Effects of Pea (Pisum sativum) Prebiotics on Intestinal Iron-Related Proteins and Microbial Populations In Vivo (Gallus gallus)
Bibliographic record
Abstract
Iron deficiency remains a public health challenge globally. Prebiotics have the potential to improve iron bioavailability by modulating intestinal bacterial population, increasing SCFA production, and stimulating expression of brush border membrane (BBM) iron transport proteins among iron-deficient populations. This study intended to investigate the potential effects of soluble extracts from the cotyledon and seed coat of three pea (Pisum sativum) varieties (CDC Striker, CDC Dakota, and CDC Meadow) on the expression of BBM iron-related proteins (DCYTB and DMT1) and populations of beneficial intestinal bacteria in vivo using the Gallus gallus model by oral gavage (one day old chicks) with 1 mL of 50 mg/mL pea soluble extract solutions. The seed coat treatment groups increased the relative abundance of Bifidobacterium compared to the cotyledon treatment groups, with CDC Dakota seed coat (dark brown pigmented) recording the highest relative abundance of Bifidobacterium. In contrast, CDC Striker Cotyledon (dark-green-pigmented) significantly increased the relative abundance of Lactobacillus (p < 0.05). Subsequently, the two dark-pigmented treatment groups (CDC Striker Cotyledon and CDC Dakota seed coats) recorded the highest expression of DCYTB. Our study suggests that soluble extracts from the pea seed coat and dark-pigmented pea cotyledon may improve iron bioavailability by affecting intestinal bacterial populations.
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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 | 0.000 |
| 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".