The effect of epidermal growth factor on performance and oxidative stress in piglets challenged with enterotoxigenic <i>Escherichia coli</i> K88
Bibliographic record
Abstract
This study evaluated the efficacy of epidermal growth factor (EGF) in piglets challenged with enterotoxigenic Escherichia coli K88 (ETEC). A total of 28 piglets were assigned to the following dietary treatments for 14 d: negative control (NC) (basal diet containing supernatant without EGF), PC (NC + 2.5 g antibiotic·kg−1 feed), EGF120 [basal diet + supernatant with 120 μg EGF·kg−1 body weight (BW)·d−1], and EGF180 (basal diet + supernatant with 180 μg EGF·kg−1 BW·d−1). After a 6 d acclimation period, each pig was gavaged with 6 mL (2.4 × 1013 cfu·mL−1) of ETEC on the morning of day 7. Overall, piglets fed the EGF and PC diets tended to have higher gain to feed ratio than those fed the NC diet (P = 0.063). Pigs fed EGF diets had lower rectal temperature than those fed the NC diet at 6 h after challenge (P < 0.05). Serum and ileal malondialdehyde concentrations were higher in piglets fed the NC diet compared with those fed EGF and PC diets on days 6 and 7 after challenge, respectively (P < 0.05). In conclusion, EGF has the potential to reduce oxidative stress and body temperature elevation in piglets exposed to ETEC while supporting better feed efficiency.
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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 | 0.001 | 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".