Optimized preparation of eugenol microcapsules and its effect on hepatic steatosis in HepG<sub>2</sub> cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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