Identification of Disease-Specific Spectral Signals
Company subjects the compiled data of both the Standard Spectral
Survey and Advanced Spectral Profile to multiple analytical
strategies to identify spectral patterns and further characterize
spectral differences between normal and disease subjects. Most
spectral comparative computational methods employ multivariate
analytical strategies that isolate key differentiating features
of the spectra. Comparative methods include simple comparison
of selected spectral variables, comprehensive methods that fully
analyze underlying spectral features, as well as computationally
'intelligent' methods such as a neural net.
Figure 1. Detection of a
discriminatory intrinsic fluorescence spectral signal that differentiates
between normal and HCV-infected human plasma samples. (See: 
Transfusion vol. 41(9), p77-78S)
This information provides the definition of a discriminatory spectral
difference between a diseased and healthy cohort, and establishes
the initial package of proprietary information supporting the
out-licensing or further pursuit of the molecular determinants
of the spectral difference. Recognizing that any spectral difference
is a manifestation of an underlying molecular difference, the
establishment of a definitive and reliable spectral difference
represents valuable information for internal product development
decision making and to prospective partners.
addition to providing a decisive demonstration of discrimination
between groups, these results provide the foundation for selecting
detection methods and parameters for isolating the underlying
indicator molecules. In some cases, like the results from SerOptix'
HCV studies, these spectral features may offer sufficient discriminatory
power for development of a direct detection assay (i.e. the ID-LBS).
SerOptix will evaluate the potential of any spectral discriminators
as parameters for the development of a direct assay based on the
level of discrimination and specific business opportunity offered.
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