Using chitosan-coated magnetite nanoparticles as a drug carrier for opioid delivery against breast cancer
Bibliographic record
Abstract
superparamagnetic nanoparticles (SPIONs) decorated with chitosan were loaded with papaverine or noscapine to surmount drug delivery-related obstacles. Modifying the magnetic nanoparticles (MNP) surface with polymeric materials such as chitosan prevents oxidation and provides a site for drug linkage, which renders them a great drug carrier. The obtained systems were characterized by DLS (20-40 nm were achieved for MNPs and drug- loaded MNPs), TEM (spherical with average size of 11-20 nm) FTIR, XRD, and VSM (71.3 - 42.8 emu/g). Contrary to noscapine, papaverine-MNPs attenuated 4T1 murine breast cancer cell proliferation (11.50 ± 1.74 µg/mL) effectively compared to the free drug (62.35 ± 2.88 µg/mL) while sparing L-929 fibroblast cells (138.14 ± 4.38 µg/mL). Furthermore, SPION and SPION-chitosan displayed no cytotoxic activity. Colony-formation assay confirmed the long-term cytotoxicity of nanostructures. Both developed formulations promoted ROS production accompanied by late apoptotic cell death. The biocompatible nanoparticle exerted an augmenting effect to deliver papaverine to metastatic breast cancer 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) | 0.000 | 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".