Intestinal nutrition: role of vitamins and biofactors and gaps of knowledge
Bibliographic record
Abstract
The role of the microbiota in the health of the host is complex and multifactorial. The microbiota both consumes nutrients in competition with the host, but also creates nutrients that can be used by other microbes, but also the host. However, the quantitative impact of the microbiota on nutrient supply and demand is not well understood in poultry. The gastrointestinal tract is one of the largest points of contact with the external environment, and the intestinal microbiome is the largest and most complex of any system. Although the intestinal microbiota has first access to consumed nutrients, including vitamins, and is potentially a major contributor to production of various vitamins, the quantification of these impacts remains very poorly understood in poultry. Based on the human literature, it is clear that vitamin deficiencies can have systemic effects on the regulation of many physiological systems, beyond the immediate, direct nutrient functions of the vitamins. The impact of excessive supplementation of vitamins on the microbiota is not well understood in any species. In the context of poultry nutrition, in which substantial dietary excesses of most vitamins are provided, this represents a knowledge gap. Given the paucity of studies investigating the vitamin requirements of modern, high-producing poultry, the limited understanding of vitamin nutrition (supply and utilization) by the microbiome, and the potential impacts on the microbiome of the move away from dietary growth-promoting antibiotic use, more research in this area is required. The microbiota also contributes a vast array of other metabolites involved in intramicrobiota communication, symbiosis and competition that can also have an impact on the host. Myo-inositol and butyrate are briefly discussed as examples of biofactors produced by the microbiota as mediators of intestinal health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".