Chickpea supplementation prior to colitis onset reduces inflammation in dextran sodium sulfate-treated C57Bl/6 male mice
Bibliographic record
Abstract
The potential for a chickpea-supplemented diet (rich in fermentable nondigestible carbohydrates and phenolic compounds) to modify the colonic microenvironment and attenuate the severity of acute colonic inflammation was investigated. C57Bl/6 male mice were fed a control basal diet or basal diet supplemented with 20% cooked chickpea flour for 3 weeks prior to acute colitis onset induced by 7-day exposure to dextran sodium sulfate (DSS; 2% w/v in drinking water) and colon and serum levels of inflammatory mediators were assessed. Despite an equal degree of DSS-induced epithelial barrier histological damage and clinical symptoms between dietary groups, biomarkers of the ensuing inflammatory response were attenuated by chickpea pre-feeding, including reduced colon tissue activation of nuclear factor kappa B and inflammatory cytokine production (tumor necrosis factor alpha and interleukin (IL)-18). Additionally, colon protein expression of anti-inflammatory (IL-10) and epithelial repair (IL-22 and IL-27) cytokines were increased by chickpea pre-feeding. Furthermore, during acute colitis, chickpea pre-feeding increased markers of enhanced colonic function, including Relmβ and IgA gene expression. Collectively, chickpea pre-feeding modulated the baseline function of the colonic microenvironment, whereby upon induction of acute colitis, the severity of the inflammatory response was attenuated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".