The Role of Mast Cells in IgE-Independent Lung Diseases
Bibliographic record
Abstract
progenitors and play a role in orchestrating adaptive immune responses. They have a well-known role in allergic reactions following immunoglobulin (Ig)E-mediated activation of the cell-surface expressed IgE high-affinity receptor (FcεRI). MCs can also respond to various other stimuli due to the expression of a variety of receptors including toll-like receptors (TLRs), immunoglobulin (IgG) receptors (FcγR), complement receptors such as C5a (CD88) expressed by skin MCs, neuropeptides receptors including nerve growth factor receptor, (NGFR), cytokines receptors such as (IL)-1R and IL-3R, and chemokines receptors including CCR-1 and CCR-3. MCs release three groups of mediators upon degranulation differentiated according to their chemical composition, storage, and time to release. These include preformed mediators (mainly histamine, tryptase, and chymase), de novo synthesized mediators such as prostaglandin (PG)D2, leukotriene (LT)B4 and LTD4, and cytokines including IL-1β, IL-3, tumor necrosis factor (TNF)α, and transforming growth factor(TGF)-β. Emerging evidence indicates a role for IgE-independent MC activation in the late-stage asthmatic response as well as in non-allergic airway diseases including chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), and lung cancer. MC infiltration/activation has been reported in some, but not all, studies of lung cancer. MC-derived TNF-α possesses tumor-suppressive activity while IL-1β supports tumor progression and metastasis. In IPF lungs, an increase in density of tryptase- and chymase-positive MCs (MCTC) and overexpression of TGF-β support the fibrosis progression. MC-derived chymase activates latent TGF-β that induces the differentiation of fibroblasts to matrix-producing myofibroblasts. In summary, increasing evidence highlights a critical role of MCs in non-allergic diseases that may indicate new approaches for therapy.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".