Optimized preparation of eugenol microcapsules and its effect on hepatic steatosis in HepG<sub>2</sub> cells
Bibliographic record
Abstract
This study was aimed at evaluating the potential of peach gum (PG) and gelatin in the microencapsulation of eugenol and the intervention of eugenol microcapsules on hepatic steatosis in vitro. Response surface method (RSM) was used to optimize the encapsulation conditions of eugenol microcapsules. The microcapsules were characterized by scanning electron microscopy (SEM), dynamic Light Scattering (DLS), Fourier transform infrared spectroscopy (FT-IR) and release behavior in vitro was determined. The effect of eugenol microcapsules on free fatty acids (FFA) treated hepatocellular cells (HepG2) cells was evaluated by oil red O staining and intracellular total cholesterol (TC) and triglyceride (TG) determination. The results showed that the optimal encapsulation conditions were as follows: the PG-gelatin ratio was 1.6:1.4, the core-wall ratio was 1.6:1.4, the pH was 4 and the emulsification speed was 9000 r/min. The optimized microcapsules were smooth spherical with a size of about 3.09 ± 0.58 μm and the encapsulation was confirmed by FT-IR. In vitro release behavior showed that eugenol microcapsules could be released stably in a neutral environment for 72 h. Oil red O staining showed that 50 and 100 μM eugenol microcapsules could significantly inhibit the lipid accumulation and reduce the TC and TG in steatotic HepG2 cells induced by FFA. Therefore, PG and gelatin can be used as excellent carriers for the microencapsulation of volatile compounds in the field of biomedical industry, and eugenol microcapsules is a promising preparation for the treatment of nonalcoholic fatty liver disease (NAFLD).
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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) | 0.000 | 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 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".