Novel role of regulatory T cells in limiting early neutrophil responses in skin
Bibliographic record
Abstract
It is clear that CD4(+) CD25(+) Foxp3(+) regulatory T (Treg) cells inhibit chronic inflammatory responses as well as adaptive immune responses. Among the CD4(+) T-cell population in the skin, at least one-fifth express Foxp3. As the skin is constantly exposed to antigenic challenge and is a common site of vaccination, understanding the role of these skin-resident Treg cells is important. Although the suppressive effect of Treg cells on T cells is well documented, less is known about the types of innate immune cells influenced by Treg cells and whether the Treg cells suppress acute innate immune responses in vivo. To address this we used a mouse melanoma cell line expressing Fas ligand (B16FasL), which induces an inflammatory response following subcutaneous injection of mice. We demonstrate that Treg cells limit this response by inhibiting neutrophil accumulation and survival within hours of tumour cell inoculation. This effect, which was associated with decreased expression of the neutrophil chemoattractants CXCL1 and CXCL2, promoted survival of the inoculated tumour cells. Overall, these data imply that Treg cells in the skin are rapidly mobilized and that this activity serves to limit the amplification of inflammatory responses at this site.
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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 | 0.000 |
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".