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Record W2127834152 · doi:10.1126/science.1215621

Tuning of Natural Killer Cell Reactivity by NKp46 and Helios Calibrates T Cell Responses

2012· article· en· W2127834152 on OpenAlexaff
Émilie Narni-Mancinelli, Baptiste N. Jaeger, Claire Bernat, Aurore Fenis, Sam K. P. Kung, Aude De Gassart, Sajid Mahmood, Simon Heath, Jordi Estellé, Elodie Bertosio, Frédéric Vely, Louis N. Gastinel, Bruce Beutler, Bernard MALISSEN, Marie Malissen, Marta Gut, Éric Vivier, Sophie Ugolini

Bibliographic record

VenueScience · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Manitoba
FundersAgence Nationale de la Recherche
KeywordsReactivity (psychology)Computer scienceCellBiologyGeneticsMedicine

Abstract

fetched live from OpenAlex

Natural killer (NK) cells are lymphocytes involved in antimicrobial and antitumoral immune responses. Using N-ethyl-N-nitrosourea mutagenesis in mice, we identified a mutant with increased resistance to viral infections because of the presence of hyperresponsive NK cells. Whole-genome sequencing and functional analysis revealed a loss-of-function mutation in the Ncr1 gene encoding the activating receptor NKp46. The down-regulation of NK cell activity by NKp46 was associated with the silencing of the Helios transcription factor in NK cells. NKp46 was critical for the subsequent development of antiviral and antibacterial T cell responses, which suggests that the regulation of NK cell function by NKp46 allows for the optimal development of adaptive immune responses. NKp46 blockade enhanced NK cell reactivity in vivo, which could enable the design of immunostimulation strategies in humans.

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 score0.001
Threshold uncertainty score0.004

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 integrity0.0000.001
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 head0.009
GPT teacher head0.234
Teacher spread0.225 · 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

Citations195
Published2012
Admission routes1
Has abstractyes

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