Human NK cells and cancer
Bibliographic record
Abstract
The long story of NK cells started about 50 y ago with the first demonstration of a natural cytotoxic activity within an undefined subset of circulating leukocytes, has involved an ever-growing number of researchers, fascinated by the apparently easy-to-reach aim of getting a "universal anti-tumor immune tool". In fact, in spite of the impressive progress obtained in the first decades, these cells proved far more complex than expected and, paradoxically, the accumulating findings have continuously moved forward the attainment of a complete control of their function for immunotherapy. The refined studies of these latter years have indicated that NK cells can epigenetically calibrate their functional potential, in response to specific environmental contexts, giving rise to extraordinarily variegated subpopulations, comprehensive of memory-like cells, tissue-resident cells, or cells in various differentiation stages, or distinct functional states. In addition, NK cells can adapt their activity in response to a complex body of signals, spanning from the interaction with either suppressive or stimulating cells (myeloid-derived suppressor cells or dendritic cells, respectively) to the engagement of various receptors (specific for immune checkpoints, cytokines, tumor/viral ligands, or mediating antibody-dependent cell-mediated cytotoxicity). According to this picture, the idea of an easy and generalized exploitation of NK cells is changing, and the way is opening toward new carefully designed, combined and personalized therapeutic strategies, also based on the use of genetically modified NK cells and stimuli capable of strengthening and redirecting their effector functions against cancer.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".