Effects of stealth liposomal daunorubicin plus tamoxifen on the breast cancer and cancer stem cells
Bibliographic record
Abstract
PURPOSE: The cancer stem cells play an important role in the invasion, metastasis and relapse of cancers as they are resistant to regular chemotherapy. In the present study, stealth liposomal daunorubicin plus tamoxifen was developed for eradicating breast cancer cells together with cancer stem cells. METHODS: Inhibitory effects were performed on the bulk human breast cancer cells (MCF-7), the sorted MCF-7 cancer stem-like cells (side population, SP), and the sorted MCF-7 cancer cells (NSP), respectively. Antitumor activity and TUNEL analysis were evaluated on the MCF-7 xenografts in nude mice. RESULTS: The encapsulation efficiencies of daunorubicin and tamoxifen were 95% and 90%, respectively. The mean particle size of the stealth liposomes was about 100 nm. Breast cancer stem cells were identified by the specific markers CD44+/CD24-, and isolated from bulk MCF-7 cells. When applying stealth liposomal daunorubicin plus tamoxifen, the inhibitory effects on both the breast cancer cells and the cancer stem cells were significantly increased in vitro, respectively. In the MCF-7 xenografts in mice, stealth liposomal daunorubicin plus tamoxifen showed the most favorable antitumor activity due to the passive targeting the tumor tissue and the synergistic effects in eliminating breast cancer cells and cancer stem cells. CONCLUSION: Stealth liposomal daunorubicin plus tamoxifen could have the potentials in eliminating both breast cancer cells and cancer stem 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) | 0.000 | 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 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".