Specificity and function of activating Ly‐49 receptors
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".