<i>In vitro</i>evaluation of the activity of microencapsulated carvacrol against<i>Escherichia coli</i>with K88 pili
Bibliographic record
Abstract
AIMS: The aim of the current study is to develop encapsulation of essential oils for oral delivery to the small intestine of pigs in order to retain their antimicrobial activity. METHODS AND RESULTS: Carvacrol was used as a model essential oil and successfully encapsulated in microcapsules made from Ca-alginate hydrogel using an emulsion-extrusion technology with high encapsulation efficiency. This encapsulation method did not compromise the antimicrobial activity when tested against Escherichia coli K88 in a culture medium, as well as in a simulated gastrointestinal model. In the simulated gastrointestinal model, <20% of encapsulated carvacrol was released in the simulated gastric fluid; the rest was nearly completely released in the intestinal fluid after 6 h of incubation. CONCLUSIONS: Encapsulation in Ca-alginate microcapsules could effectively reduce the early absorption of carvacrol in the upper gastrointestinal tract after oral administration, therefore, retains its potential antibacterial activity for the small intestine. SIGNIFICANCE AND IMPACT OF THE STUDY: The developed encapsulation method is expected to be suitable for encapsulation of other essential oils. The results from this study would increase the likelihood of success in the application of essential oils as antimicrobial agents for controlling enteric diseases in pigs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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 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".