Effects of Chitosan on Intestinal Inflammation in Weaned Pigs Challenged by Enterotoxigenic Escherichia coli
Bibliographic record
Abstract
The aim of this study was to investigate whether supplementation with chitosan (COS) could reduce diarrhea and to explore how COS alleviates intestinal inflammation in weaned pigs. Thirty pigs (Duroc×Landrace×Yorkshire, initial BW of 5.65±0.27) weaned at age 21 d were challenged with enterotoxigenic Escherichia coli during a preliminary trial period, and then divided into three treatment groups. Pigs in individual pens were fed a corn-soybean meal diet, that contained either 0 (control), 50 mg/kg chlortetracycline, or 300 mg/kg COS for 21 days. The post-weaning diarrhea frequency, calprotectin levels and TLR4 protein expression were decreased (P<0.05) in both the COS and chlortetracycline groups compared with control. Simultaneously, supplemental COS and chlortetracycline had no effect on the mRNA expression of TNF-α in the jejunal mucosa, or on the concentrations of IL-1β, IL-6 and TNF-α in serum. However, COS supplementation improved (P<0.05) the mRNA expression of IL-1β and IL-6 in the jejunal mucosa. The results indicate that supplementation with COS at 300 mg/kg was effective for alleviating intestinal inflammation and enhancing the cell-mediated immune response. As feed additives, chitosan and chlortetracycline may influence different mechanisms for alleviating inflammation in piglets.
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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".