Double‐Layered Metal‐Organic‐Frameworks‐Based Microswimmers for Adaptive Dual‐Drug Anti‐Cancer Therapy Using Artemisinin‐Based Compounds
Bibliographic record
Abstract
Magnetic microrobots have the potential for anti‐cancer drug delivery; however, using dual‐drug to counter drug resistance, a critical issue in cancer research, has only been briefly investigated. This study introduces the double‐layered metal‐organic‐frameworks (MOFs)‐based microswimmers for sustained dual‐drug delivery. These microswimmers are made up of ZIF‐8 and MIL‐100, biocompatible MOFs, that can selectively adsorb two types of drugs. The MOFs increase the surface area of the microswimmers by ≈2.42 times, which greatly enhances drug adsorption, and improves hydrophilicity, which reduces adhesion for surface locomotion. Their biocompatibility and dual‐drug adsorption are verified through cell viability and drug‐loading tests. The microswimmers have remarkable versatility in loading different drug combinations (DHA + 5‐FU, CPT‐11, or DOX), indicating the potential for adaptive therapy. They can inhibit cancer cells for up to 72 h through the sustained release of dual drugs. In contrast, drug treatments without microswimmers only inhibit cell proliferation for 24 h, leading to a significant rebound. This study provides a method to mass fabricate fully biocompatible microrobots with dual drug loading versatility and high drug adsorption capacity; thus, suggests a powerful platform for sustained adaptive dual‐drug therapy.
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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".