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Predictive Medicine by Cytomics: former:
Cell Biochemistry Group
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Diseases are caused by molecular changes in cellular systems or organs. They are induced by exposure to external influences like microorganisms, allergens, toxic substances a.o. or alternatively by genetic disposition or genetic aberrations. Disease course prediction ( >95% correct) for individual patients is usually considered impossible for the majority of diseases. Exceptions concern e.g. genotypic aberrations detected during amniocentesis or preimplantation diagnostics (PID).
The substantial clinical interest in predicting the future disease development in individual patients prompts for the search of the disease relevant molecular information at the cellular level.
Considering genomics or proteomics for genome or proteome analysis, the high multiparametric complexity and the usual preanalytic content mixing from different cell populations in cell or tissue extracts, constitute substantial drawbacks for predictive conclusions in common diseases like infections, allergies, malignancies, intoxications, degenerative disease a.o.
Cytomics, the multimolecular cytometric analysis of the cellular heterogeneity of cytomes (cellular systems/organs/body), access a maximum of information on the apparent molecular cell phenotype as it results from cell genotype and exposure.
The cell phenotypes in the naturally existing cellular and cell population heterogeneity of disease affected body cytomes contain the information on the future development (prediction) as well as on the present status (diagnosis) of a disease.
Data classifications are considered predictive for individual patients at predictive values >95% for each classified disease category of the learning set while they are prognostic at values <95%.
a.) multiparametric cytometric
determination of
functions
or constituents in disease associated cytomes
b.) exhaustive
analysis
(1,
2)
of all measured numeric parameters
for all cell populations (i.e. in practice for >95% of the
collected cells)
c.) data pattern classification
of this entire information against the future disease course (disease progression)
of patients during the learning phase
d.) classification of the embedded test set of patient
data, measured under the same conditions as the learning set but
remaining unknown to the learning process. Typically, every
5th or 10th patient is assigned to the test set prior to the
learning phase to exclude classification biases.
e.) prospective classification of data collected from
subsequent new patients during the clinical evaluation phase
Patterns of various biomolecules can be reliably quantitated by cytometric
analysis of viable or fixed cells following staining with biochemically
specific fluorescent dyes. The particular effort of this laboratory
consists in the development of specific stains for
cell functions
in viable cells as sensitive indicators of the altered cellular
metabolism in acute or chronic disease. The simultaneous
multiparameter data collection by the cytometer provides high amounts
of functional and structural information on
heterogeneous i.e. essentially unprocessed ex-vivo cell suspensions
shortly after removal from the human body.
The cellular heterogeneity of human samples offers important
advantages for clinical and experimental
system cytometry (cytomics)
because the high information content of simultaneously
collected multiparameter data from a great variety of different
cell types can be utilized. The cytometric strategy is
explicitely to measure as much heterogeneity as possible to
profit during evaluation from the high information content of
biocomplexity. The cytometric approach is therefore quite
different from the tissue biochemistry approach where
one tries to reach as much homogeneity as possible e.g. by
the isolation of groups of similar cells by laser microdissection
to assure unambiguous interpretation of experimental results.
The exhaustive extraction of information from cytometric or clinical chemistry multiparameter measurements by a laboratory and instrument independent, self learning and standardized data classification algorithm, developed earlier (ref#4), provides accurate single patient disease course prediction, as well as diagnostics in case of sufficiently information rich molecular data or other patient data.
Clinical examples
from several medical disciplines underline this point.
Predictive medicine by cytomics represents Evidence Based Medicine
(EBM) at the cellular level.
The practical consequence of this approach is that complications
in a number of common diseases like severe infections, shock,
exacerbation of rheumatoid and asthmatic disease,
thromboembolic complications in diabetes, myocardial infarction
and stroke sensitive patients or survival in cancer patients,
but also e.g. complications in bone marrow stem cell transplantation
(BM-SCT) will become increasingly predictable at the individual
patient level.
Minor interventions like cytometry supervised short term
antiphlogistic therapy e.g. just prior to an imminent exacerbation of
rheumatoid disease may prevent severe tissue destruction leading otherwise
to the stepwise disabling of the patient by deficient repair processes.
The cell biochemical approach has in this case the potential to
significantly postpone the invalidization of patients. The higher
quality of patients's life would be paralleled by shorter disease periods
at substantially lower therapy costs and a lower number
of unwanted therapeutic side effects and adverse drug reactions (ADRs)
(Optimized Medicineby Medical or Clinical Cytomics).
Purdue CD-Series)
=external links
CD1 1996(1)
ISBN: none |
![]() ISSN: 1091-2037 |
![]() ISBN: 1-890473-02-2 |
![]() ISBN: 1-890473-03-0 |
![]() ISBN: 1-890475-05-7 |
CD6 2002
ISBN: 0-97117498-3-3 |
CD7 2003
ISBN: 0-97117498-8-4 |
CD8 2004
ISBN: 1-890473-C6-5 |
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