Resp-Aid in Ova-challenged mice decreased airway inflammatory cell infiltration, AHR and IL-13 cytokine expression levels
Bibliographic record
Abstract
Abstract Introduction Asthma is an inflammatory disease caused by repeated immediate phase hypersensitivity and late-phase allergic reactions in the lungs. Resp-Aid is a formula composed of 9 medicinal herbs that supports in the resolution of respiratory disorders (Coltsfoot, Marshamallow, Plantain, Mullein, Horehound, Licorice, Lobelia, Ginger, and Myrrh). These herbs, alone, have shown positive effects in reducing symptoms associated with asthma. However, the effects of the combination of these herbs on asthma is not well understood. Therefore, we conducted a series of studies to explore the mechanism of action of Resp-Aid. Material and Methods C57Bl/6 male mice (n = 7 each group) were challenged with or without ovalbumin (OVA). After the challenge, they were divided into 4 groups where 2 groups received 1 drop of Resp-Aid twice daily for 7 days and 2 groups acted as control. On day 5 of the treatment with Resp-Aid the airway hyperresponsiveness (AHR) was measured. After the treatment with Resp-Aid, lungs and bronchoalveolar lavage (BAL) fluid were collected and the cytokines (IL-4, IL-5, IL-13, IL-17a, IFNy), MPO and EPO were quantified. Results All OVA-challenged mice had increased AHR, leukocytes and cytokine levels compared to their respective controls showing the development of an asthmatic response. The Resp-Aid asthmatic groups had decreased AHR, IL-13, neutrophils and EPO levels compared to asthmatic mice. Conclusion This data show that Resp-Aid changed the immune response related to asthma, and it may be a potential target for therapeutic intervention in allergic lung disease or asthma.
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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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".