Unleashing Anti-Tumor Activity of Natural Killer Cells Via Modulation of Immune Checkpoints Receptors and Molecules
Bibliographic record
Abstract
As vital innate lymphocytes, natural killer (NK) cells suppress cancer progression chiefly by inducing cell lysis and secreting pro-inflammatory cytokines. NK cell activation relies on the balance between inhibitory and stimulating signals mediated by a wide range of surface receptors. Specific receptors initiate intracellular signaling pathways, which are negatively regulated by specific checkpoint molecules. Synergistic activation is controlled by Cbl proteins and GSK-3β, while the downstream signaling pathways induced by ITIM-bearing receptors are regulated by SHP-1. These intracellular NK checkpoints are attractive targets for immune checkpoint blockade therapies, but not enough attention has been given. Hence, this paper discusses the major signaling pathways regulated by the intracellular checkpoints and their potential clinical application. The current progress in the investigation of NK checkpoint receptors is also summarized. This paper aims to promote the development of novel immunotherapies that optimize the tumor-suppressive activity of NK cells while suppressing tumor immunological evasion.
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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 | 0.000 |
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".