Licensed and Unlicensed NK Cells: Differential Roles in Cancer and Viral Control
Bibliographic record
Abstract
Natural killer (NK) cells are known for their well characterized ability to control viral infections and eliminate tumor cells. Through their repertoire of activating and inhibitory receptors, NK cells are able to survey different potential target cells for various surface markers, such as MHC-I - which signals to the NK cell that the target is healthy - as well as stress ligands or viral proteins, which alert the NK cell to the aberrant state of the target and initiate a response. According to the "licensing" hypothesis, interactions between self-specific MHC-I receptors - Ly49 in mice and KIR in humans - and self-MHC-I molecules during NK cell development is crucial for NK cell functionality. However, there also exists a large proportion of NK cells in mice and humans, which lack self-specific MHC-I receptors and are consequentially "unlicensed." While the licensed NK cell subset plays a major role in the control of MHC-I-deficient tumors, this review will go on to highlight the important role of the unlicensed NK cell subset in the control of MHC-I-expressing tumors, as well as in viral control. Unlike the licensed NK cells, unlicensed NK cells seem to benefit from the lack of self-specific inhibitory receptors, which could otherwise be exploited by some aberrant cells for immunoevasion by upregulating the expression of ligands or mimic ligands for these receptors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".