Phytogenic Compounds as Alternatives to In-Feed Antibiotics: Potentials and Challenges in Application
Why this work is in the frame
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Bibliographic record
Abstract
This article summarizes current experimental knowledge on the efficacy, possible mechanisms and feasibility in the application of phytogenic products as feed additives for food-producing animals. Phytogenic compounds comprise a wide range of plant-derived natural bioactive compounds and essential oils are a major group. Numerous studies have demonstrated that phytogenic compounds have a variety of functions, including antimicrobial/antiviral, antioxidative and anti-inflammation effects and improvement in the palatability of feed and gut development/health. However, the mechanisms underlying their functions are still largely unclear. In the past, there has been a lack of consistency in the results from both laboratory and field studies, largely due to the varied composition of products, dosages, purities and growing conditions of animals used. The minimal inhibitory concentration (MIC) of phytogenic compounds required for controlling enteric pathogens may not guarantee the best feed intake, balanced immunity of animals and cost-effectiveness in animal production. The lipophilic nature of photogenic compounds also presents a challenge in effective delivery to the animal gut and this can partially be resolved by microencapsulation and combination with other compounds (synergistic effect). Interestingly, the effects of photogenic compounds on anti-inflammation, gut chemosensing and possible disruption of bacterial quorum sensing could explain a certain number of studies with different animal species for the better production performance of animals that have received phytogenic feed additives. It is obvious that phytogenic compounds have good potential as an alternative to antibiotics in feed for food animal production and the combination of different phytogenic compounds appears to be an approach to improve the efficacy and safety of phytogenic compounds in the application. It is our expectation that the recent development of high-throughput and "omics" technologies can significantly advance the studies on the mechanisms underlying phytogenic compounds' functions and, therefore, guide the effective use of the compounds.
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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) | 0.001 | 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 | 0.000 | 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 it