Essential oils as alternatives to antibiotics in swine production
Bibliographic record
Abstract
This review article summarizes the efficacy, feasibility and potential mechanisms of the application of essential oils as antibiotic alternatives in swine production. Although there are numerous studies demonstrating that essential oils have several properties, such as antimicrobial, antioxidative and anti-inflammatory effects, feed palatability enhancement and improvement in gut growth and health, there is still a need of further investigations to elucidate the mechanisms underlying their functions. In the past, the results has been inconsistent in both laboratory and field studies because of the varied product compositions, dosages, purities and growing stages and conditions of animals. The minimal inhibitory concentration (MIC) of essential oils needed for killing enteric pathogens may not ensure the optimal feed intake and the essential oils inclusion cost may be too high in swine production. With the lipophilic and volatile nature of essential oils, there is a challenge in effective delivery of essential oils within pig gut and this challenge can partially be resolved by microencapsulation and nanotechnology. The effects of essential oils on inflammation, oxidative stress, microbiome, gut chemosensing and bacterial quorum sensing (QS) have led to better production performance of animals fed essential oils in a number of studies. It has been demonstrated that essential oils have good potential as antibiotic alternatives in feeds for swine production. The combination of different essential oils and other compounds (synergistic effect) such as organic acids seems to be a promising approach to improve the efficacy and safety of essential oils in applications. High-throughput systems technologies have been developed recently, which will allow us to dissect the mechanisms underlying the functions of essential oils and facilitate the use of essential oils in swine production.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".