alexa HIV Dynamics Impacting the Efficacy of HIV/AIDS Treatme
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

HIV Dynamics Impacting the Efficacy of HIV/AIDS Treatments

George Towfic1*, Frank Graziano1, Fadi Towfic2, Karin Dorman3, Dianne Cook4, Samira Kettoola1 and Laura Neal5

1Department of Computer Science, Dubuque, IA, USA

2Immuneering Corporation, Cambridge, MA, USA

3Department of Genetics, Development and Cell Biology, Iowa State University, USA

4Department of Statistics, Iowa State University, USA

5Dubuque Medical Associates Clinic, USA

*Corresponding Author:
George Towfic
Department of Computer Science
Dubuque, IA, USA
E-mail: [email protected]

Received Date: January 26, 2013; Accepted Date: February 25, 2013; Published Date: February 28, 2013

Citation: Towfic G, Graziano F, Towfic F, Dorman K, Cook D, et al. (2013) HIV Dynamics Impacting the Efficacy of HIV/AIDS Treatments. J Proteomics Bioinform 6:051-057. doi:10.4172/jpb.1000261

Copyright: © 2013 Towfic G, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

HIV/AIDS symptoms, treatment efficacy and type of treatments differ from one region/country to another. In this article, we discuss and analyze American Midwest patients HIV/AIDS laboratory data. A total of 9,392 patient’s visits for 2,588 patients have been considered. The efficacy of different treatments, in terms of reducing the Ribonucleic acid (RNA), and/or increasing the CD4 counts, has been analyzed. We provide data summary graphs that help to identify important patterns embedded in patient’s reactions to different therapies. We provide statistical evidence regarding treatment tolerance (in terms of the number of consecutive times a given treatment can be used on a considered patient) and most effective treatments (in terms of the rate of change in CD4 and RNA levels). We avoided using genetic sequences, to help understand the treatment efficacy, based solely on basic laboratory tests (CD4 count and RNA levels). We believe that since genetic analysis cannot be easily obtained in resource-limited countries, it is important to investigate if basic laboratory data (when collected in large quantities) will be sufficient to determine the most effective treatments. We show that, before 2004, the percentage of CD4 count>350 kept improving to rise from about 51% on 2000, to about 55% in 2004. During the same period of time, the percentage of undetectable RNA level declined from about 65% to about 52%. We also show that both healthy CD4 count of >350 cells/μl and undetectable RNA level (<75/50 cells/μl depending on the year of measurement) have significantly improved with CD4 count>350 going up from about 55% in 2004 to about 65% in 2007. We provide statistical analyses and efficacy evaluation, related to the most significant treatments throughout the years 2000-2008. All data and analysis tools are provided on our Midwest HIV/AIDs web portal (https://hivdatamining.com).

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