Comparison of cytokine expression and disease severity between plasma cell-dominant and eosinophil-dominant patients in chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
PURPOSE: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous disease characterized by inflammation of the nasal and sinus mucosa. The inflammatory patterns may differ among patients, leading to different subtypes based on the dominant inflammatory cell type. This study aimed to compare the differences in cytokine expression and disease severity between plasma cell-dominant and eosinophil-dominant subtypes in patients with CRSwNP. METHODS: This study included 53 CRSwNP patients and 19 control subjects who did not have asthma or a history of cigarette smoking. The expression of cytokines and inflammatory cells was assessed via enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry, respectively. RESULTS: Among the cytokines analyzed, only IL-6 was significantly different between the two subtypes. A greater proportion of mast cells and IgE cells was present in plasma cell-dominant CRSwNP patients than in eosinophil-dominant group. For the three disease severity scores (LMK-CT, TPS and SNOT-22), objective scores (LMK-CT and TPS) were greater in the eosinophil-dominant CRSwNP group, while the opposite result was shown for the subjective score (SNOT-22). Additionally, the percentage of plasma cell-dominant cells was significantly positively correlated with disease severity according to the TPS and SNOT-22 scores. CONCLUSIONS: Our data revealed that plasma cell-dominant inflammation, a subtype of type 2 CRS, was significantly correlated with subjective disease severity. The study also highlights the role of IL-6, IgE and mast cells as distinguishing factors between eosinophil-dominant and plasma cell-dominant CRSwNP. This information could be useful for clinical diagnosis and personalized treatment.
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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".