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Record W2955920193 · doi:10.1128/jvi.00933-19

HLA-F on Autologous HIV-Infected Cells Activates Primary NK Cells Expressing the Activating Killer Immunoglobulin-Like Receptor KIR3DS1

2019· article· en· W2955920193 on OpenAlexafffund
Zahra Kiani, Julie Bruneau, Daniel E. Geraghty, Nicole F. Bernard

Bibliographic record

VenueJournal of Virology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsCentre Hospitalier de l’Université de MontréalMcGill UniversityUniversité de MontréalMcGill University Health Centre
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchGovernment of Canada
KeywordsImmunologyBiologyInterleukin 12Cytotoxic T cellIn vitro

Abstract

fetched live from OpenAlex

This study investigated a mechanism that may underly epidemiological studies showing that carriage of theKIR3DS1homozygous genotype is more frequent among HIV-exposed seronegative subjects than among HIV-susceptible individuals. Carriage of this genotype is associated with a reduced risk of HIV infection. The protective mechanism involves the interaction of HLA-F on CD4+cells infected with replication-competent HIV with the activating NK receptor, KIR3DS1. This interaction leads to the activation of KIR3DS1+NK cells for secretion of cytokines and chemokines with anti-HIV activity. Among these is CCL4, which binds and blocks CCR5, the coreceptor for HIV entry of HIV into new target cells. In the setting of an exposure to HIV, incoming HIV-infected cells expressing HLA-F rapidly activate KIR3DS1+NK cells to elicit anti-HIV activity. Exclusive gating strategies and blocking experiments support the notion that the HLA-F/KIR3DS1 interaction is sufficient to activate NK cell functions.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0050.001

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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 designBench or experimental
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

Citations27
Published2019
Admission routes2
Has abstractyes

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