A predictive model of the distribution of T-cell phenotype variations

dc.contributor.authorD'Orsi L.
dc.contributor.authorPresti E.L.
dc.contributor.authorGiacopelli G.
dc.contributor.authorDe Gaetano A.
dc.contributor.correspondenceD'Orsi L.
dc.contributor.otherMahidol University
dc.date.accessioned2024-09-05T18:07:13Z
dc.date.available2024-09-05T18:07:13Z
dc.date.issued2024-01-01
dc.description.abstractUnderstanding the initiation and evolution of the immune response is of fundamental importance for a rational approach to infectious and autoimmune diseases. Flow cytometry now allows the sequential study of sub-populations of T-lymphocytes, where samples of cells undergo phenotype modifications induced by initial contact with the antigen. Such phenotype changes, reflecting the transition from Naive to Effector to Memory cells, consist among others in a continuing reduction in expression of the CD27 surface antigen, accompanied by an initial reduction followed by an increment of the expression of CD45RA, also a surface antigen. The present work is the first attempt to formalize the evolution of this population of immune cells by means of a mathematical model describing the movement of clusters of T-cells over the plane defined by the (log) concentrations of CD27 and CD45RA, as measured on each cell by modern flow cytometry. Eventual estimation of the parameters of such a movement model in a given subject will help caregivers to better classify the current state and the likely progression of disease.
dc.identifier.citationINES 2024 - 28th IEEE International Conference on Intelligent Engineering Systems 2024, Proceedings (2024) , 15-19
dc.identifier.doi10.1109/INES63318.2024.10629116
dc.identifier.scopus2-s2.0-85202598586
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/20.500.14594/100946
dc.rights.holderSCOPUS
dc.subjectMathematics
dc.subjectComputer Science
dc.subjectMedicine
dc.subjectDecision Sciences
dc.titleA predictive model of the distribution of T-cell phenotype variations
dc.typeConference Paper
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85202598586&origin=inward
oaire.citation.endPage19
oaire.citation.startPage15
oaire.citation.titleINES 2024 - 28th IEEE International Conference on Intelligent Engineering Systems 2024, Proceedings
oairecerif.author.affiliationObuda University
oairecerif.author.affiliationConsiglio Nazionale delle Ricerche
oairecerif.author.affiliationMahidol University

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