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 in vitro. 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 in vivo 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".