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Record W2573625654 · doi:10.1111/ahg.12182

No Evidence for Association of β‐Defensin Genomic Copy Number with HIV Susceptibility, HIV Load during Clinical Latency, or Progression to AIDS

2017· article· en· W2573625654 on OpenAlexaff
Razan Abujaber, Patrick R. Shea, Paul J. McLaren, Shabir Lakhi, Jill Gilmour, Susan Allen, Jacques Fellay, Edward J. Hollox

Bibliographic record

VenueAnnals of Human Genetics · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungInternational AIDS Vaccine Initiative
KeywordsDefensinCopy-number variationBiologyBeta defensinViral loadLatency (audio)Human immunodeficiency virus (HIV)GeneticsSingle-nucleotide polymorphismImmunologyGeneGenomeGenotype

Abstract

fetched live from OpenAlex

Common single-nucleotide variation in the host accounts for 25% of the variability in the plasma levels of HIV during the clinical latency stage (viral load set point). However, the role of rare variants and copy number variants remains relatively unexplored. Previous work has suggested copy number variation of a cluster of β-defensin genes affects HIV load in treatment-naïve sub-Saharan Africans and rate of response to antiretroviral treatment. Here we analyse a total of 1827 individuals from two cohorts of HIV-infected individuals from Europe and sub-Saharan Africa to investigate the role of β-defensin copy number variation on HIV load at set point. We find no evidence for association of copy number with viral load. We also compare distribution of β-defensin copy number between European cases and controls and find no differences, arguing against a role of β-defensin copy number in HIV acquisition. Taken together, our data argue against an effect of copy number variation of the β-defensin region in the spontaneous control of HIV infection.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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

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 head0.151
GPT teacher head0.457
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2017
Admission routes1
Has abstractyes

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