Literature DB >> 27190799

Application of Radial Basis Function Network Tool for Correlation of CD4+ Count with Plasma Viral Load in HIV-Seropositive Individuals.

Arnaw Kishore1, Sumana M Neelambike2.   

Abstract

INTRODUCTION: Human Immunodeficiency Virus (HIV) infects and cripples the immune system of the body. The two important marker CD4+T cells and Plasma viral load are crucial not only in understanding the disease progression but also in starting the antiretroviral therapy. A lot of research is going on in understanding the dynamic nature of HIV. AIM: To find the correlation between CD4+ count and Plasma Viral Load (PVL) measured by two different technologies; with the help of correlation technique in conjunction with the three dimensional HIV model with a purpose of establishing a mathematical model between the CD4+ cells and PVL using a sinusoidal function as well as Radial Basis Function (RBF) neural network.
MATERIALS AND METHODS: Plasma Viral Load were determined by two different methods viz Exavir Cavidi(TM) and Abbott Real time HIV-1 assay and then they were correlated with the CD4+ count with the help of computational intelligence in predicting viral load.
RESULTS: It was found that there exists a positive correlation between the CD4+ cells and viral loads. A correlation value of 0.4082 and 0.3652 was observed between CD4+ cells and viral measured using Exavir Cavidi(TM) and Abbott Real time HIV-1 assay respectively.
CONCLUSION: The existence of positive correlation had helped us to understand the nature and dynamic of the existence of HIV and how the CD4 + and PVL act.

Entities:  

Keywords:  Antiretroviral Therapy; HIV infection; Immune system

Year:  2016        PMID: 27190799      PMCID: PMC4866097          DOI: 10.7860/JCDR/2016/17745.7604

Source DB:  PubMed          Journal:  J Clin Diagn Res        ISSN: 0973-709X


  26 in total

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Journal:  Lancet       Date:  2002-07-13       Impact factor: 79.321

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Journal:  Nature       Date:  1995-01-12       Impact factor: 49.962

10.  DR_SEQAN: a PC/Windows-based software to evaluate drug resistance using human immunodeficiency virus type 1 genotypes.

Authors:  César Garriga; Luis Menéndez-Arias
Journal:  BMC Infect Dis       Date:  2006-03-08       Impact factor: 3.090

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