Mechanisms of TREG cell adaptation to inflammation
Bibliographic record
Abstract
Abstract Inflammation is an important defense mechanism. In this complex and dynamic process, drastic changes in the tissue micro-environment play key roles in dictating the nature of the evolving immune response. However, uncontrolled inflammation is detrimental, leading to unwanted cellular damage, loss of physiological functions, and even death. As such, the immune system possesses tools to limit inflammation while ensuring rapid and effective clearance of the inflammatory trigger. Foxp3+ regulatory T (TREG) cells, a potently immunosuppressive CD4+ T cell subset, play a crucial role in immune tolerance by controlling the extent of the response to self and non-self Ags, all-the-while promoting a quick return to immune homeostasis. TREG cells adapt to changes in the local micro-environment enabling them to migrate, proliferate, survive, differentiate, and tailor their suppressive ability at inflamed sites. Several inflammation-associated factors can impact TREG cell functional adaptation in situ including locally released alarmins, oxygen availability, tissue acidity and osmolarity and nutrient availability. Here, we review some of these key signals and pathways that control the adaptation of TREG cell function in inflammatory settings.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".