Microwave-Responsive Metal-Organic Frameworks (MOFs) for Enhanced In Vitro Controlled Release of Doxorubicin
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) are excellent candidates for a range of applications because of their numerous advantages, such as high surface area, porosity, and thermal and chemical stability. In this study, microwave (MW) irradiation is used as a novel stimulus in vitro controlled release of Doxorubicin (DOX) from two MOFs, namely Fe-BTC and MIL-53(Al), to enhance drug delivery in cancer therapy. DOX was encapsulated into Fe-BTC and MIL-53(Al) with drug-loading efficiencies of up to 67% for Fe-BTC and 40% for MIL-53(Al). Several characterization tests, including XRD, FTIR, TGA, BET, FE-SEM, and EDX, confirmed both MOF samples' drug-loading and -release mechanisms. Fe-BTC exhibited a substantial improvement in drug-release efficiency (54%) when exposed to microwave irradiation at pH 7.4 for 50 min, whereas 11% was achieved without the external modality. A similar result was observed at pH 5.3; however, in both cases, the release efficiencies were substantially higher with microwave exposure (40%) than without (6%). In contrast, MIL-53(Al) exhibited greater sensitivity to pH, displaying a higher release rate (66%) after 38 min at pH 5.3 compared to 55% after 50 min at pH 7.4 when subjected to microwave irradiation. These results highlight the potential of both MOFs as highly heat-responsive to thermal stimuli. The results of the MTT assay demonstrated the cell viability across different concentrations of the MOFs after two days of incubation. This suggests that MOFs hold promise as potential candidates for tumor targeting. Additionally, the fact that the cells maintained their viability at different durations of microwave exposure confirms that the latter is a safe modality for triggering drug release from MOFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".