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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".