Regulatory T cells dynamically regulate selectin ligand function during multiple challenge contact hypersensitivity (146.12)
Bibliographic record
Abstract
Regulatory T cells (Tregs) play critical roles in regulation of T cell‐mediated inflammation, and in organs such as the skin, this is dependent on their expression of selectin ligands required for rolling in peripheral microvessels. However, whether there are differences in the molecules used by Tregs and pro‐inflammatory T cells to undergo rolling remains unclear. Here we used spinning disk confocal microscopy of Foxp3‐GFP mice to visualize rolling of endogenous Tregs in dermal postcapillary venules. Tregs were observed to undergo infrequent but consistent rolling interactions under resting and inflamed conditions. Despite this, during inflammation, Tregs were able to adhere efficiently, comprising 40% of the total adherent CD4 + T cell population at the peak of the response. In a multiple challenge model of contact sensitivity, rolling of both Tregs and conventional CD4 + T cells was mostly dependent on overlapping contributions of P‐ and E‐selectin. However, shortly after a second challenge, rolling of Tregs but not conventional T cells became P‐selectin‐independent, an effect associated with a reduction in their capacity to bind P‐selectin in vitro . These findings demonstrate that Treg selectin‐binding capacity and the molecular basis of Treg rolling in the peripheral microvasculature can be regulated dynamically in a multiple challenge model of inflammation.
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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 | 0.000 |
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".