Application of ɑ-Tocotrienol-Loaded Biocompatible Precirol in Attenuation of Doxorubicin Dose-Dependent Behavior in HUH-7 Hepatocarcinoma Cell Line
Bibliographic record
Abstract
Tumor-targeted nanoparticle delivery system has been known as a substitute and capable achievement in cancer treatment compared to conventional methods. In this study, we examined potential application of ɑ-tocotrienol-Precirol formulation to enhance efficiency of doxorubicin (DOX) in induction of apoptosis in HUH-7 hepatocarcinoma cells. ɑ-tocotrienol-loaded nanoparticles were characterized at the point of zeta potential, particle size, scanning electron microscope (SEM), and cell internalization. To evaluate antiproliferative effects of formulation, apoptosis, cell cycle procedure, flow cytometry, and MTT assays were employed. Optimum size of the ɑ-tocotrienol formulation revealed narrow size distribution with mean average of 78 ± 3 nm. IC50 values for ɑ-tocotrienol and ɑ-tocotrienol-nano structured lipid carriers after 24 h were 15 ± 0.6 and 10 ± 0.03 µM, respectively. After incubation of cells with ɑ-tocotrienol-loaded careers, the rate of cell proliferation decreased from 53 ± 6.1 to 34 ± 7.1% (P < 0.05). A significant improvement in the apoptosis percentage was revealed after treatment of the HUH-7 cell line with DOX and ɑ-tocotrienol careers (P < 0.05). Gene expression results demonstrated a marked decrease in survivin and increase in Bid and Bax levels. Our findings suggest that ɑ-tocotrienol-loaded nanoparticles elevate DOX efficacy in HUH-7 hepatocarcinoma cell.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".