many germs that make people sick, helping doctors know what to treat.

Methodology

The study analyzed nasal wash specimens using the RPM v.1 microarray and compared results with conventional culture and PCR methods.

Potential Biases

Potential biases may arise from the reliance on archived samples and the limitations of the reference assays used.

Limitations

The RPM v.1 does not cover all respiratory pathogens, and some samples still tested negative despite flu-like symptoms.

Participant Demographics

Participants were recruited from military treatment facilities in the Washington, DC metropolitan area, representing a broad age, gender, and geographic distribution.

Statistical Information

P-Value

p<0.05

Statistical Significance

p<0.05

Digital Object Identifier (DOI)

10.1371/journal.pone.0000419

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