Macronutrient intake and gastrointestinal inflammation affect iron and zinc status in growing pigs.
Bibliographic record
Abstract
Deficiencies of copper, iron and zinc in chronic inflammatory bowel disease are common yet little is know as to how these trace elements are affected during acute persistent intestinal inflammation. We examined how copper, iron and zinc status were affected by GI inflammation, in different nutritional states, using our piglet colitis model. Piglets (n=24), receiving 1g/(kg·d) dextran sulfate, were randomized to receive a 50% macronutrient restricted diet, a 50% macronutrient restricted diet with probiotics or a diet providing 100% NRC requirements for growing piglets receiving phytate‐free liquid diet. An additional 8 piglets were randomized into a well‐nourished group without colitis. Copper, iron and zinc concentrations were determined in plasma collected on days 3 and 14 and 5‐day fecal and urine collections from days 9 to 14. Copper status, both plasma concentrations and 5‐day mass balance, did not appear to be affected by colitis in either the well‐nourished or malnourished groups. Plasma iron declined to a greater extent in all piglets with colitis compared to those without, regardless of nutritional status. Zinc losses were higher in malnourished piglets versus well‐nourished piglets. In light of our findings we suggest the use of dual isotope techniques to determine the role endogenous zinc loss and absorption rates affect status during acute inflammation.
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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 | 0.001 | 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".