Local eosinophils are associated with increased IgA subclass levels in the sinonasal mucosa of chronic rhinosinusitis with polyp patients
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) describes an inflammatory condition affecting the sinonasal mucosa. As the immune system players such as immunoglobulins play prominent roles in the development of CRS, we aimed to investigate the expression of IgA subclasses and factors involved in IgA class switching in the sinonasal mucosa of CRS patients. METHODS: Specimens were collected from the sinonasal mucosa of the healthy controls and CRS patients. Histological assessments were performed by H&E and immunohistochemistry. Real-time PCR and ELISA methods were applied to measure gene expression and protein levels extracted from tissue samples, respectively. RESULTS: We observed that total IgA and subclass-positive cells were higher in the patient groups than controls. There was a significant correlation between the number of eosinophils and total IgA and subclasses-positive cells (Pv < 0.0001). The expression of CXCL13, BAFF, AID, and germline transcripts were increased in CRSwNP patients. In contrast to IgA2 levels, IgA1 levels were significantly increased in the sinonasal tissue of CRSwNP patients (Pv < 0.01). TGF-β was significantly elevated in the sinonasal tissue of patients with CRSsNP. CONCLUSIONS: Increased protein levels of IgA subclasses and related antibody-producing cells were associated with elevated eosinophils in CRSwNP patients which may result in eosinophil pathological functions. Several therapeutic approaches might be developed to modulate the IgA production to ameliorate the inflammatory mechanisms in CRSwNP patients.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".