MAST CELLS AS “TUNABLE” EFFECTOR AND IMMUNOREGULATORY CELLS: Recent Advances
Bibliographic record
Abstract
This review focuses on recent progress in our understanding of how mast cells can contribute to the initiation, development, expression, and regulation of acquired immune responses, both those associated with IgE and those that are apparently expressed independently of this class of Ig. We emphasize findings derived from in vivo studies in mice, particularly those employing genetic approaches to influence mast cell numbers and/or to alter or delete components of pathways that can regulate mast cell development, signaling, or function. We advance the hypothesis that mast cells not only can function as proinflammatory effector cells and drivers of tissue remodeling in established acquired immune responses, but also may contribute to the initiation and regulation of such responses. That is, we propose that mast cells can also function as immunoregulatory cells. Finally, we show that the notion that mast cells have primarily two functional configurations, off (or resting) or on (or activated for extensive mediator release), markedly oversimplifies reality. Instead, we propose that mast cells are "tunable," by both genetic and environmental factors, such that, depending on the circumstances, the cell can be positioned phenotypically to express a wide spectrum of variation in the types, kinetics, and/or magnitude of its secretory functions.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".