Interleukin-15 increases neutrophil adhesion onto human respiratory epithelial A549 cells and attracts neutrophils<i>in vivo</i>
Bibliographic record
Abstract
Interleukin-15 (IL-15) is a neutrophil agonist that plays a role in inflammatory disorders, including a variety of pulmonary diseases. Adhesion of neutrophils onto pulmonary cells is a major event leading to development of inflammation. Recently, elevated levels of IL-15 have been associated with different pulmonary diseases. There is no clear evidence that IL-15 modulates cell surface expression of adhesion molecules in neutrophils, or that IL-15 is involved in neutrophil adhesion onto pulmonary cells. Also, it is not clear if IL-15 induces a neutrophilic inflammation in vivo. This study was aimed at elucidation of these issues. Neutrophils were treated with IL-15 and cell surface expression of CD11a, CD11b, CD11c and CD18 was monitored by flow cytometry. The human respiratory epithelial A549 cell line was used as a substrate for the neutrophil adhesion assay and cell surface expression of CD50, CD54 and CD106 was monitored in IL-15-induced A549 cells. The murine air pouch model was used for investigating potential neutrophilic inflammation induced by IL-15 in vivo. IL-15 significantly increased neutrophil cell surface expression of CD11b and CD18 and up-regulated A549 cell surface expression of CD54. Moreover, A549 cells were found to express IL-15R components and adhesion of neutrophils onto A549 cells was increased when neutrophils or A549 cells were treated with IL-15. Finally, IL-15 induced neutrophilic inflammation in vivo and concentrations of IL-6 and CXCL2/MIP-2 were increased in IL-15-induced pouches. IL-15 might participate in inflammatory pulmonary diseases by attracting neutrophils, modulating cell surface expression molecules and increasing neutrophil adhesion onto pulmonary cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".