MIR-181A-5P Attenuates Ovalbumin-Induced Allergic Inflammation in Nasal Epithelial Cells by Targeting IL-33/P38 MAPK Pathway
Bibliographic record
Abstract
PURPOSE: Chronic inflammation of the nasal mucosal tissues plays an important role in the pathogenesis of allergic rhinitis (AR). Aberrantly-expressed micro ribonucleic acid (miRNA) has been found to have strong associations with the inflammatory reactions in allergic diseases; however, its functional significance and molecular mechanism in AR remains unclear. The purpose of this study is to determine the functional role and mechanism of miR-181a-5p in AR. METHODS: Allergic inflammatory reaction was induced by ovalbumin in human nasal epithelial cell line RPMI2650. The anti-inflammatory effects of miR-181a-5p were evaluated by examining pro-inflammatory cytokines (interleukin (IL)-1β, IL-6, tumor necrosis factor-α (TNF-α)) in the culture of RPMI-2650 cells stimulated by ovalbumin, using quantitative real-time reverse transcription polymerase chain reaction and enzyme-linked immunosorbent assay. Luciferase assay and gain-of-function assay were used to investigate the association of miR-181a-5p and IL-33/p38 MAPK axis. RESULTS: MiR-181a-5p was significantly downregulated in mucosal tissues of AR patients and in RPMI-2650 cells treated with ovalbumin. The overexpression of miR-181a-5p showed prominent suppression of inflammatory cytokine production in RPMI-2650 cells with the stimulation of ovalbumin. MiR-181a-5p directly targeted, and negatively regulated IL-33 to suppress the activation of p38 MAPK signalling. CONCLUSION: The results suggest that miR-181a-5p restricted allergic inflammation through inhibition of IL-33/p38 MAPK pathway, indicating miR-181a-5p may play an anti-inflammatory role in AR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".