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Record W2160007318 · doi:10.1093/infdis/jir833

Single-Nucleotide Polymorphism–Defined Class I and Class III Major Histocompatibility Complex Genetic Subregions Contribute to Natural Long-term Nonprogression in HIV Infection

2012· article· en· W2160007318 on OpenAlexaff
Julien Guergnon, Cyril Dalmasso, Philippe Broët, Laurence Meyer, Samantha J. Westrop, Nesrina Imami, Elisa Vicenzi, Giulia Morsica, Marco Tinelli, Barbara Zanone Poma, Cécile Goujard, Valérie Potard, F Gotch, Claudio Casoli, Andrea Cossarizza, Fabìo Macciardi, Patrice Debré, Jean‐François Delfraissy, Massimo Galli, Brigitte Autran, Dominique Costagliola, Guido Poli, I Théodorou, Agostino Riva

Bibliographic record

VenueThe Journal of Infectious Diseases · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsLinkage disequilibriumBiologySingle-nucleotide polymorphismMajor histocompatibility complexGeneticsHaplotypeMHC class ISNPGenetic associationVirologyGenotypeGene

Abstract

fetched live from OpenAlex

We performed a genome-wide association study comparing a cohort of 144 human immunodeficiency virus (HIV type 1-infected, untreated white long-term nonprogressors (LTNPs) with a cohort of 605 HIV-1-infected white seroconverters. Forty-seven single-nucleotide polymorphisms (SNPs), located from class I to class III major histocompatibility complex (MHC) subregions, show statistical association (false discovery rate, <0.05) with the LTNP condition, among which 5 reached genome-wide significance after Bonferonni correction. The MHC LTNP-associated SNPs are ordered in ≥4 linkage disequilibrium blocks; interestingly, an MHC class III linkage disequilibrium block (defined by the rs9368699 SNP) seems specific to the LTNP phenotype.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.272
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2012
Admission routes1
Has abstractyes

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