In Vitro Study: Synthesis and Evaluation of Fe<sub>3</sub>O<sub>4</sub>/CQD Magnetic/Fluorescent Nanocomposites for Targeted Drug Delivery, MRI, and Cancer Cell Labeling Applications
Why this work is in the frame
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Bibliographic record
Abstract
In the present study, first, Fe3O4 nanoparticles were functionalized using glutaric acid and then composited with CQDs. Doxorubicin (DOX) drug was loaded to evaluate the performance of the nanocomposite for targeted drug delivery applications. The XRD pattern confirmed the presence of characteristic peaks of CQDs and Fe3O4. In the FTIR spectrum, the presence of carboxyl functional groups on Fe3O4/CQDs was observed; DOX (positive charge) is loaded onto Fe3O4/CQDs (negative charge) by electrostatic absorption. FESEM and AFM images showed that the particle sizes of Fe3O4 and CQDs were 23–75 and 1–3 nm, respectively. The hysteresis curves showed superparamagnetic properties for Fe3O4 and Fe3O4/CQDs (57.3 and 8.4 emu/g). The Fe3O4 hysteresis curve showed superparamagnetic properties (Ms and Mr: 57.3 emu/g and 1.46 emu/g. The loading efficiency and capacity for Fe3O4/CQDs were 93.90% and 37.2 mg DOX/g MNP, respectively. DOX release from Fe3O4/CQDs in PBS showed pH-dependent release behavior where after 70 h at pH 5 and 7.4, about 50 and 21% of DOX were released. Fluorescence images of Fe3O4/CQD-treated cells showed that Fe3O4/CQDs are capable of labeling MCF-7 and HFF cells. Also, T2-weighted MRI scans of Fe3O4/CQDs in water exhibited high r2 relaxivity (86.56 mM–1 S–1). MTT assay showed that DOX-loaded Fe3O4/CQDs are highly biocompatible in contact with HFF cells (viability = 95%), but they kill MCF-7 cancer cells (viability = 45%). Therefore, the synthesized nanocomposite can be used in MRI, targeted drug delivery, and cell labeling.
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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) | 0.000 | 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 it