Formulation and Characterization of Poly (Ethylene Glycol)-Coated Core-Shell Methionine Magnetic Nanoparticles as a Carrier for Naproxen Delivery: Growth Inhibition of Cancer Cells
Bibliographic record
Abstract
An efficient and selective drug delivery vehicle for cancer cells can remarkably improve therapeutic approaches. In this study, we focused on the synthesis and characterization of magnetic Ni1−xCoxFe2O4 nanoparticles (NPs) coated with two layers of methionine and polyethylene glycol to increase the loading capacity and lower toxicity to serve as an efficient drug carrier. Ni1−xCoxFe2O4@Methionine@PEG NPs were synthesized by a reflux method then characterized by FTIR, XRD, FESEM, TEM, and VSM. Naproxen was used as a model drug and its loading and release in the vehicles were evaluated. The results for loading efficiency showed 1 mg of Ni1−xCoxFe2O4@Methionine@PEG NPs could load 0.51 mg of the naproxen. Interestingly, Ni1−xCoxFe2O4@Methionine@PEG showed a gradual release of the drug, achieving a time-release up to 5 days, and demonstrated that a pH 5 release of the drug was about 20% higher than Ni1−xCoxFe2O4@Methionine NPs, which could enhance the intracellular drug release following endocytosis. At pH 7.4, the release of the drug was slower than Ni1−xCoxFe2O4@Methionine NPs; demonstrating the potential to minimize the adverse effects of anticancer drugs on normal tissues. Moreover, naproxen loaded onto the Ni1−xCoxFe2O4@Methionine@PEG NPs for breast cancer cell lines MDA-MB-231 and MCF-7 showed more significant cell death than the free drug, which was measured by an MTT assay. When comparing both cancer cells, we demonstrated that naproxen loaded onto the Ni1−xCoxFe2O4@Methionine@PEG NPs exhibited greater cell death effects on the MCF-7 cells compared with the MDA-MB-231 cells. The results of the hemolysis test also showed good hemocompatibility. The results indicated that the prepared magnetic nanocarrier could be suitable for controlled anticancer drug delivery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".