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Record W2767522183 · doi:10.1097/qad.0000000000001680

Toll-like receptor 9 polymorphism is associated with increased Epstein–Barr virus and Cytomegalovirus acquisition in HIV-exposed infants

2017· article· en· W2767522183 on OpenAlexaff
Kristin Beima‐Sofie, Dalton Wamalwa, Elizabeth Maleche‐Obimbo, Jairam R. Lingappa, Romel D. Mackelprang, Soren Gantt, Grace John‐Stewart, Corey Casper, Jennifer A. Slyker

Bibliographic record

VenueAIDS · 2017
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesFogarty International CenterNational Institute of Allergy and Infectious DiseasesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Cancer Institute
KeywordsVirusVirologyGenotypeImmunologyEpstein–Barr virusBiologyHerpesviridaeCytomegalovirusHuman cytomegalovirusAlleleLocus (genetics)GammaherpesvirinaeViral diseaseMedicineGeneGenetics

Abstract

fetched live from OpenAlex

: Polymorphisms in the Toll-like receptor 9 1635 locus have been associated with HIV-1 acquisition and progression. Cytomegalovirus (CMV) and Epstein-Barr virus (EBV) acquisition were compared between Kenyan HIV-exposed infants by 1635 genotype. Having one or more copies of the 1635A allele was associated with increased CMV acquisition in HIV-infected infants (42 vs. 11%, P = 0.03) and increased risk of EBV acquisition in HIV-exposed uninfected infants (hazard ratio = 4.2, P = 0.02) compared with 1635GG. In addition, 1635A was associated with 0.4 log10 copies/ml lower median EBV levels in HIV-infected infants (P = 0.03). These data suggest a potentially important role for this locus in primary herpesvirus 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 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.023
GPT teacher head0.288
Teacher spread0.266 · 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

Citations13
Published2017
Admission routes1
Has abstractyes

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