Unique subset of natural killer cells develops from progenitors in lymph node
Bibliographic record
Abstract
Natural killer (NK) cells have been thought to develop from committed progenitors in the bone marrow. However, a novel pathway of thymus-dependent NK-cell development that produces a unique subset of NK cells expressing CD127 has recently been reported. We now have identified 2 populations of NK progenitors, one in the thymus and the other in the lymph node (LN). Immature double-negative 2 (CD4(-)CD8(-)CD44(+)CD25(+)) thymocytes have potential to produce NK cells with rearranged T-cell receptor gamma genes (Tcrgamma(+)) in vitro. Tcrgamma(+) NK cells are rare in spleen but relatively abundant in the thymus and LN. Approximately 20% of LN NK cells are Tcrgamma(+), and they are found at similar levels in both CD127(+) and CD127(-) subsets. Moreover, a subpopulation of LN cells resembling immature thymocytes differentiates into Tcrgamma(+) NK cells in vitro and also repopulates the NK compartment in lymphopenic mice. Athymic mice lack the LN NK progenitors expressing CD127 as well as Tcrgamma(+) NK cells. These results suggest that Tcrgamma(+) NK cells may be generated from unique progenitors in the thymus as well as in the LN.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".