Application of metabolomics to assess the intestinal response to dietary supplementation
Bibliographic record
Abstract
Abstract Recently, there has been increasing interest in the use of feed supplements for improving human and animal nutrition and health. Identification of nutrient biomarkers is a top priority to measure the biological and physiological effects of dietary components. Metabolomics is an objective and accurate tool to expand our knowledge of biological systems response to feed supplements by defining intestinal pathways and mechanisms. This review focuses on the impact of feed supplements on the host intestinal system and blood constituents, illustrating systemic changes in metabolic pathways and functionality. From scientific reports dealing with metabolomic data, the paper compiles evidence on feed additive effects on small intestine morphology, nutrient absorption, enzyme regulation and intestinal epithelium, as well as colon microbiota community. The review concentrates on the cellular and molecular functions to demonstrate the possible biological effects of feed supplements on health. The combinations of quantitative metabolomic assays are finding applications not only in animal and human nutritional sciences but also in agricultural, medical, and medicinal research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".