Functional analysis of NK cell subsets activated by 721.221 and K562 HLA-null cells
Bibliographic record
Abstract
HLA-null cell lines [721.221 (henceforth, 721) and K562] are often used to study NK cell activation. NK cells are innate immune lymphocytes that express a variety of stochastically expressed inhibitory and activating receptors. Although it is known that 721 and K562 have divergent origins, they have been used interchangeably to stimulate NK cells in many studies. We hypothesized that the differences between 721 and K562 cells may result in differential NK cell-activation patterns. In this report, we assessed all possible combinations of CD107a expression and IFN-γ and CCL4 secretion in total NK and 3DL1(+/-) NK cell populations induced by these 2 cell lines. 721 activates a significantly higher frequency of NK cells and 3DL1(+) NK cells than K562. The NK cell functional subsets that are stimulated to a higher degree by 721 than K562 include those secreting IFN-γ and/or CCL4. On the other hand, the functional subsets that include CD107 expression contribute to a higher proportion of the total NK cell response following stimulation with K562 than 721. These results have implications for the selection of HLA-null cell lines to use as NK cell stimuli in investigations of their role in infectious diseases, cancer, and transplantation.
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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 | 0.001 |
| 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".