Evidence map›Paper›PMID 29445252›Full record

ArticleProceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control2017

Implications of Measurement Assay Type in Design of HIV Experiments.

LaMont Cannon, Aditya Jagarapu, Cesar A Vargas-Garcia, Michael J Piovoso, Ryan Zurakowski

Abstract read
In one paragraph

Article in Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

LaMont CannonUniversity of Delaware Biomedical Engineering Department, Newark, DE 19716, USA.
Aditya JagarapuUniversity of Delaware Biomedical Engineering Department, Newark, DE 19716, USA.
Cesar A Vargas-GarciaUniversity of Delaware Electrical and Computer Engineering Department Newark, DE 19716, USA.
Michael J PiovosoUniversity of Delaware Electrical and Computer Engineering Department Newark, DE 19716, USA.
Ryan ZurakowskiUniversity of Delaware Biomedical Engineering Department, Newark, DE 19716, USA.

Funding

HIV 2-LTR Dynamics and Cryptic ViremiaR21AI110288 · NIAID · UNIVERSITY OF DELAWARE · PI ZURAKOWSKI, RYAN M · 2014 to 2015
$418k
NIAID NIH HHS R21 AI110288
6 · The paper itself

Abstract

Time series measurements of circular viral episome (2-LTR) concentrations enable indirect quantification of persistent low-level Human Immunodeficiency Virus (HIV) replication in patients on Integrase-Inhibitor intensified Combined Antiretroviral Therapy (cART). In order to determine the magnitude of these low level infection events, blood has to be drawn from a patients at a frequency and volume that is strictly regulated by the Institutional Review Board (IRB). Once the blood is drawn, the 2-LTR concentration is determined by quantifying the amount of HIV DNA present in the sample via a PCR (Polymerase Chain Reaction) assay. Real time quantitative Polymerase Chain Reaction (qPCR) is a widely used method of performing PCR; however, a newer droplet digital Polymerase Chain Reaction (ddPCR) method has been shown to provide more accurate quantification of DNA. Using a validated model of HIV viral replication, this paper demonstrates the importance of considering DNA quantification assay type when optimizing experiment design conditions. Experiments are optimized using a Genetic Algorithm (GA) to locate a family of suboptimal sample schedules which yield the highest fitness. Fitness is defined as the expected information gained in the experiment, measured by the Kullback-Leibler Divergence (KLD) between the prior and posterior distributions of the model parameters. We compare the information content of the optimized schedules to uniform schedules as well as two clinical schedules implemented by researchers at UCSF and the University of Melbourne. This work shows that there is a significantly greater gain information in experiments using a ddPCR assay vs. a qPCR assay and that certain experiment design considerations should be taken when using either assay.

Identifiers

PMID29445252
PMCPMC5809129

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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.