Nutraceuticals for Functional Feeding and for Drug Discovery in Ruminant Production: A Commentary Paper
Bibliographic record
Abstract
The paper lists nutraceuticals from the literature and the world-wide web according to their definition and benefits to nutrition and health. The following nutraceuticals are both novel or have received attention in the literature. They are anti-oxidants, water-soluble carbohydrates (WSC), soluble dietary fibre (SDF), biogenic peptides, functional amino acids (FAA), probiotics, vitamins as pharma, minerals, phytochemicals, “greens” such as herbs, weeds and green forages, omega-3 fatty acids, dietary “bulk” fibre, phytonutraceuticals and adaptogens or anti-stress compounds. The paper then discusses selected topics encountered in the literature or that is still being proposed for the lab bench. They are the: sugars of WSC and SDF, peptides from feed proteins as prebiotics for the rumen stomach and hindgut, prebiotic “bulk” fibre, VitD2,3 analogues as agonists and milk food proteins (MFP). Drug development is suggested of the type by mining the transcriptome and its transcription factors (TF) and the use of peptide nucleic acid (PNA) - based fine-biochemical agents that can be directly applied for therapeutic purposes versus another approach using the low molecular weight (LMW) – proteome in plasma, tissues and secretalogues to find biopharma. An actual e. g. described here not necessarily derived from nutraceuticals but illustrating the use for PNA-carriered TF is to enhance vaccines against Streptococcus pneumoniae, that is, using MR1 protein molecules that activate mucosa-associated invariant T (MAIT) cells in a humeral response against the bacteria that causes pneumonia to be direct applied (DA) by nasal spray applicator. With new perspectives from nutraceuticals, it should be possible to derive new pharma through research drug pipelines to cure, manage incidence or prevent diseases.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".