Evidence map›Paper›PMID 22659043›Full record

ArticleJournal of theoretical biology2012

Piecewise HIV virus dynamic model with CD4(+) T cell count-guided therapy: I.

Sanyi Tang, Yanni Xiao, Ning Wang, Hulin Wu

Abstract read
In one paragraph

Article in Journal of theoretical biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sanyi TangCollege of Mathematics and Information Science, Shaanxi Normal University, Xi'an 710062, PR China. sanyitang219@hotmail.com
Yanni Xiao
Ning Wang
Hulin Wu

Funding

Virology/Immunology CoreP30AI078498 · NIAID · UNIVERSITY OF ROCHESTER · PI DEWHURST, STEPHEN · 2008 to 2018
$14.6M
Statistical Methods for ODE Models in AIDS ResearchR01AI087135 · NIAID · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI WU, HULIN · 2010 to 2019
$3.2M
NIAID NIH HHS N01 AI050020NIAID NIH HHS P30 AI078498NIAID NIH HHS P30AI078498NIAID NIH HHS R01 AI087135NIAID NIH HHS R01AI087135
6 · The paper itself

Abstract

The strategies of structured treatment interruptions (STIs) of antiretroviral therapies have been proposed for clinical management of HIV infected patients, but clinical studies on STIs failed to achieve a consistent conclusion for this strategy. To evaluate the STI strategies, in particular, CD4(+) T cell count-guided STIs, and explain these controversial conclusions from different clinical studies, in this paper we propose to use piecewise HIV virus dynamic models to quantitatively explore the STI strategies and investigate their dynamic behaviors. Our analysis results indicate that CD4(+) T cell counts can be maintained above a safe level using the STI with a single threshold or a threshold window. Numerical simulations show that the CD4(+) T cell counts either fluctuate or approach a stable level for a patient, depending on the prescribed upper or lower threshold values. In particular, the CD4(+) T cell counts can be stabilized at a desired level if the threshold policy control is applied. The durations of drug-on and drug-off are very sensitive to the prescribed upper or lower threshold levels, which possibly explains why the on-off strategy with fixed schedule or an STI strategy with frequent switches are associated with the high rate of failure. Our findings suggest that it is critical to carefully choose the thresholds of CD4(+) T cell count and individualize the STIs for each individual patient based on initial CD4(+) T cell counts.

Indexed as

Models, ImmunologicalAnti-HIV AgentsAntiretroviral Therapy, Highly ActiveCD4 Lymphocyte CountComputer SimulationHIVHIV InfectionsHumansAnti-HIV Agents

Identifiers

PMID22659043
PMCPMC3399056

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.