Impacts of resistant starch and wheat bran consumption on enteric inflammation in relation to colonic bacterial community structures and short-chain fatty acid concentrations in mice
Bibliographic record
Abstract
Identifying the connection among diet, the intestinal microbiome, and host health is currently an area of intensive research, but the potential of dietary fiber (DF) consumption to ameliorate intestinal inflammation has not been extensively studied. We examined the impacts of the DFs, wheat bran (WB) and resistant starch (RS) on host enteric health. A murine model of acute Th1/Th17 colitis (i.e. incited by Citrobacter rodentium ) was used. Diets enriched with RS increased weight gain in mice inoculated with C. rodentium compared to mice consuming a conventional control (CN) diet. Short-chain fatty acid (SCFA) quantities in the cecum and distal colon were higher in mice consuming DFs, and these mice exhibited higher butyrate concentrations in the distal colon during inflammation. Histopathologic scores of inflammation in the proximal colon on day 14 post-inoculation (p.i.) (peak infection) and 21 p.i. (late infection) were lower in mice consuming DF-enriched diets compared to the CN diet. Consumption of WB reduced the expression of Th1/Th17 cytokines. As well, the expression of bacterial recognition and response genes such as Relmβ , RegIIIγ , and Tlr4 increased in mice consuming the RS-enriched diets. Furthermore, each diet generated a region-specific bacterial community, suggesting a link between selection for specific bacterial communities, SCFA concentrations, and inflammation in the murine colon. Collectively, data indicated that the consumption of DF-rich diets ameliorates the effects of C. rodentium -induced enteritis by modifying the host microbiota to increase SCFA production, and bacterial recognition and response mechanisms to promote host health.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".