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Record W2604987899 · doi:10.1155/2017/4979252

A Robust PCR Protocol for HIV Drug Resistance Testing on Low-Level Viremia Samples

2017· article· en· W2604987899 on OpenAlexafffund
Shivani Gupta, Tracy Taylor, Aileen P. Patterson, Binhua Liang, Jared Bullard, Paul Sandstrom, Gary Van Domselaar, Hezhao Ji

Bibliographic record

VenueBioMed Research International · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious DiseasesNational Institutes of HealthManitoba Medical Service Foundation
KeywordsViremiaHIV drug resistanceGenotypingViral loadVirologyDrug resistanceHuman immunodeficiency virus (HIV)Protocol (science)MedicineBiologyGenotypeAntiretroviral therapyGeneGeneticsPathology

Abstract

fetched live from OpenAlex

The prevalence of drug resistance (DR) mutations in people with HIV-1 infection, particularly those with low-level viremia (LLV), supports the need to improve the sensitivity of amplification methods for HIV DR genotyping in order to optimize antiretroviral regimen and facilitate HIV-1 DR surveillance and relevant research. Here we report on a fully validated PCR-based protocol that achieves consistent amplification of the protease (PR) and reverse transcriptase (RT) regions of HIV-1 pol gene across many HIV-1 subtypes from LLV plasma samples. HIV-spiked plasma samples from the External Quality Assurance Program Oversight Laboratory (EQAPOL), covering various HIV-1 subtypes, as well as clinical specimens were used to optimize and validate the protocol. Our results demonstrate that this protocol has a broad HIV-1 subtype coverage and viral load span with high sensitivity and reproducibility. Moreover, the protocol is robust even when plasma sample volumes are limited, the HIV viral load is unknown, and/or the HIV subtype is undetermined. Thus, the protocol is applicable for the initial amplification of the HIV-1 PR and RT genes required for subsequent genotypic DR assays.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.346
GPT teacher head0.444
Teacher spread0.099 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreProtocol

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2017
Admission routes2
Has abstractyes

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