Relationship between the bacterial ocular surface microbiota and outcomes for cats with feline herpesvirus type 1 ocular surface disease
Bibliographic record
Abstract
OBJECTIVE: Feline herpesvirus 1 (FHV-1) causes ocular surface disease in domestic cats. The purpose of this study was to assess the relationship between bacterial ocular surface microbiota and outcomes for cats with FHV-1 ocular surface disease. ANIMALS STUDIED: Twenty-two shelter-housed cats with confirmed FHV-1 ocular surface disease. PROCEDURES: Animals were grouped according to FHV-1 shedding and ocular clinical scores following intervention: worsened outcome (WorOut, n = 11) or improved outcome (ImpOut, n = 11). Scoring and conjunctival sampling were completed on Days 1 and 8 of twice daily antiviral treatment. Bacterial DNA was extracted and submitted for 16S rRNA gene sequencing. Real-time polymerase chain reaction was performed for selected bacterial species. Overall DNA concentration between groups was assessed. RESULTS: Bacterial microbiota relative abundance composition was significantly different between ImpOut and WorOut groups (weighted UniFrac p = .006). Alpha diversity was significantly higher in the ImpOut group compared with the WorOut group (Shannon p = .042, Simpson's p = .022, Pielou's p = .037). Differences in the relative abundance of various phyla and species were detected between groups. Total DNA concentration was higher in the WorOut group compared with the ImpOut group (p = .04). Feline GAPDH (p = .001) and Bilophila wadsworthia (p = .024) copy number was significantly higher in the ImpOut group compared with the WorOut group. CONCLUSIONS: The results highlight the important relationship between the bacterial ocular surface microbiota and FHV-1 infection outcomes in cats treated with antiviral medications. Low bacterial species diversity, higher overall DNA (presumed predominantly bacterial) load, and certain bacterial phyla/species were associated with poor outcomes for cats with FHV-1 ocular disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".