Inhibition of Multidrug Resistance of Cancer Cells by Co‐Delivery of DNA Nanostructures and Drugs Using Porous Silicon Nanoparticles@Giant Liposomes
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
Biocompatible, multifunctional, stimuli responsive, and high drug loading capacity are key factors for the new generation of drug delivery platforms. However, it is extremely challenging to create such a platform that inherits all these advanced properties in a single carrier. Herein, porous silicon nanoparticles (PSi NPs) and giant liposomes are assembled on a microfluidic chip as an advanced nano‐in‐micro platform (PSi NPs@giant liposomes), which can co‐load and co‐deliver hydrophilic and hydrophobic drugs combined with synthesized DNA nanostructures, short gold nanorods, and magnetic nanoparticles. The PSi NPs@giant liposomes with photothermal and magnetic responsiveness show good biocompatibility, high loading capacity, and controllable release. The hydrophilic thermal oxidized PSi NPs encapsulate hydrophobic therapeutics within the hydrophilic core of the giant liposomes, endowing high therapeutics loading capacity with tuneable ratio and controllable release. It is demonstrated that the DAO‐E A DNA nanostructures have synergism with drugs and importantly they contribute to the significant enhancement of cell death to doxorubicin‐resistant MCF‐7/DOX cells, overcoming the multidrug resistance in the cancer cells. Therefore, the PSi NPs@giant liposomes nano‐in‐micro platform hold great potential for a cocktail delivery of drugs and DNA nanostructures for effective cancer therapy, controllable drug release with tuneable therapeutics ratio, and both photothermal and magnetic dual responsiveness.
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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) | 0.000 | 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