Glutamine supplementation improves intestinal barrier function in a weaned piglet model of<i>Escherichia coli</i>infection
Bibliographic record
Abstract
The weaning period is associated with an increased prevalence of gastrointestinal infection in many species. Glutamine (Gln) has been shown to improve intestinal barrier function and immune function in both in vivo and in vitro models. The objective of the present study was to determine the effect of dietary Gln supplementation on intestinal barrier function and intestinal cytokines in a model of Escherichia coli infection. We randomised 21-d-old piglets (n 20) to nutritionally complete isonitrogenous diets with or without Gln (4·4 %, w/w) for 2 weeks. Intestinal loops were isolated from anaesthetised pigs and inoculated with either saline or one of the two E. coli (K88AC or K88 wild-type)-containing solutions. Intestinal tissue was studied for permeability, cytokine expression, fluid secretion and tight-junction protein expression. Animals receiving Gln supplementation had decreased potential difference (PD) and short-circuit current (I(sc)) in E. coli-inoculated intestinal loops (PD 0·628 (SEM 0·151) mV; I(sc) 13·0 (SEM 3·07) μA/cm(2)) compared with control-fed animals (PD 1·36 (SEM 0·227) mV; I(sc) 22·4 (SEM 2·24) μA/cm(2)). Intestinal tissue from control, but not from Gln-supplemented, animals responded to E. coli with a significant increase in mucosal cytokine mRNA (IL-1β, IL-6, transforming growth factor-β and IL-10). Tight-junction protein expression (claudin-1 and occludin) was reduced with exposure to E. coli in control-fed animals and was not influenced in Gln-supplemented piglets. Gln supplementation may be useful in reducing the severity of weaning-related gastrointestinal infections, by reducing the mucosal cytokine response and altering intestinal barrier function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".