Expression and release of IL‐29 by mast cells and modulation of mast cell behavior by IL‐29
Bibliographic record
Abstract
BACKGROUND: The role of interleukin (IL)-29 in innate immunity has been recognized recently, and it is regarded as a potent bioactive molecule. However, little is known about its role in the pathogenesis of allergy. Because mast cells are recognized as primary effector cells of allergy, we investigated the potential relationship between IL-29 and mast cells in this study. OBJECTIVE: To examine the expression of IL-29 in mast cells and the influence of IL-29 on mast cell mediator release and accumulation. METHODS: Expression of IL-29 in mast cells was determined by double-labeling immunohistochemistry and flow cytometry analysis. Mast cell cell-line was cultured to examine the mediator release, and mouse peritoneal model was employed to observe the mast cell accumulation. RESULTS: Large proportions of mast cells expressing IL-29 were localized in human tissue including the colon, tonsil and lung. Mast cells can release substantial quantity of IL-29 upon challenge with proteolytic allergens. Extrinsic IL-29 provoked IL-4 and IL-13 release from mast cell line P815 cells through PI3K/Akt and (JAK)/STAT3 signaling pathways, but failed to induce mast cell histamine release from human mast cells. Extrinsic IL-29 also induced mast cell infiltration in mouse peritoneum by a CD18- and ICAM1-dependent mechanism. CONCLUSION: Mast cell-derived IL-29 has the potential to be involved in the pathogenesis of allergic inflammation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".