The role of innate lymphoid cells in chronic rhinosinusitisseverity in children: a cross-sectional study
Bibliographic record
Abstract
Introduction:The microbiome has been identified as a significant factor in the pathogenesis of various inflammatory and autoimmune diseases, including chronic rhinosinusitis (CRS), a condition that affects up to 12% of the global population.Aim: To evaluate the role of innate lymphoid cells (ILC1, ILC2, and ILC3) and their relationship with nasal microbiota in CRS in children. Material and methods:We assessed the clinical, microbiological, and immunological characteristics of 63 children with CRS.We evaluated disease severity using the Sinus and Nasal Quality of Life Survey (SN-5) and measured ILC1, ILC2, and ILC3 levels in nasal scrapings; microbial diversity was expressed as OTU richness.Results: We found a statistically significant relationship between ILC1 levels and CRS severity, suggesting a potential role of ILC1 in the development of the disease.ILC3 levels were significantly associated with lower microbial richness.While atopy was more common in children with high levels of ILC2, the relationship was not significant.Conclusions: Our results indicate that innate lymphoid cells may play a significant role in the inflammatory processes underlying the development and severity of chronic rhinosinusitis, with ILC1 activation being particularly strongly associated with CRS severity in young children.Additionally, ILC3 may play a role in modulating the nasal microbiome in CRS patients, but the relationship is not strong enough to significantly impact the clinical characteristics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".