Vitamin D Supplementation Impacts Calcium and Phosphorus Metabolism in Piglets Fed a Diet Contaminated with Deoxynivalenol and Challenged with Lipopolysaccharides
Bibliographic record
Abstract
Using alternative feed ingredients in pig diets can lead to deoxynivalenol (DON) contamination. DON has been shown to induce anorexia, inflammation, and—more recently—alterations in the vitamin D, calcium, and phosphorus metabolisms. Adding vitamin D supplementation in the form of vitamin D3 and 25-OH-D3 to the feed could modify the effects of DON in piglets. In this study, vitamin D3 or 25-OH-D3 supplementation was used in a control or DON-contaminated treatment. A repetitive exposure over 21 days to DON in the piglets led to disruptions in the vitamin D, calcium, and phosphorus metabolisms, resulting in a decreased growth performance, increased bone mineralization, and the downregulation of genes related to calcium and to phosphorus intestinal and renal absorption. The DON challenge also decreased blood concentrations of 25-OH-D3, 1,25-(OH)2-D3, and phosphate. The DON contamination likely decreased the piglets’ vitamin D status indirectly by modifying the calcium metabolism response. Vitamin D supplementations did not restore vitamin D status or bone mineralization. After a lipopolysaccharide-induced inflammatory stimulation, feeding a 25-OH-D3 supplementation increased 25-OH-D3 concentration and 1,25-(OH)2-D3 regulations during the DON challenge. DON contamination likely induced a Ca afflux by altering the intestinal barrier, which resulted in hypercalcemia and hypovitaminosis D. The vitamin D supplementation could increase the calcitriol production to face the combined LPS and DON challenge.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".