Regulatory T Cells Modulate Staphylococcal Enterotoxin B-Induced Effector T-Cell Activation and Acceleration of Colitis
Bibliographic record
Abstract
Oral administration of bacterial superantigen Staphylococcus aureus enterotoxin B (SEB) activates mucosal T cells but does not cause mucosal inflammation. We examined the effect of oral SEB on the development of mucosal inflammation in mice in the absence of regulatory T (Treg) cells. SCID mice were fed SEB 3 and 7 days after reconstitution with CD4(+) CD45RB(high) or CD4(+) CD45RB(high) plus CD4(+) CD45RB(low) T cells. Mice were sacrificed at different time points to examine changes in tissue damage and in T-cell phenotypes. Feeding SEB failed to produce any clinical effect on SCID mice reconstituted with CD4(+) CD45RB(high) and CD4(+) CD45RB(low) T cells, but feeding SEB accelerated the development of colitis in SCID mice reconstituted with CD4(+) CD45RB(high) T cells alone. The latter was associated with an increase in the number of CD4(+) Vbeta8(+) T cells expressing CD69 and a significantly lower number of CD4(+) CD25(+) Foxp3(+) T cells. These changes were not observed in SCID mice reconstituted with both CD45RB(high) and CD45RB(low) T cells. In addition, SEB impaired the development of Treg cells in the SCID mice reconstituted with CD4(+) CD45RB(high) T cells alone but had no direct effect on Treg cells. In the absence of Treg cells, feeding SEB induced activation of mucosal T cells and accelerated the development of colitis. This suggests that Treg cells prevent SEB-induced mucosal inflammation through modulation of SEB-induced T-cell activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".