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Specificity and function of activating Ly‐49 receptors

2001· review· en· W1975709511 on OpenAlex
Kevin P. Kane, Elizabeth T. Silver, Bart Hazes

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImmunological Reviews · 2001
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversity of Alberta
FundersNational Cancer InstituteCanadian Institutes of Health Research
KeywordsReceptorBiologyMHC class IMajor histocompatibility complexCell biologyImmune receptorImmunologyAntigenGenetics

Abstract

fetched live from OpenAlex

Summary: Inhibitory Ly‐49 receptors allow murine natural killer (NK) cells to kill cells with aberrant class I MHC expression while sparing normal cells. This is accomplished by their recognition of specific class I MHC products and prevention of NK‐cell lysis of cells that present a normal repertoire of class I MHC ligands –“the missing self hypothesis”. However, Ly‐49 receptors that lack the cytoplasmic immunoreceptor tyrosine‐based inhibitory motif, which is required for inhibition of killing, have also been described. These receptors were found to stimulate NK killing and are therefore referred to as activating Ly‐49 receptors. Interestingly, the activating receptors have class I MHC‐binding domains that are nearly indistinguishable from those of the inhibiting receptors, and binding to class I MHC has now been demonstrated for three activating receptors. Presently, there is no defined physiological role for activating Ly‐49 receptors. Here we present an overview of current knowledge regarding the diversity, structure and function of activating Ly‐49 receptors with a focus on class I MHC specificity, and we discuss their potential role(s) in natural resistance. This research was supported by an operating grant from the National Cancer Institute of Canada and the Canadian Cancer Society (K. P. Kane) and a Canadian Institutes for Health Research operating grant (B. Hazes). E. Silver is supported by an Alberta Heritage Foundation for Medical Research (AHFMR) studentship and K. P. Kane is an (AHFMR) senior scholar.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.092
GPT teacher head0.326
Teacher spread0.235 · 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