Development of Functional Human NK Cells in an Immunodeficient Mouse Model with the Ability to Provide Protection against Tumor Challenge
Bibliographic record
Abstract
Studies of human NK cells and their role in tumor suppression have largely been restricted to in vitro experiments which lack the complexity of whole organisms, or mouse models which differ significantly from humans. In this study we showed that, in contrast to C57BL/6 Rag2(-/-)/gamma(c) (-/-) and NOD/Scid mice, newborn BALB/c Rag2(-/-)/gamma(c) (-/-) mice can support the development of human NK cells and CD56+ T cells after intrahepatic injection with hematopoietic stem cells. The human CD56(+) cells in BALB/c Rag2(-/-)/gamma(c) (-/-) mice were able to produce IFN-gamma in response to human IL-15 and polyI:C. NK cells from reconstituted Rag2(-/-)/gamma(c) (-/-) mice were also able to kill and inhibit the growth of K562 cells in vitro and were able to produce IFN-gamma in response to stimulation with K562 cells. In vivo, reconstituted Rag2(-/-)/gamma(c) (-/-) mice had higher survival rates after K562 challenge compared to non-reconstituted Rag2(-/-)/gamma(c) (-/-) mice and were able to control tumor burden in various organs. Reconstituted Rag2(-/-)/gamma(c) (-/-) mice represent a model in which functional human NK and CD56+ T cells can develop from stem cells and can thus be used to study human disease in a more clinically relevant environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".