Antimicrobial treatment reduces intestinal microflora and improves protein digestive capacity without changes in villous structure in weanling pigs
Bibliographic record
Abstract
The immediate post-weaning period is often associated with gut malfunction and diarrhoea for young pigs. Administration of antimicrobials remains an effective way to control weaning diarrhoea but it remains unclear how they affect gut physiology and microbiology although this is a prerequisite for being able to devise better alternatives. Hence, for 7 d we treated pigs, weaned at 24 d of age, with a combination of amoxicillin (25 mg/kg feed and injection of 8.75 mg/kg body weight per 12 h) and ZnO (2.5 g/kg feed). The pigs treated with antimicrobials (n 11) showed no signs of gut malfunction at any time, whereas untreated weaned controls (n 11) developed clinical diarrhoea. The antimicrobial treatment resulted in a higher daily weight gain compared with weaned controls (101 v. -44 g/d, P < 0.0001), whereas both groups had a similar degree of villous atrophy compared with unweaned 24-d-old controls (n 8; P < 0.05). The antimicrobial treatment gave a dramatic reduction in small intestinal microbial diversity, and specifically prevented tissue colonization with Escherichia coli compared with weaned controls. Further, the antimicrobial treatment improved amylase, trypsin and small intestinal aminopeptidase A and N activities (all P < 0.05). Specifically for the colon, the antimicrobial treatment was associated with reduced tissue weight ( -23 %, P < 0.05), reduced concentration of SCFA (P < 0.05), and increased mucosal goblet cell area (P < 0.0001) compared with weaned controls. We conclude that the beneficial effects of antimicrobials are mediated not only through reduction in intestinal bacterial load, but also through a stimulation of protein digestive function and goblet cell density.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".