PSV-28 Liver abscess microbiota of beef cattle administered in-feed tylosin differ according to abscess size and fraction
Bibliographic record
Abstract
Abstract Liver abscesses (LA) pose a significant challenge to the Canadian beef industry as they are estimated to cost the industry ~ $61.2 million annually. This is likely an underestimate as it does not account for losses in animal productivity. Tylosin phosphate is widely used to reduce LA, but concerns over antimicrobial use selecting for antimicrobial resistance has created an urgency to explore alternative approaches. Understanding the impact of tylosin on the microbial ecology of LA is crucial to this. We hypothesized that altering the duration of in-feed administration of tylosin to feedlot cattle would alter the microbial community of LA. To investigate this, we collected abscessed livers from cattle fed a diet containing tylosin 1) throughout finishing, 2) during the first 78% of the feeding or 3) during the last 75% of the feeding period. We examined LA microbial ecology of purulent material, abscess capsule tissue, originating from abscesses of different sizes using a metataxonomic approach. Our findings revealed that shortening tylosin administration did not notably alter the alpha (P > 0.05) or beta-diversity (P > 0.05) of LA microbial communities. There was a significant difference in microbial richness associated with abscess capsule (P < 0.05) compared with bulk purulent material. Fusobacterium or Bacteroides ASVs dominated LA microbiomes, alongside probable opportunistic gut pathogens and other bacteria. Interestingly, classifying samples based on whether they originated from a liver with a single abscess, multiple abscesses or a very large abscess tended to differ in microbiome composition (P = 0.06). These insights contribute to our understanding of factors impacting liver abscess microbial ecology and will be valuable in identifying antibiotic alternatives. They underscore the importance of exploring varied approaches to address liver abscesses while reducing reliance on in-feed antibiotics.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".