Intestinal expression of pro-infiammatory cytokines induced by oral intake of lipopolysaccharide (LPS) from E. coli in weaned pigs
Bibliographic record
Abstract
Background: treatments with lipopolysaccharide (LPS) from E. coli are an accepted way of inducing inflammation in immunological studies since they have the ability to activate a coordinate series of signs through the synthesis of pro-inflammatory cytokines such as interleukin 8 (IL-8), IL-18 and tumor necrosis factor alpha (TNF-α), which can cause significant changes in intestinal structure and functionality. Objective: the aim of this study was to evaluate the effect of adding LPS of E. coli on pro-inflammatory cytokine gene expression IL-8, IL-18 and TNF-α in early-weaned pigs. Methods: fieldwork was conducted at Centro San Pablo, belonging to the Universidad Nacional de Colombia with 32 pigs at 21 days of age, with 6.5 ± 0.5 kg of weight. Animals were fed with a basal diet supplemented with two levels of inclusion of LPS of E. coli serotype 0111:B4 (0 to 0.3 μg/mg of food). Pigs were slaughtered in stages on days 1, 5, 7 and 10 postweaning and complete extraction of small intestine was made. Gene expression was evaluated by qPCR. Blocks at random in a factorial arrangement 2 x 4 were used as statistical design. Results: the basal diet (without addition of LPS) presented increase in mRNA expression (p<0.01) of all the cytokines in jejunum for each post-weaning day, which suggests an inflammatory response and extensive tissue damage in pigs after early weaning. In diet 1 (with consumption of 0.3 μg LPS / mg diet) cytokines TNF-α, IL-8 and IL- 18 showed a significant increase in their levels of expression (p<0.01). All cytokines presented significant increase (p<0.01) in jejunum for each post-weaning day. Conclusion: The increase observed in the expression of TNF-α can be involved in the development of post-weaning diarrhea.
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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 | 0.000 | 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".