Phytochemical-induced mucin accumulation in the gastrointestinal lumen is independent of the microbiota
Bibliographic record
Abstract
Abstract The mucus layer is critical to gastrointestinal health and ecology. Dietary phytochemicals are well documented to stimulate mucus production and secretion, but the underlying mechanism and effects on gut health are poorly understood. We fed germ-free and conventional mice diets containing approximately 0.4% of polyphenols per gram to determine if the phytochemical-induced accumulation of mucin in the gastrointestinal lumen is dependent on the microbiota. In addition, we assess how increased mucin shapes microbial communities in conventional mice. Germ-free mice receiving a pea ( Pisum sativuum ) seed coat proanthocyanidin-containing diet (PA) had greater levels of fecal mucin compared to the non-proanthocyanidin-containing (NPA) pea seed coat diet control ( P < 0.05), confirming that fecal mucin accumulation is independent of the gut microbiota. Conventional mice fed the PA diet and a red osier dogwood (ROD; Cornus sericea ) extract diet (DW) had higher mucin levels compared to a control diet without phytochemicals ( P < 0.01 and P < 0.05, respectively). The increase in luminal mucin was associated with consistent increases in bacterial taxa belonging to Lachnospiraceae and [ Clostridium ] leptum species and a decrease in Romboutsia species. We conclude that phytochemicals have the ability to alter gut microbial ecology by increasing the amount of mucin in the gastrointestinal lumen.
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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".