The role of G protein‐coupled receptors in mast cell activation by antimicrobial peptides: is there a connection?
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are ancient and essential elements of the host defense system, which are found in a wide variety of species. They show antimicrobial activity against a wide range of pathogenic microorganisms. In addition, AMPs are expressed by different immune cells and have a important function in host innate immune response against pathogens by mechanisms that are different from those involved in direct microbial cytolysis. One host innate immune response that is directly activated by AMPs involves induction of localized inflammation through interaction with mast cells. Activation of mast cells releases pre-formed mediators, cytokines, chemokines and eicosaniods, which influence recruitment, survival, phenotype and functions of many immune cells. Mast cells can respond to AMPs independent of antigen and Fc epsilon receptor 1 stimulation. One of these pathways involves G protein-coupled receptor signaling, which can lead to mast cell degranulation. Whether AMPs activate G proteins in mast cells through a receptor-dependent or a receptor-independent mechanism remains poorly understood and there are a great many questions that have yet to be answered. In this review, we will discuss the possible involvement and role of GPCRs in mast cells activation by AMPs and the gaps in our current understanding of this important interaction.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".