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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".