IL-17 Enhances Chemotaxis of Primary Human B Cells during Asthma
Bibliographic record
Abstract
IL-17 is a pro-inflammatory mediator that is believed to play a critical role in regulating tissue inflammation during asthma, COPD, as well as other inflammatory disorders. The level of expression of IL-17 has been shown to be upregulated in lung bronchial tissue of asthmatic patients. Several reports have provided further evidence that this cytokine could play a key role in enhancing the migration of inflammatory as well as structural cells of the bronchial lung tissue during asthma and COPD. B cell infiltration to sites of inflammation during inflammatory disorders such as bowel disease, asthma and COPD has been reported. Accordingly, in this study we hypothesized that IL-17 may exert a chemotactic effect on primary B cells during asthma. We observed that B cells from asthmatic patients expressed significantly higher levels of IL-17RA and IL-17RC, compared to those of healthy subjects. Using an in-vitro migration assay, B cells were shown to migrate towards both IL-17A and IL-17F. Interestingly, blocking IL-17A and IL-17F signaling using either anti-IL-17R antibodies or MAP kinase inhibitors prevented in vitro migration of B cell towards IL-17. These observations indicate a direct chemotactic effect of IL-17 cytokines on primary peripheral blood B cells with higher effect being on asthmatic B cells. These findings revealed a key role for IL-17 in enhancing the migration of B cells to the lung tissue during asthma or COPD.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".