Vitamin D supplementation results in higher numbers of Clostridium coccoides in the feces of female but not male mice with intestinal inflammation
Bibliographic record
Abstract
Vitamin D may have immunomodulatory effects in the intestine and potential to target the gut microbiota. Our objective was to determine if exposure to supplemental levels of vitamin D mitigates intestinal inflammation in interkeukin‐10 knockout (IL‐10 KO) mice. Mice were randomized to a diet containing 25 IU (low levels) or 5000 IU (supplemental levels) of vitamin D/kg of diet in utero until necropsy at 3 months of age when colonic and fecal samples were collected. Colon inflammation severity and IL‐8 levels (males only) were assessed by histological analysis and ELISA, respectively, and fecal microbiota composition was determined by qPCR. Vitamin D had no effect on IL‐8 levels in males, or body weight and inflammation severity in either gender. Vitamin D had no effect on microbiota composition in males, but females in the supplemental group had higher (p<0.05) counts of Clostridium coccoides than females in the low group. All other bacteria measured were unaffected. Moreover, female mice had lower (p<0.05) colonic inflammation scores and more (p<0.05) C. coccoides than males. Clostridia are major butyrate producers and promote Treg cell activity in the colon. Therefore, vitamin D may favourably modulate microbiota composition without attenuating inflammation in female IL‐10 KO mice. Funding sources: Dairy Farmers of Canada, NSERC and Ontario Graduate Scholarship
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".