Herbal Bioenhancers in Veterinary Phytomedicine
Bibliographic record
Abstract
Herbal bioenhancers are active phytomolecules that increase the bioavailability, bioefficacy and biological activity of various drugs when coadministered at low concentrations. These valuable compounds reduce the dose, increase the treatment rate, decrease the treatment duration, drug resistance or related adverse reactions which have economical implications in livestock and pet medicine. Eventhough the concept of herbal bioenhancers are known for years through Ayurvedic medicine, the underlying mechanisms remains unclear. The main mechanisms involved are related to drug absorption (effect on solubility, drug efflux and transport proteins, increased permeability in gastrointestinal system) and drug metabolism (inhibition/induction of drug metabolysing enzymes, thermogenic effect). Due to species specific differences in these mechanisms, corresponding data on human and laboratory animal could not be attributed. As multidrug resistance is a major treat to both human and animal health, within "One Health" concept, efficient therapeutical strategies are encouraged by authorities, where focus on herbal supplements as a vast unexploited field remains to be researched within "Bioenhancement Concept." This review brings insight to mechanims involved in bioenhancing effect, examples of herbal extracts and phytoactive compounds and their potential in the veterinary medicine including different classes of drugs such as antibiotics, anticancerous, antiviral, and antituberculosis.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".