Impaired NK‐cell education diminishes resistance to murine CMV infection
Bibliographic record
Abstract
Ly49G2 (G2+) NK cells mediate murine (M)CMV resistance in MHC D(k) -expressing mice. Bone marrow transplantation (BMT) studies revealed that G2+ NK cell-mediated MCMV resistance requires D(k) in both hematopoietic and nonhematopoietic cells. As a Ly49G2 ligand, D(k) in both cell lineages may contribute to lysis of virus-infected cells. Alternatively, cellular differences in self-MHC D(k) may have affected NK-cell education, and consequently NK cell-mediated viral clearance. We investigated the D(k) -licensing effect on BM-derived NK cells in BMT recipients by analyzing cytokines, cytotoxicity and MCMV resistance. In BMT recipients with lineage-restricted D(k) , G2+ NK-cell reactivity and cytotoxicity was diminished in comparison to BMT recipients with self-MHC in all cells. Reduced G2+ NK-mediated MCMV resistance in BMT recipients with lineage-restricted self-MHC indicates that licensing of G2+ NK cells is related to NK-cell reactivity and viral control. Titrating donor BM with self-MHC-bearing hematopoietic cells, as well as adoptive transfer of mature G2+ NK cells into BMT recipients with self-MHC in non-hematopoietic cells only, enhanced NK-cell licensing and rescued MCMV resistance. This disparate self-MHC NK-cell education model would suggest that inadequately licensed NK cells corresponded to inefficient viral sensing and clearance.
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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".