Impact of fecal sample preservation and handling techniques on the canine fecal microbiota profile
Bibliographic record
Abstract
Canine fecal microbiota profiling provides insight into host health and disease. Standardization of methods for fecal sample storage for microbiomics is currently inconclusive, however. This study investigated the effects of homogenization, the preservative RNAlater, room temperature exposure duration, and short-term storage in the fridge prior to freezing on the canine fecal microbiota profile. Within 15 minutes after voiding, samples were left non-homogenized or homogenized and aliquoted, then kept at room temperature (20-22°C) for 0.5, 4, 8, or 24 hours. Homogenized aliquots then had RNAlater added or not. Following room temperature exposure, all aliquots were stored in the fridge (4°C) for 24 hours prior to storing in the freezer (-20°C), or stored directly in the freezer. DNA extraction, PCR amplification, then sequencing were completed on all samples. Alpha diversity (diversity, evenness, and richness), and beta diversity (community membership and structure), and relative abundances of bacterial genera were compared between treatments. Homogenization and RNAlater minimized changes in the microbial communities over time, although minor changes in relative abundances occurred. Non-homogenized samples had more inter-sample variability and greater changes in beta diversity than homogenized samples. Storage of canine fecal samples in the fridge for 24 h prior to storage in the freezer had little effect on the fecal microbiota profile. Our findings suggest that if immediate analysis of fecal samples is not possible, samples should at least be homogenized to preserve the existing microbiota profile.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".