Initial description of the core ocular surface microbiome in dogs: Bacterial community diversity and composition in a defined canine population
Bibliographic record
Abstract
OBJECTIVE: To characterize the bacterial community residing on the conjunctiva of clinically healthy dogs. METHODS: Bacterial DNA from conjunctival swabs of 10 dogs with normal ocular examinations (both OD and OS, n = 20) was extracted, and 16S rRNA amplicons were sequenced using Illumina MiSeq 600. Resulting data were subjected to quality control steps, and analyzed for bacterial community richness and diversity, within- and between-group dissimilarity, and relative taxonomic composition. RESULTS: High-quality reads (2.22 million bp) resulted in a mean of 159 068 sequences per sample. Bacterial community evenness and diversity was high when compared to other species, and did not significantly differ when samples were grouped by dogs or eyes. As expected, within-dog samples were more similar than between-dog samples. Taxonomic classification revealed that >95% of the community consisted of Firmicutes (34.9 ± 8.8%), Actinobacteria (26.3 ± 7.1%), Proteobacteria (26.2 ± 6.6%), and Bacteroidetes (9.4 ± 2.4%). Key members of the dog ocular surface microbiome, found in all dogs and corresponding to >25% of all identified OTUs (operational taxonomic units), were part of the Bifidobacteriaceae, Lachnospiraceae, Moraxellaceae, Corynebacteriaceae families. Genera previously thought to account for the majority of the core ocular surface microbiome in the dog (Staphylococcus sp., Streptococcus sp., and Bacillus sp.) were associated with only 2.63% of overall reads. CONCLUSIONS: This study shows the feasibility of conjunctival swabs and high-throughput sequencing to profile the bacterial community structure of the canine ocular surface. A core ocular surface microbiome was identified for this canine population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".