Mast Cell Responses to Viruses and Pathogen Products
Bibliographic record
Abstract
Mast cells are well accepted as important sentinel cells for host defence against selected pathogens. Their location at mucosal surfaces and ability to mobilize multiple aspects of early immune responses makes them critical contributors to effective immunity in several experimental settings. However, the interactions of mast cells with viruses and pathogen products are complex and can have both detrimental and positive impacts. There is substantial evidence for mast cell mobilization and activation of effector cells and mobilization of dendritic cells following viral challenge. These cells are a major and under-appreciated local source of type I and III interferons following viral challenge. However, mast cells have also been implicated in inappropriate inflammatory responses, long term fibrosis, and vascular leakage associated with viral infections. Progress in combating infection and boosting effective immunity requires a better understanding of mast cell responses to viral infection and the pathogen products and receptors we can employ to modify such responses. In this review, we outline some of the key known responses of mast cells to viral infection and their major responses to pathogen products. We have placed an emphasis on data obtained from human mast cells and aim to provide a framework for considering the complex interactions between mast cells and pathogens with a view to exploiting this knowledge therapeutically. Long-lived resident mast cells and their responses to viruses and pathogen products provide excellent opportunities to modify local immune responses that remain to be fully exploited in cancer immunotherapy, vaccination, and treatment of infectious diseases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".