Core–shell inorganic NP@MOF nanostructures for targeted drug delivery and multimodal imaging-guided combination tumor treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is well known that metal-organic framework (MOF) nanostructures have unique characteristics such as high porosity, large surface areas and adjustable functionalities, so they are ideal candidates for developing drug delivery systems (DDSs) as well as theranostic platforms in cancer treatment. Despite the large number of MOF nanostructures that have been discovered, conventional MOF-derived nanosystems only have a single biofunctional MOF source with poor colloidal stability. Accordingly, developing core-shell MOF nanostructures with good colloidal stability is a useful method for generating efficient drug delivery, multimodal imaging and synergistic therapeutic systems. The preparation of core-shell MOF nanostructures has been done with a variety of materials, but inorganic nanoparticles (NPs) are highly effective for drug delivery and imaging-guided tumor treatment. Herein, we aimed to overview the synthesis of core-shell inorganic NP@MOF nanostructures followed by the application of core-shell MOFs derived from magnetic, quantum dots (QDs), gold (Au), and gadolinium (Gd) NPs in drug delivery and imaging-guided tumor treatment. Afterward, we surveyed different factors affecting prolonged drug delivery and cancer therapy, cellular uptake, biocompatibility, biodegradability, and enhanced permeation and retention (EPR) effect of core-shell MOFs. Last but not least, we discussed the challenges and the prospects of the field. We envision this article may hold great promise in providing valuable insights regarding the application of hybrid nanostructures as promising and potential candidates for multimodal imaging-guided combination cancer therapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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