Boosting Regulatory T Cells Limits Neuroinflammation in Permanent Cortical Stroke
Bibliographic record
Abstract
Inflammatory mechanisms contribute substantially to secondary tissue injury after brain ischemia. Regulatory T cells (Tregs) are key endogenous modulators of postischemic neuroinflammation. We investigated the potential of histone deacetylase inhibition (HDACi) to enhance Treg potency for experimental stroke in mice. HDACi using trichostatin A increased the number of Tregs and boosted their immunosuppressive capacity and interleukin (IL)-10 expression. In vivo treatment reduced infarct volumes and behavioral deficits after cortical brain ischemia, attenuated cerebral proinflammatory cytokine expression, and increased numbers of brain-invading Tregs. A similar effect was obtained using tubastatin, a specific inhibitor of HDAC6 and a key HDAC in Foxp3 regulation. The neuroprotective effect of HDACi depended on the presence of Foxp3(+) Tregs, and in vivo and in vitro studies showed that the anti-inflammatory cytokine IL-10 was their main mediator. In summary, modulation of Treg function by HDACi is a novel and potent target to intervene at the center of neuroinflammation. Furthermore, this novel concept of modulating endogenous immune mechanisms might be translated to a broad spectrum of diseases, including primary neuroinflammatory and neurodegenerative disorders.
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) | 0.001 | 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".