Growth performance of piglets during the first two weeks of lactation affects the development of the intestinal microbiota
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of newborn piglet weight gain during the first 2 weeks of lactation on the luminal and mucosal microbiota of the ileum and colon. The microbiota from high-weight-gain (HWG) and low-weight-gain (LWG) 2-week-old piglets was characterized by amplicon length heterogeneity PCR (LH-PCR) and compared using diversity indices and multivariate statistical analyses. At birth, LWG piglets weighted in average 0.26 kg less than HWG piglets (p = .002). The weight difference between LWG and HWG piglets increased with time and reached 2.1 kg after 16 days of lactation (p < .0001). Based on these growth performance differences, estimated colostrum and milk intake was greater in HWG than in LWG piglets (p < .0001). Analysis of the LH-PCR data of the microbiota using non-metric multidimensional scaling (NMS) and blocked multiresponse permutation procedure (MRBP) revealed that the microbiota of the HWG and LWG piglets tended to differ in ileal mucosa (p = .097) and differed in colonic lumen (p = .024). The microbiota of HWG piglets had higher levels of Bacteroidetes, Bacteroides and Ruminoccocaceae, and lower proportions of Actinobacillus porcinus and Lactobacillus amylovorus when compared with those of LWG piglets. As the weight gain of nursing piglets is highly correlated with the amount of ingested colostrum and milk, the results strongly suggest that colostrum and milk intake in the first 2 weeks of life influenced the development of the gut microbiota.
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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 | 0.001 |
| 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 | 0.001 |
| 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".