Cooked navy and black bean diets improve biomarkers of colon health and reduce inflammation during colitis
Why this work is in the frame
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Bibliographic record
Abstract
Common beans contain non-digestible fermentable components (SCFA precursors) and phenolic compounds (phenolic acids, flavonoids and anthocyanins) with demonstrated antioxidant and anti-inflammatory potential. The objective of the present study was to assess the in vivo effect of cooked whole-bean flours, with differing phenolic compound levels and profiles, in a mouse model of acute colitis. C57BL/6 mice were fed a 20 % navy bean or black bean flour-containing diet or an isoenergetic basal diet (BD) for 2 weeks before the induction of experimental colitis via 7 d dextran sodium sulphate (DSS, 2 % (w/v) in the drinking-water) exposure. Compared with the BD, both bean diets increased caecal SCFA and faecal phenolic compound concentrations (P< 0·05), which coincided with both beneficial and adverse effects on colonic and systemic inflammation. On the one hand, bean diets reduced mRNA expression of colonic inflammatory cytokines (IL-6, IL-9, IFN-γ and IL-17A) and increased anti-inflammatory IL-10 (P< 0·05), while systemically reduced circulating cytokines (IL-1β, TNFα, IFNγ, and IL-17A, P< 0·05) and DSS-induced oxidative stress. On the other hand, bean diets enhanced DSS-induced colonic damage as indicated by an increased histological injury score and apoptosis (cleaved caspase-3 and FasL mRNA expression) (P< 0·05). In conclusion, bean-containing diets exerted both beneficial and adverse effects during experimental colitis by reducing inflammatory biomarkers both locally and systemically while aggravating colonic mucosal damage. Further research is required to understand the mechanisms through which beans exert their effects on colonic inflammation and the impact on colitis severity in human subjects.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it