A distributed national network for label-free rapid identification of emerging pathogens

J. Paul Robinson; Bartek P. Rajwa; M. Murat Dundar; Euiwon Bae; Valery Patsekin; E. Daniel Hirleman; Ali Roumani; Arun K. Bhunia; J. Eric Dietz; V. Jo Davisson; John G. Thomas

doi:10.1117/12.884619

3 June 2011 A distributed national network for label-free rapid identification of emerging pathogens

J. Paul Robinson, Bartek P. Rajwa, M. Murat Dundar, Euiwon Bae, Valery Patsekin, E. Daniel Hirleman, Ali Roumani, Arun K. Bhunia, J. Eric Dietz, V. Jo Davisson, John G. Thomas

Author Affiliations +

Proceedings Volume 8018, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XII; 80180C (2011) https://doi.org/10.1117/12.884619
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Typical bioterrorism prevention scenarios assume well-known and well-characterized pathogens like anthrax or tularemia, which are serious public concerns if released into food and/or water supplies or distributed using other vectors. Common governmental contingencies include rapid response to these biological threats with predefined treatments and management operations. However, bioterrorist attacks may follow a far more sophisticated route. With the widely known and immense progress in genetics and the availability of molecular biology tools worldwide, the potential for malicious modification of pathogenic genomes is very high. Common non-pathogenic microorganisms could be transformed into dangerous, debilitating pathogens. Known pathogens could also be modified to avoid detection, because organisms are traditionally identified on the basis of their known physiological or genetic properties. In the absence of defined primers a laboratory using genetic biodetection methods such as PCR might be unable to quickly identify a modified microorganism. Our concept includes developing a nationwide database of signatures based on biophysical (such as elastic light scattering (ELS) properties and/or Raman spectra) rather than genetic properties of bacteria. When paired with a machine-learning system for emerging pathogen detection these data become an effective detection system. The approach emphasizes ease of implementation using a standardized collection of phenotypic information and extraction of biophysical features of pathogens. Owing to the label-free nature of the detection modalities ELS is significantly less costly than any genotypic or mass spectrometry approach.

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J. Paul Robinson, Bartek P. Rajwa, M. Murat Dundar, Euiwon Bae, Valery Patsekin, E. Daniel Hirleman, Ali Roumani, Arun K. Bhunia, J. Eric Dietz, V. Jo Davisson, and John G. Thomas "A distributed national network for label-free rapid identification of emerging pathogens", Proc. SPIE 8018, Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing XII, 80180C (3 June 2011); https://doi.org/10.1117/12.884619

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