137 Effects of dietary supplementation with xylanase and probiotics on growth performance and gut health of newly weaned pigs
Bibliographic record
Abstract
Abstract This study was to evaluate the supplemental effects of xylanase and probiotics on growth performance and gut health of newly weaned pigs. One hundred and forty-four pigs at 3 weeks of age with an initial body weight (BW) at 6.47 ± 0.03 kg were allotted (4 pigs per pen) to 3 dietary treatments based on a randomized complete block design with sex (gilts and barrow) and initial BW as blocks. The treatments were CON (no supplement), XYL (xylanase at 10,000 XU/kg, Xylamax, BRI, Durham, NC), and PRO (Bacillus spp. 6 x 109 CFU/kg plus xylanase 10,000 XU/kg, EnzaPro, BRI). All diets met the NRC (2012) requirements in 3 phases (P1 for 7 d, P2 for 14 d, and P3 for 14 d). The ADG, ADFI, and G/F were measured weekly. On d 21, 1 pig representing a median BW of each pen was euthanized to collect jejunal samples to measure the immune and oxidative stress status, microbiome, and histology. Data were analyzed using the MIXED procedure of SAS. Treatment was a fixed effect and blocks were random effects. There were no fixed effects among treatments on growth performance, immune and oxidative stress status, and histology. Use of xylanase increased (P < 0.05) the Chao1 index in jejunal mucosa of pigs (24.6 to 29.5). Relative abundance of the Enterobacteriaceae in jejunal mucosa tended to decrease (P = 0.083) with the use of xylanase (16.2 to 1.6%). The PRO increased (P < 0.05) the relative abundance of Mitsuokella compared with others treatments (0.9 vs. 4.8%). In conclusion, supplementation of xylanase or Mitsuokella can enhance the microbiome in jejunal mucosa by increasing the diversity and beneficial bacterial abundance, whereas by reducing pathogenic bacterial abundance without affecting the growth performance, histology, immune, and oxidative stress status.
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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) | 0.001 | 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 | 0.001 |
| 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".