Evaluating the liver abscess microbiota of beef cattle during a reduction in tylosin supplementation shows differences according to abscess size and fraction
Bibliographic record
Abstract
Liver abscesses (LA) resulting from bacterial infection in cattle pose a significant global challenge to the beef and dairy industries. Economic losses from liver discounts at slaughter and reduced animal performance drive the need for effective mitigation strategies. Tylosin phosphate supplementation is widely used to reduce LA occurrence, but concerns over antimicrobial overuse emphasize the urgency to explore alternative approaches. Understanding the microbial ecology of LA is crucial to this, and we hypothesized that a reduced timeframe of tylosin delivery would alter LA microbiomes. We conducted 16S rRNA sequencing to assess severe liver abscess bacteriomes in beef cattle supplemented with in-feed tylosin. Our findings revealed that shortening tylosin supplementation did not notably alter microbial communities. Additionally, our findings highlighted the significance of sample processing methods, showing differing communities in bulk purulent material and the capsule-adhered material. Fusobacterium or Bacteroides ASVs dominated LA, alongside probable opportunistic gut pathogens and other microbes. Moreover, we suggest that liver abscess size correlates with microbial community composition. 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 LA while reducing reliance on in-feed antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".