Controlled release of 5-fluorouracil and progesterone from magnetic nanoaggregates
Bibliographic record
Abstract
BACKGROUND: The potential use of magnetic nanoparticles in biomedical applications has witnessed an exponential growth in recent years. METHODS: In this study, we used nanoaggregates of magnetic nanoparticles as carriers for controlled drug delivery. The nanoaggregates are formed due to the presence of the block copolymer of polyethylene oxide-polypropylene oxide (Pluronic F-68) and beta-cyclodextrin that surround the magnetic core of the nanoparticles. The administration of the drug carriers occurs by inhalation, and the drug is delivered systemically via the pulmonary route. We tested the delivery of 5-fluorouracil and progesterone, which are used as models of hydrophilic and hydrophobic drugs, respectively. RESULTS: The estimated nanoaggregates' diameters are between 293 nm ± 14.65 nm and 90.2 nm ± 4.51 nm, respectively. In-situ and post-synthesis techniques are two approaches for drug loading. The polymer composition of nanoaggregates and initial drug concentration showed a significant effect on both the drug entrapment efficiency and release kinetics. Average drug entrapment efficiencies ranged between 16.11% and 83.25%. In-situ loaded samples showed significantly slower release rates. The drug release mechanism is investigated by mathematical curve fitting to different drug release kinetics models. In most cases, the Peppas model has shown good correlations (coefficients of correlation, R(2), between 0.85 and 0.99) with the examined release profiles. The estimated release indices are below 0.5, which indicates the Fickian diffusion mechanism. For samples with an initial burst effect, the modified Peppas model can provide a better understanding of the drug release mechanism, both in the samples loaded with progesterone, or those high polymer concentrations. CONCLUSION: Our work showed prolonged delivery of drugs (5-fluorouracil and progesterone) by diffusion from nanoaggregates, with the potential to reduce dose-related adverse effects.
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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 | 0.001 | 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 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".