Naringenin inhibits allergen-induced airway inflammation and airway responsiveness and inhibits NF-κB activity in a murine model of asthma
Bibliographic record
Abstract
Naringenin, a flavonoid, has antiinflammatory and immunomodulatory properties. We investigated whether naringenin could attenuate allergen-induced airway inflammation and its possible mechanism in a murine model of asthma. Mice were sensitized and challenged with ovalbumin. Some mice were administered with naringenin before ovalbumin challenge. We evaluated the development of airway inflammation and airway reactivity. Interleukin (IL)4, IL13, chemokine (C-C motif) ligand (CCL)5, and CCL11 in bronchoalveolar lavage fluid and serum total IgE were detected by ELISA. IkappaBalpha degradation and inducible nitric oxide synthase (iNOS) in lungs were measured by Western blot. We also tested NF-kappaB binding activity by electrophoretic mobility shift assay. The mRNA levels of iNOS, CCL5, and CCL11 were detected by real-time PCR. Naringenin attenuated ovalbumin-induced airway inflammation and airway reactivity in experimental mice. The naringenin-treated mice had lower levels of IL4 and IL13 in the bronchoalveolar lavage fluid and lower serum total IgE. Furthermore, naringenin inhibited pulmonary IkappaBalpha degradation and NF-kappaB DNA-binding activity. The levels of CCL5, CCL11, and iNOS were also significantly reduced. The results indicated that naringenin may play protective roles in the asthma process. The inhibition of NF-kappaB and the decreased expression of its target genes may account for this phenomenon.
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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 | 0.001 | 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 | 0.002 |
| 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".