Encapsulation of Polygonum bistorta root phenolic compounds as a novel phytobiotic and its protective effects in the mouse model of enteropathogenic Escherichia coli infection
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Microencapsulation technology is the fundamental delivery system for encapsulating the natural bioactive compounds especially phenolic in order to developing bioavailability, stability and controlling release. This study was conducted to determine the antibacterial and health-promoting potential of the phenolic rich extract (PRE)-loaded microcapsules obtained from Polygonum bistorta root as a dietary phytobiotic in mice challenged by enteropathogenic Escherichia coli (E. coli). METHOD: The PRE was obtained from Polygonum bistorta root using fractionation by different polarity solvents and the highest PRE was encapsulated by the combination of modified starch, maltodextrin, and whey protein concentrate as wall materials using a spray dryer. Then, the physicochemical characterization (particle size, zeta potential, Morphology and polydispersity index) of microcapsules have been assessed. For the invivo study, 30 mice at five treatment were designed and antibacterial properties were analyzed. Furthermore, relative fold changes in the ileum population of E. coli was investigated using Real time PCR. RESULTS: The encapsulation of PRE resulted in the production of phenolic enriched extract-loaded microcapsules (PRE-LM) with a mean diameter of 330 nm and relatively high entrapment efficiency (87.2% w/v). The dietary supplementation of PRE-LM improved weight gain, liver enzymes, gene expression, morphometric characteristics of the ileum and decreased the population of E. coli present in the ileum significantly (p < 0.05). CONCLUSION: Our funding suggested PRE-LM as a promising phytobiotic against E. coli infection in mice.
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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) | 0.000 | 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) | 0.000 | 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