Nebulization of 2% lidocaine has no detectable impact on the healthy equine respiratory microbiota
Bibliographic record
Abstract
Glucocorticosteroids remain the most common pharmaceutical approach for the treatment of equine asthma but can be associated with significant side effects, including respiratory microbiome alterations. The goal of the study was to assess the impact of 2% lidocaine nebulization, a projected alternative treatment of equine asthma, on the healthy equine respiratory microbiota. A prospective, randomized, controlled, blinded, 2-way crossover study was performed, to assess the effect of 1 mg/kg 2% lidocaine (7 treatments over 4 days) on the equine respiratory microbiota compared to control horses (saline and no treatment). Clinical assessments and respiratory samples, including nasal wash, endoscopic tracheal aspirate and bronchoalveolar lavage fluid, were obtained at each sample collection timepoint. The profile of the respiratory bacterial microbiota was evaluated using 16S amplicon sequencing, and clinical data compared using related samples analyses, based on data normality. The treatment did not affect the clinical data or alter the tracheal and nasal microbiota in healthy horses. However, time explained 12.6% of microbiota variation among samples. A significant difference in bacterial composition was observed between nasal and tracheal samples, showing the greatest relative abundance of Actinobacteria and Firmicutes, respectively. Bacterial DNA from bronchoalveolar lavage fluid did not amplify with generic primers targeting the V4 variable region of the prokaryotic small subunit ribosomal RNA gene, despite attempting multiple DNA extraction methods and PCR protocols, and after excluding PCR inhibition. This observation indicates that bronchoalveolar lavage fluid of healthy horses has a low bacterial load.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".