Ultrasmall Gold−Doxorubicin Conjugates Rapidly Kill Apoptosis-Resistant Cancer Cells
Bibliographic record
Abstract
Ultrasmall (mean diameter, 2.7 nm) gold nanoparticles conjugated to doxorubicin (Au-Dox) are up to 20-fold more cytotoxic to B16 melanoma cells than the equivalent concentration of doxorubicin alone, and act up to six times more quickly. Ultrasmall Au-Dox enters the cell endocytic vesicles and is also seen free in the cytoplasm and nuclei. This is in distinct contrast to larger particles reported in previous studies, which are excluded from the nucleus and which show no increased toxicity over Dox alone. Cell death with Au-Dox is confirmed to be apoptotic by TUNEL staining and ultrastructural examination using transmission electron microscopy. To further explore the mechanism of action, two other cell lines were examined: HeLa cells which are highly sensitive to Dox, and HeLa cells overexpressing Bcl-2 which show impaired apoptosis and Dox resistance. Interestingly, the Dox-sensitive cells show a slightly decreased sensitivity to Au-Dox relative to Dox alone, whereas the Dox-resistant cells are not resistant to Au-Dox. These results have implications for the design of chemotherapeutic nanoparticles, suggesting that it is possible to selectively target apoptosis-resistant cancer cells while at the same time reducing cytotoxicity to normal cells.
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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) | 0.001 | 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".