SN-38 loaded polymeric micelles to enhance cancer therapy
Bibliographic record
Abstract
7-Ethyl-10-hydroxycamptothecin (SN-38) loaded poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol) (Pluronic F-108) and poly(ethylene glycol)-block-poly(ε-caprolactone) (PEG-b-PCL) nanoparticles were successfully prepared by a modified film hydration method and characterized by scanning electric microscopy (SEM), transmission electron microscopy (TEM), atomic force microscopy (AFM) and dynamic light scattering (DLS). Satisfactory drug loading of 20.73 ± 0.66% and a high encapsulation efficiency of 83.83 ± 1.32% were achieved. The SN-38 nanoparticles (SN-38 NPs) can completely disperse into a phosphate buffered saline (PBS) medium to produce a clear aqueous suspension that remains stable for up to three days. Total drug releases were 67.91% and 91.09% after 24 h in a PBS or fetal bovine serum (FBS) medium. Half maximal inhibitory concentration (IC(50)) tests of SN-38 and SN-38 NPs on A549 lung cells produced results of 200.0 ± 14.9 ng ml(-1) and 80.0 ± 4.6 ng ml(-1), respectively. Similarly, IC(50) tests of SN-38 and SN-38 NPs on MCF-7 breast cells yielded results of 16.0 ± 0.7 ng ml(-1) and 8.0 ± 0.5 ng ml(-1), respectively. These in vitro IC(50) studies show significant (p < 0.01) enhancement of the SN-38 NP drug efficiency in killing cancer cells in comparison to the free drug SN-38 control. All the materials used for this nanoformulation are approved by the US FDA, with the virtue of extremely low toxicity 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".