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Molecular diagnostic test for acute respiratory viral infections

Molecular diagnostic test for acute respiratory viral infections

Unmet Need

Acute respiratory viral (ARV) infections are among the most common reasons for patient visits in primary and acute care settings. ARV can be caused by many viruses including human rhinovirus (HRV), respiratory syncytial virus (RSV) and influenza. These viruses can be associated with a range of clinical severity from being largely asymptomatic to mild, self-limited illness to respiratory failure and death. Influenza alone causes 25 to 50 million infections annually in the USA, resulting in several hundred thousand hospitalizations and 20-40,000 deaths. Despite viral etiologies driving most cases of acute respiratory infection, definitive diagnostic tools for these syndromes are lacking. Even highly sensitive pathogen-specific tests such as PCR are dependent upon proper sampling technique and inclusion of virus-type-specific reagents and processing methods. Moreover, detection of a specific microbe in a clinical sample does not necessarily indicate the cause of the acute clinical syndrome. Therefore, better tools that help providers define the etiology of a suspected infectious syndrome in a safe, rapid, accurate, and cost-effective manner are of great importance for both individual and public health.


Duke researchers developed a molecular diagnostic test that overcomes many of the limitations of current methods for the determination of the etiology of respiratory infection. The test detects the host's response to an ARV infection by measuring and analyzing the expression of a discrete set of proteins or component peptides in nasal samples. The proteins or peptides in this “signature”, revealed by statistical analysis, are differentially expressed in individuals presenting with an ARV infection. Monitoring the host response to ARV infection using this multi-analyte test in conjunction with analytic methods provides a classifier of high diagnostic accuracy and clinical utility, allowing health care providers to use the response of the patient to reliably determine the presence or absence of a respiratory viral infection.


  • Allows classification of ARV infection with a high degree of accuracy.
  • Has the potential for safe, simple, rapid, and inexpensive pre-symptomatic detection and identification of viral infection.
  • Was tested on influenza A H3N2 and HRV samples collected from human subjects.

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