Linked Life Data

9627228

Source:http://linkedlifedata.com/resource/pubmed/id/9627228

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
1998-7-28
pubmed:abstractText
Flow cytometric methods are widely used for analyzing cytokine release from immune cell populations. However, difficulties are frequently encountered in the analysis of flow cytometric data from intracellular cytokine staining because the cytokine positive and cytokine negative histograms overlap considerably. This study compares models containing Gaussian, Giddings, Haarhoff-van der Linde (HVL) and Weibull distributions for fitting flow cytometric intracellular cytokine histogram peaks. The results show that flow cytometric data for the Th1 cytokines, interferon-gamma and interleukin-2, in peripheral blood are well described by a model consisting of the sum of two log-normal distributions but the other distributions tested also showed satisfactory fits. The model-based approach may potentially eliminate the need to use markers derived from isotype control staining. The results obtained using peak fitting were also compared to the widely practised 99% division line or marker method. The percent positive calculated using the 99% division line or marker methods correlates poorly with the percent area under the cytokine-positive peak. However, when the model is used to calculate a cytokine-positive percentage analogous to that determined by the 99% division line or marker method, the two methods correlate well.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0196-4763
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
32
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
147-56
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Analysis of intracellular Th1 cytokine secretion data using parametric methodology.
pubmed:affiliation
Department of Pharmaceutics, State University of New York, Buffalo 14260-1200, USA. murali@acsu.buffalo.edu
pubmed:publicationType
Journal Article, Comparative Study, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't