IL-1β and iNOS can drive the asthmatic comorbidities and decrease of lung function in perennial allergic rhinitis children
Bibliographic record
Abstract
BACKGROUND: Allergic asthma and rhinitis (AR) are closely linked, with a significant proportion of AR patients developing asthma. Identification of the early signs of comorbidity of AR and asthma can enable prompt treatment and prevent asthma progression. OBJECTIVES AND METHODS: This study investigated the role of interleukin-1β (IL-1β), a pro-inflammatory cytokine, and inducible nitric oxide synthase (iNOS) in the comorbidity of AR and asthma and lung function in Korean children with perennial AR (PAR). A cohort of 240 subjects (6 to 10 years old) with PAR (PAR alone: 113 children, PAR and asthma: 127 children) was analyzed for various biomarkers, including IL-1β, iNOS, and epithelial-mesenchymal transition (EMT) markers in serum. The blood levels of eosinophils and immunoglobulin E (IgE) were examined. IL-1β, CCL-24, E-cadherin, and vimentin were measured by enzyme-linked immunosorbent assay (ELISA). Epithelial iNOS was measured by the NOS kit. RESULTS: Elevated levels of IL-1β, iNOS, and vimentin in the serum were identified as significant indicators of the likelihood of comorbidity of PAR and asthma in children. Furthermore, higher concentrations of IL-1β, iNOS, and vimentin have been linked to reduced lung function in PAR children. Notably, IL-1β expression shows a relationship with the levels of E-cadherin, vimentin, and CCL-24. However, no correlation was found between IL-1β and iNOS expressions. CONCLUSIONS: This study suggests that IL-1β and iNOS can be biomarkers in the progression of PAR and asthma and decreased lung function, suggesting potential targets for early intervention and 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 | 0.000 | 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 | 0.001 |
| 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".