Characterizing Inflammation of Nasal Epithelial Cells in Children With Obstructive Sleep Apnea
Bibliographic record
Abstract
Abstract Rationale: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder and a chronic inflammatory condition, characterized by upper airway inflammation. Primary human nasal epithelial cell (HNEC) cultures provide a model to study airway epithelial cell physiology and inflammation. However, the role of these cells in pediatric OSA and their inflammatory profile remains poorly understood.The purpose of this study was to characterize the inflammatory profile of HNECs in children with OSA vs. healthy controls at baseline and post viral stimulation. Methods: HNECs were collected from children with OSA and healthy controls. Cells were cultured under air-liquid interface (ALI) conditions for 4-weeks (baseline) then infected with 0.5 MOI RSV. Cytokine expression of IL-6 and IL-8 in the basal media was quantified using Luminex. Results: A total of 6 OSA and 3 control subjects donated cells for study. Week 1 baseline median IL-6 levels (OSA n= 6; Control n= 3) were significantly higher in the control group compared to the OSA group (p=0.047) (Median ± SEM; OSA: 61.98 ± 31.34 vs. controls: 240.20 ± 68.92). At baseline, IL-6 production followed a consistent pattern in the OSA group, with levels peaking at week 2, before steadily declining through week 4. There was no significant difference in IL-6 and IL-8 cytokine concentrations within OSA and control groups over all 4 baseline weeks combined. However, high heterogeneity was observed between individual subjects; values ranged from 7.8 to 930.7pg/ml in IL-6 and from 4837.2 to 8095.0pg/ml in IL-8. Post-infection, while cytokine concentrations increased when compared to baseline values, there was no significant difference in IL-6 and IL-8 concentrations between the OSA and control groups (OSA n=5; Control n=2). Conclusion: Further studies are needed to explore the inflammatory mechanisms of OSA, which may provide insights into the value of potential anti-inflammatory treatments.
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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) | 0.001 | 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".