The Ly49 natural killer cell receptors: a versatile tool for viral self‐discrimination
Bibliographic record
Abstract
The activation of murine and human natural killer (NK) cells is regulated by families of receptors including the Ly49 and Killer immunoglobulin-like receptors, respectively, both of which contain activating and inhibitory members. The archetypal role of inhibitory Ly49 receptors is to attenuate NK cell responses to normal cells that express major histocompatibility complex (MHC) class-I molecules, in essence allowing for more robust responses to infected or cancerous cells that lack MHC-I on their cell surface. However, it is now evident that Ly49 receptors have an appreciably more sophisticated array of functions. In particular, some activating Ly49 receptors can bind directly to MHC-I-like viral gene products such as m157, whereas others recognize self-MHC-I but only in the presence of viral chaperones. Although Ly49 receptor recognition is centred on the MHC-I-like fold, these NK cell receptors can also engage related ligands in unexpected ways. Herein we review the varied strategies employed by Ly49 receptors to recognize both self and viral ligands, with particular emphasis on the recently determined mode of Ly49-m157 ligation, and highlight the versatile nature of this family in the control of viral infections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".