Guar gum and resistant starch consumption increases interleukin‐10 (IL‐10) abundance in the colon of pigs fed a high‐fat diet
Bibliographic record
Abstract
This study was conducted to examine colonic abundances of anti‐inflammatory cytokine interleukin 10 (IL‐10) and pro‐inflammatory cytokines tumor necrosis factor‐alpha (TNF‐α) and interleukin‐6 (IL‐6) in pigs fed a high‐fat basal diet supplemented with 15% guar gum and resistant starch. A total of 24 Yorkshire grower barrows were assigned into a high‐fat basal diet as the control and two basal diets supplemented with 15% guar gum and retrograded high amylose cornstarch, i.e., resistant starch, according to a randomized block design for 4 weeks. Compared with the control group, guar gum and resistant starch consumption at 15% increased ( P <0.05) colonic IL‐10 abundance. However, there was no difference ( P >0.05) in colonic IL‐10 abundance between the 15%‐guar gum and the 15%‐resistant starch groups. Furthermore, the consumption of guar gum and resistant starch did not affect ( P >0.05) colonic abundances of TNF‐α and IL‐6. We conclude that consumption of guar gum and resistant starch supplemented in a high‐fat basal diet may protect the colon from developing inflammation by enhancing IL‐10 abundance. Supported by OMAFRA of Canada.
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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) | 0.002 | 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".