Fabrication of Calcium Phosphate‐Based Nanocomposites Incorporating DNA Origami, Gold Nanorods, and Anticancer Drugs for Biomedical Applications
Bibliographic record
Abstract
Abstract DNA origami is designed by folding DNA strands at the nanoscale with arbitrary control. Due to its inherent biological nature, DNA origami is used in drug delivery for enhancement of synergism and multidrug resistance inhibition, cancer diagnosis, and many other biomedical applications, where it shows great potential. However, the inherent instability and low payload capacity of DNA origami restrict its biomedical applications. Here, this paper reports the fabrication of an advanced biocompatible nano‐in‐nanocomposite, which protects DNA origami from degradation and facilities drug loading. The DNA origami, gold nanorods, and molecular targeted drugs are co‐incorporated into pH responsive calcium phosphate [Ca3(PO4)2] nanoparticles. Subsequently, a thin layer of phospholipid is coated onto the Ca3(PO4)2 nanoparticle to offer better biocompatibility. The fabricated nanocomposite shows high drug loading capacity, good biocompatibility, and a photothermal and pH‐responsive payload release profile and it fully protects DNA origami from degradation. The codelivery of DNA origami with cancer drugs synergistically induces cancer cell apoptosis, reduces the multidrug resistance, and enhances the targeted killing efficiency toward human epidermal growth factor receptor 2 positive cells. This nanocomposite is foreseen to open new horizons for a variety of clinical and biomedical applications.
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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".