Neutrophils That Infiltrate the Central Nervous System Regulate T Cell Responses
Bibliographic record
Abstract
Regulation of inflammatory responses is critical to progression of organ-specific autoimmune disease. Although many candidate cell types have been identified, immunoregulatory activity has rarely been directly assayed and never from the CNS. We have analyzed the regulatory capability of Gr-1high neutrophils isolated from the CNS of mice with experimental autoimmune encephalomyelitis. Proportions of neutrophils were markedly increased in the CNS of IFN-gamma-deficient mice. Strikingly, CNS-derived neutrophils, whether or not they derived from IFN-gamma-deficient mice, were potent suppressors of T cell responses to myelin or adjuvant Ags. Neutrophil suppressor activity was absolutely dependent on IFN-gamma production by target T cells, and suppression was abrogated by blocking NO synthase. These data identify an immunoregulatory capacity for neutrophils, and indicate that interplay between IFN-gamma, NO, and activated Gr-1high neutrophils within the target organ determines the outcome of inflammatory and potentially autoimmune T cell responses.
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 | 0.000 |
| 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".