Cytokine patterns in nasal secretion of non-atopic patients distinguish between chronic rhinosinusitis with or without nasal polys
Bibliographic record
Abstract
BACKGROUND: Being one of the most common nasal diseases, chronic rhinosinusitis (CRS) is subdivided into CRS with nasal polyps (NP) and CRS without nasal polyps (CRSsNP). CRSsNP presents itself with a TH1 milieu and neutrophil infiltration, while NP is characterised by a mixed TH1/TH2 profile and an influx of predominantly eosinophils, plasma cells and mast cells. For the purpose of discovering disease-specific cytokine profiles, the present study compares levels of mediators and cytokines in nasal secretions between CRSsNP, NP, and healthy controls. METHODS: The study included 45 participants suffering from NP, 48 suffering from CRSsNP and 48 healthy controls. Allergic rhinitis constituted an exclusion criterion. Nasal secretions, sampled using the cotton wool method, were analysed for IL-4, IL-5, IL-10, IL-12, IL-13, IL-17, IL-8, GM-CSF, G-CSF, IFN-γ, MCP-1, MIP-1α, MIP-1β, eotaxin, and RANTES, and for ECP and tryptase, using Bio-Plex Cytokine assay or ELISA, respectively. RESULTS: Elevated levels of IL-5, IL-17, G-CSF, MCP-1, MIP-1α, MIP-1β, ECP, and tryptase, as well as decreased levels of IL-10, IL-12, IL-13, and IFN-γ were detected in NP. CRSsNP presented increased levels of RANTES and MIP-1β while IL-13 was decreased. No differences between the three groups were found for IL-4, IL-8, GM-CSF, and eotaxin. CONCLUSIONS: The present work suggests a disequilibrium of TH1 and TH2, together with a down-regulation of regulatory T lymphocytes and up-regulated TH17 in NP. Moreover, elevated levels of diverse mediators represent the activation of various inflammatory cells in this disease entity. The inflammation in CRSsNP, however, is only weakly depicted in nasal secretions. Therefore, cytokines in nasal secretions may provide helpful information for differential diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".