NK cell development from a novel progenitor found in the murine lung (138.10)
Bibliographic record
Abstract
Abstract NK cells in the lung are phenotypically different from those in the spleen or bone marrow (BM). We have identified NK cell progenitors (NKPs) in normal mouse lung. Lung NKPs are lineage- NK1.1- CD122+ B220+/- and are phenotypically similar to conventional BM NKPs. Cells with the same phenotype are also found in peripheral blood. Lung NKPs cultured on OP9 stromal cells with cytokines expand over 2×104 fold and mature into NK1.1+CD3- NK cells. The frequencies of NKPs among the lineage-NK1.1-CD122+B220+ population in the lung and lineage-NK1.1-CD122+B220- cells in the BM are 0.16 and 0.14, respectively. However, NK cells generated in vitro from lung NKPs and BM NKPs significantly differ in the expression of the Ly49 family of NK cell receptors. Most NK cells develop in vitro from lung NKPs express multiple Ly49s whereas most of those develop from BM NKPs express CD94/NKG2 but not Ly49. Lung NKPs develop into CD3-NK1.1+ NK cells upon adoptive transplant into NOD/SCID IL2Rγ-/- hosts. Thus, we have identified NKPs in the lung. They likely develop in the BM and migrate to the lung through peripheral blood. This supports the emerging hypothesis that NK cell development includes final maturation stages outside of the BM, where the local microenvironment influences development. The contribution of lung NKPs to the lung NK cell population in vivo is currently being investigated.
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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) | 0.001 | 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".