Impact of feeding blends of organic acids and herbal extracts on growth performance, gut microbiota and digestive function in newly weaned pigs
Bibliographic record
Abstract
One hundred eighty newly weaned pigs were used to inves tigate effects of feeding organic acids and herbal extracts on growth performance, gut morphology and microbiota, and immune response in newly weaned pigs during a 4-wk period. There were five dietary treatments: control, Acid 1 (acetic, propionic, phosphoric and citric acid; 1.1% inclusion), Acid 2 (Acid 1 + 1.0% lactic acid), herbal extracts (0.75% inclusion; containing cinnamon, thyme and oregano extract), and antibiotic (110 ppm lincomycin). As compared to the control, pigs on antibiotic and Acid 2 showed higher (P < 0.05) ADG only during week 2 post-weaning, whereas pigs on herbal extract showed lower (P < 0.05) ADG only during week 3 post-weaning. Fecal coliform counts were lower (P < 0.08) in pigs on Acid 1 and 2 on day 4 post-weaning and in pigs on antibiotic and herbal extract on day 14 post-weaning. Fecal lactobacilli counts were lower (P < 0.05) in pigs on antibiotic on day 14 post-weaning. Based on PCR-DGGE, treatment influenced the composition of gut microbiota. The pH of the colon was lower (P < 0.05) in pigs on acid treatments and serum IgG was lower (P < 0.05) in pigs on antibiotic. Dietary treatment did not affect (P > 0.10) intestinal morphology. These results show that the inclusion of antibiotic in the diet reduced the proliferation of both potentially harmful coliform bacteria and potentially beneficial lactobacilli in the pig’s gut, while herbal extract and organic acids appeared to reduce the proliferation of coliform bacteria. Blends of organic acids can serve as an alternative to in-feed antibiotics during the first few weeks post-weaning for pigs. Key words: Piglet, organic acids, herb, microbiota, immune response, intestinal morphology
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".