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Record W2160079103 · doi:10.1086/421504

Long‐Term Survivors in Nairobi: Complete HIV‐1 RNA Sequences and Immunogenetic Associations

2004· article· en· W2160079103 on OpenAlexaff
Guowei Fang, Carla Kuiken, Barbara Weiser, Sarah Rowland‐Jones, Francis A. Plummer, Chih‐Hsiung Chen, Rupert Kaul, Aggrey O. Anzala, Job J. Bwayo, Joshua Kimani, Sean Philpott, Christina M. Ramirez, Janet S. Sinsheimer, Brian Gaschen, Dorothy Lang, Binshan Shi, Kimdar Sherefa Kemal, Tim Rostron, Cheryl Brunner, Simon Beddows, Quentin J. Sattenau, Ellen E. Paxinos, Julius Oyugi, Harold Burger

Bibliographic record

VenueThe Journal of Infectious Diseases · 2004
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsUniversity of Manitoba
FundersNational Institute of Allergy and Infectious DiseasesNational Institute of Mental HealthNational Institutes of HealthNational Institute for Health and Care Research
KeywordsImmunologyHuman leukocyte antigenCohortImmune systemHuman immunodeficiency virus (HIV)DiseaseImmunogeneticsMedicineBiologyLentivirusVirologyAntibodyViral diseaseInternal medicineAntigen

Abstract

fetched live from OpenAlex

To investigate African long-term survivors (LTSs) infected with non-subtype B human immunodeficiency virus type 1 (HIV-1), we obtained full-length HIV-1 RNA sequences and immunogenetic profiles from 6 untreated women enrolled in the Pumwani Sex Worker Cohort in Nairobi, Kenya. There were no discernible sequence changes likely to cause attenuation. CCR2-V64I, an immunogenetic polymorphism linked to LTSs, was detected in 4 women, all of whom carried the HLA B58 allele. Further investigation of 99 HIV-1-infected Nairobi women found an association between CCR2-V64I and HLA B58 (P=.0048). Studying the interaction among immunogenetics, immune responses, and viral sequences from all HIV-1 subtypes may increase our understanding of slow HIV-1 disease progression.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.014
GPT teacher head0.261
Teacher spread0.248 · 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

Citations29
Published2004
Admission routes1
Has abstractyes

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