Advancements in Aptamer‐Driven DNA Nanostructures for Precision Drug Delivery
Bibliographic record
Abstract
DNA nanostructures exhibit versatile geometries and possess sophisticated capabilities not found in other nanomaterials. They serve as customizable nanoplatforms for orchestrating the spatial arrangement of molecular components, such as biomolecules, antibodies, or synthetic nanomaterials. This is achieved by incorporating oligonucleotides into the design of the nanostructure. In the realm of drug delivery to cancer cells, there is a growing interest in active targeting assays to enhance efficacy and selectivity. The active targeting approach involves a "key-lock" mechanism where the carrier, through its ligand, recognizes specific receptors on tumor cells, facilitating the release of drugs. Various DNA nanostructures, including DNA origami, Tetrahedral, nanoflower, cruciform, nanostar, nanocentipede, and nanococklebur, can traverse the lipid layer of the cell membrane, allowing cargo delivery to the nucleus. Aptamers, easily formed in vitro, are recognized for their targeted delivery capabilities due to their high selectivity for specific targets and low immunogenicity. This review provides a comprehensive overview of recent advancements in the formation and modification of aptamer-modified DNA nanostructures within drug delivery systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".