Inhibition of natural killer cell activation signals by killer cell immunoglobulin‐like receptors (CD158)
Bibliographic record
Abstract
The killer cell immunoglobulin-like receptor (KIR) family includes receptors that bind to HLA class I molecules on target cells and inhibit natural killer (NK)-cell cytotoxicity, and receptors such as KIR3DL7 with no known ligand and function. Inhibitory KIR recruit the tyrosine phosphatase SHP-1 to block signals transduced by any one of a number of activation receptors. Inhibition of overall protein tyrosine phosphorylation by SHP-1 during binding of KIR to MHC class I on target cells is selective, suggesting that a limited number of substrates are dephosphorylated by SHP-1. We have chosen to study KIR inhibition as it occurs during binding of KIR to MHC class I on target cells, despite the technical limitations inherent to studies of processes regulated by cell contact. KIR binding to MHC class I on target cells inhibits tyrosine phosphorylation of the activation receptor 2B4 (CD244) and disrupts adhesion of NK cells to target cells. Inhibition of proximal events in NK activation may increase the availability of NK cells by liberating them from non-productive interactions with resistant target cells. As the receptors and the signaling pathways that induce NK cytotoxicity are not fully characterized, elucidation of the inhibitory mechanism employed by KIR may provide insight into NK activation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".