For example, whether one is identifying anthrax on the farm, from the environment, or in a patient’s blood stream, the identification can be quickly made using a fairly easily agreed upon set of standard genomic and immunological reagents. Subsequently, there must be cultures of microorganisms grown in the laboratory using agreed upon standard methods. The identification should be based on uniform standards and not a free-for-all depending on program officers or agencies with differing views.

To date, a disproportionate amount of the effort in the bioagent detection arena has been focused on the development of technology platforms. Efforts on standardization or validation of sample collection and sample processing procedures, as well as on test validation in a real-world setting, have had much lower priority. But the use of genomic and proteomic information, as well as the development of robotic sensing devices that can communicate signals from many environmental sites, offers new possibilities for the early detection of biologic agents in the environment. It also increases the risk of false alarms when sophisticated analysis and decision-making systems are lacking.

Another challenge involves creating broad-spectrum detection tools and methods. Currently a large number of tests rely on a small number of specific antibodies or microbial genomic sequences. This reliance creates vulnerabilities—for example, with respect to bioagents having modified antibody epitopes (binding sites) or sequences. Rather than relying on methods that target specific, known organisms, one would like to have detection methods that target groups of organisms (i.e., all members of these groups) and that can identify specific members of the group, including recognition of those that may not yet have been characterized. Although there are experimental challenges, the expertise exists to immediately begin addressing these problems (Cummings 2000, 2002; Nikkari et al., 2002).

A further challenge is the need for highly sensitive systems, as some highly infectious pathogens require the inhalation of only 1 to 10 organisms to cause disease. In general, much greater attention is needed to translate basic laboratory research into field applications and clinical validation (standards will play an important role; see Recommendation 3.16 and surrounding discussion). Finally, because no test is perfect, it is important to be able to anticipate false-positive test results in a reliable and quantitative fashion. One potential strategy for minimizing the impact of false-positive test results is to create a system of multiple, parallel, independent technical platforms so as to avoid dependence on any one testing procedure. This requires crosscutting, interdisciplinary science (e.g., combining environmental microbiology, cell biology, biophysics, electronics, materials science and microfabrication, microfluidics, and bioinformatics/statistics) and would require collaboration between several federal agencies and industry. However, even the currently available tests could be made significantly more useful by adopting a quality assurance index that would be applied to any positive test result. For example, single positives in tests with high false-positive rates, such



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