Generation of large numbers of functional NK cells without feeders or serum
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are innate lymphocytes that modulate immune responses by secreting proinflammatory cytokines, and provide antitumor and antivirus activity. The ability to generate therapeutically relevant numbers of functional NK cells is critical for the development of advanced cellular immunotherapies. We have developed a streamlined culture workflow that enables expansion of functional NK cells without using either feeder cells or serum. Fresh human NK cells were isolated from peripheral blood using EasySep™, then cultured in ImmunoCult™ NK Cell Expansion Medium in plates coated with ImmunoCult™ NK Cell Expansion Coating Material. NK cells were fed, harvested, and replated on days 7 and 10 – 11 prior to phenotyping and functional assessment on day 14. The average frequency of CD56+ CD3− NK cells was 88% (range 75 – 96%, n=34), 74% (range 49 – 92%) of which expressed CD16, with an average expansion of 88 fold (range 3 – 454). Expanded NK cells were stimulated with phorbol 12-myristate 13-acetate (PMA) and ionomycin, or co-cultured with K562 cells. As detected by intracellular flow cytometry, the average frequency of interferon-γ+ among NK cells was 68% (range 41 – 90%, n=7) and 43% (range 12 – 55%) when stimulated by PMA/ionomycin or K562 cells, respectively. Similarly, the frequency of degranulated NK cells (CD107a+) was 81% (range 35 – 97%, n=7) and 64% (range 35 – 97%), respectively. The ability of expanded NK cells to kill target K562 cells was visualized by activated caspase in co-cultures with labeled K562 cells using the Incucyte® imaging system. At an effector:target ratio of 1:1, an average of 50% of K562 cells were killed (range 45 – 56, n=3). These results show large numbers of functional NK cells can be generated.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".