Galactose-modified selenium nanoparticles for targeted delivery of doxorubicin to hepatocellular carcinoma
Bibliographic record
Abstract
Galactose-modified selenium nanoparticles (GA-SeNPs) loading with doxorubicin (DOX) for hepatocellular carcinoma (HCC) therapy was investigated in this paper. Selenium nanoparticles (SeNPs) were modified with galactose as tumor targeting moiety to fabricate tumor-targeted delivery carrier GA-SeNPs, then doxorubicin was loaded onto the surface of GA-SeNPs for improving antitumor efficacy of DOX in HCC therapy. Chemical structure characterization of GA-Se@DOX showed that DOX was successfully loaded to the surface of GA-SeNPs to prepare functionalized antitumor drug delivery system GA-Se@DOX. GA-Se@DOX exhibited effective cellular uptake in HepG2 cells and entered HepG2 cells mainly by clathrin-mediated endocytosis pathway. GA-Se@DOX showed significant activity to induce the apoptosis of HepG2 cells <i>in vitro</i>. The western blotting result indicated that GA-Se@DOX induced HepG2 cells apoptosis via activating caspase signaling and Bcl-2 family proteins. Moreover, active targeting delivery system GA-Se@DOX exhibited excellent antitumor efficacy <i>in vivo</i> in comparison with passive targeting delivery system Se@DOX. Histology analysis showed that GA-Se@DOX exhibited no obvious damage to major organs including heart, liver, spleen, lung, and kidney under the experimental condition. Taken together, GA-Se@DOX may be one novel promising nanoscale drug candidate for HCC therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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; both teacher heads agree on what is shown here.
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".