Cytokine Profile of Chronic Sinusitis in Patients With Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: The inflammatory-cell and cytokine profiles of chronic sinusitis (CS) are well documented in the literature. In contrast, little is known about the pathogenesis of this condition in patients with cystic fibrosis (CF). OBJECTIVE: To determine whether patients with CF have inflammatory-cell and cytokine profiles that differ from other patients with CS. METHODS: Patients with CF (n = 7) and adults with CS (n = 7) undergoing functional endoscopic sinus surgery were recruited for the study. Patients with no allergies or sinus disease (n = 6) were used as controls. Using immunohistochemical analysis, we assessed sinus mucosal specimens for the presence of T lymphocytes, eosinophils, macrophages, and neutrophils. Using in situ hybridization, we assessed the expression of interleukin (IL) 4, IL-5, IL-8, IL-10, and interferon gamma. RESULTS: There was a higher number of neutrophils, macrophages, and cells expressing messenger RNA for interferon gamma and IL-8 in patients with CF than in patients with CS or in controls (P<.01). The number of eosinophils and cells expressing messenger RNA for IL-4, IL-5, and IL-10 was higher in patients with CS than in those with CF and controls (P<.01). CONCLUSIONS: Sinus disease in patients with CF presents different inflammatory-cell and cytokine profiles than that seen in other patients with CS. These results may explain the difference in response to treatment in the CF group.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".