T Regulatory Cells Control the Numbers of NK Cells and Thereby Protect CD8alpha+ Immature Dendritic Cells Presence in the Lymph Node Paracortex (88.13)
Bibliographic record
Abstract
Abstract In the absence of infection, the spleen contains numerous natural killer (NK) cells located mainly in the red pulp and whose differentiation profile is characterized by a preponderance of mature elements. In contrast, lymph nodes (LNs) contain few NK cells that are sited mostly in T-cell zones and that are phenotypically skewed toward immature developmental stages. We present evidence that CD4+CD25+ T regulatory (Treg) cells hamper generation of mature NK cells in the LNs through short-range interactions with NK precursors. Moreover, we show that Treg cells are both necessary and sufficient to repress accumulation of NK cells in resting LNs and an absence of Treg totally alleviates the differentiation blockade and leads to an accumulation of mature NK cells in the LN paracortex. In turn, mature NK cells specifically regulate the amount of CD8α+ phenotypically immature dendritic cells (iDCs) present in LN T-cell zones. We propose that the dominant influence of Treg cells on NK cell precursors and consequently on CD8α+ iDCs explains why “quiescent” LNs, in the absence of infection, function as privileged sites for induction and maintenance of tolerance to peripheral self antigens.
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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) | 0.002 | 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".