Randomised study of the immunomodulatory effects of azithromycin in severely asthmatic horses
Bibliographic record
Abstract
Neutrophilic inflammation is believed to contribute to the airway obstruction and remodelling in equine asthma. Azithromycin, an antibiotic with immunomodulatory properties, reduces pulmonary neutrophilia and hyper-responsiveness in human asthmatics and decreases airway remodelling in rodent models of asthma. It was therefore hypothesised that azithromycin would improve lung function, mucus accumulation and central airway remodelling by decreasing luminal neutrophilia in severe equine asthma. The effects of a 10-day treatment with either azithromycin or ceftiofur, an antimicrobial without immune-modulating activity, were assessed using a blind, randomised, crossover design with six severe asthmatic horses in clinical exacerbation. Lung function, tracheal mucus accumulation, tracheal wash bacteriology, bronchial remodelling, airway neutrophilia and mRNA expression of proinflammatory cytokines (interleukin (IL)-8, IL-17A, IL-1β, tumour necrosis factor-α) in bronchoalveolar lavage fluid were evaluated. Azithromycin decreased the expression of IL-8 (P=0.03, one-tailed) and IL-1β (P=0.047, one-tailed) but failed to improve the other variables evaluated. Ceftiofur had no effect on any parameter. The reduction of neutrophilic chemoattractants (IL-8, IL-1β) justifies further efforts to investigate the effects of a prolonged treatment with macrolides on airway neutrophilia and remodelling. The lack of efficacy of ceftiofur suggests that severe equine asthma should not be treated with antibiotics at first-line therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".