Neuroinflammation: Extinguishing a blaze of T cells
Bibliographic record
Abstract
Inflammation is a biological process that dynamically alters the surrounding microenvironment, including participating immune cells. As a well-protected organ surrounded by specialized barriers and with immune privilege properties, the central nervous system (CNS) tightly regulates immune responses. Yet in neuroinflammatory conditions, pathogenic immunity can disrupt CNS structure and function. T cells in particular play a key role in promoting and restricting neuroinflammatory responses, while the inflamed CNS microenvironment can influence and reshape T cell function and identity. Still, the contraction of aberrant T cell responses within the CNS is not well understood. Using autoimmunity as a model, here we address the contribution of CD4 T helper (Th) cell subsets in promoting neuropathology and disease. To address the mechanisms antagonizing neuroinflammation, we focus on the control of the immune response by regulatory T cells (Tregs) and describe the counteracting processes that preserve their identity under inflammatory challenges. Finally, given the influence of the local microenvironment on immune regulation, we address how CNS-intrinsic signals reshape T cell function to mitigate abnormal immune T cell responses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".