Upper and lower respiratory tract microbiota in horses: bacterial communities associated with health and mild asthma (inflammatory airway disease) and effects of dexamethasone
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The microbial composition of the equine respiratory tract, and differences due to mild equine asthma (also called Inflammatory Airway Disease (IAD)) have not been reported. The primary treatment for control of IAD in horses are corticosteroids. The objectives were to characterize the upper and lower respiratory tract microbiota associated with respiratory health and IAD, and to investigate the effects of dexamethasone on these bacterial communities using high throughput sequencing. RESULTS: The respiratory microbiota of horses was dominated by four major phyla, Proteobacteria (43.85%), Actinobacteria (21.63%), Firmicutes (16.82%), and Bacteroidetes (13.24%). Fifty genera had a relative abundance > 0.1%, with Sphingomonas and Pantoea being the most abundant. The upper and lower respiratory tract microbiota differed in healthy horses, with a decrease in richness in the lower airways, and 2 OTUs that differed in abundance. There was a separation between bacterial communities in the lower respiratory tract of healthy and IAD horses; 6 OTUs in the tracheal community had different abundance with disease status, with Streptococcus being increased in IAD horses. Treatment with dexamethasone had an effect on the lower respiratory tract microbiota of both heathy and IAD horses, with 8 OTUs increasing in abundance (including Streptococcus) and 1 OTU decreasing. CONCLUSIONS: The lower respiratory tract microbiota differed between healthy and IAD horses. Further research on the role of Streptococcus in IAD is warranted. Dexamethasone treatment affected the lower respiratory tract microbiota, which suggests that control of bacterial overgrowth in IAD horses treated with dexamethasone could be part of the treatment strategy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it