Formulation and In-Vitro Characterization of Chitosan-Nanoparticles Loaded with the Iron Chelator Deferoxamine Mesylate (DFO)
Bibliographic record
Abstract
The objective of this study was to develop chitosan (CS) nanoparticles (NPs) loaded with deferoxamine mesylate (DFO) for slow release of this iron-chelating drug. Drug nanoencapsulation was performed via ionic gelation of chitosan using sodium tripolyphosphate (TPP) as cross-linker. Nanoparticles with a size ranging between 150 and 400 nm were prepared for neat CS/TPP with a 2/1 molar ratio while their yield was directly dependent on the applied stirring rate during the preparation process. DFO at different content (20, 45 and 75 wt %) was encapsulated into these nanoparticles. We found that drug loading correlates with increasing DFO content while the entrapment efficiency has an opposite behavior due to the high solubility of DFO. Hydrogen-bonding between amino and hydroxyl groups of DFO with reactive groups of CS were detected using FT-IR spectroscopy while X-ray diffraction revealed that DFO was entrapped in amorphous form in the CS nanoparticles. DFO release is directly dependent on the content of loaded drug, while model analysis revealed that the release mechanism of DFO for the CS/TPP nanoparticles is by diffusion. Treatment of murine RAW 264.7 macrophages with nanoencapsulated DFO promoted an increased expression of transferrin receptor 1 (TfR1) mRNA, a typical homeostatic response to iron deficiency. These data provide preliminary evidence for release of pharmacologically active DFO from the chitosan nanoparticles.
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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".