Sequential release of drugs from hollow manganese ferrite nanocarriers for breast cancer therapy
Bibliographic record
Abstract
Single drug therapies for cancer are often suboptimal and may not provide long term clinical benefits. To overcome this obstacle for effective treatment the applications of two or more drugs are preferable. A limitation of multidrug use is the varying pharmacokinetics of different drugs. To overcome these impediments, we designed and synthesized multi-layered polyvinyl alcohol tethered hollow manganese ferrite nanocarriers capable of encapsulating two drugs with unique attributes of sensitivity towards tumor acidic milieu, mono-dispersive, compactness and high encapsulation efficiency. We encapsulated tamoxifen and diosgenin in the peripheral and subsequent inner layers of multilayered nanocarriers. In vitro and in vivo studies evaluated the nanocarrier uptake and retention ability of the tumor through magnetic saturation studies and elucidated the molecular mechanisms mediating drug(s)-induced apoptosis. The acidity of the tumor environment triggers extracellular dissociation of the peripheral coats resulting in release of tamoxifen blocking the estrogen receptor. The partially degraded nanocarriers localize intracellularly through endosomal escape and release diosgenin. Nanocarrier treatment reduced the cellular levels of Bcl2 and p53, while increasing the levels of Bim. This delivery system successfully embodies the sequential release of drugs and may provide a therapeutic strategy for sequentially affecting multiple targets in advanced cancers.
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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".