Shift of Microbiota and Modulation of Resistome in the Ceca of Broiler Chicken Fed Berry Pomace Alone or in Combination of a Multienzyme Mixture
Bibliographic record
Abstract
Alternative feed additives are being investigated due to the restriction of antibiotics use to decrease antimicrobial resistance (AMR) in food-producing animals. This study investigated the effects of dietary American cranberry (Vaccinium macrocarpon) and wild blueberry (V. angustifolium) pomaces on the cecal microbiota and resistome profiles as well as the short-chain fatty acid levels. Male broiler chickens Cobb500 were fed a basal diet with either 55 ppm bacitracin methylene disalicylate (BMD); 0.5% (CRP0.5) and 1% (CRP1) cranberry pomace; and 0.5% (LBP0.5) and 1% (LBP1) lowbush blueberry pomace with or without a multienzyme mixture (ENZ). The results showed that at 21 days of age, the total coliform counts decreased in the CRP0.5-fed birds compared to BMD (p < 0.05). The use of pomace significantly increased the abundance of Lactobacillus and Bacteroides regardless of ENZ, while CRP decreased the Proteobacteria phylum abundance. In-feed ENZ tended to increase the relative abundance of genes conferring aminoglycoside resistance. Treatment with CRP0.5 decreased the abundance of cepA genes encoding for macrolide (MACROLIDE) and lincomycin (InuD) resistance while increasing those for tetracycline (tetO and tetX) resistance. These results showed, for the first time, the potential of the studied enzymes in influencing berry pomace’s effects on antimicrobial resistance gene profiles in broilers.
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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 | 0.000 | 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".