Bibliographic record
Abstract
DNA is one of the most predictable and programmable self-assembling molecules. This Chapter will focus on an emerging research area at the interface of DNA nanotechnology and supramolecular chemistry. This area brings a range of orthogonal interactions to DNA assembly, resulting in new motifs from a minimum number of DNA sequences. It allows the use of DNA to guide the assembly of two- and three-dimensional structures with precise positioning of functional units, such as transition metals, polymers and gold nanoparticles on the nanometer scale. Small molecules and polymers introduce profound changes in how DNA assembles, and can significantly stabilize this molecule. We describe synthetic methods for the construction of DNA cages and nanotubes with deliberate variation of geometry, size, single- and double-stranded forms, permeability and length. They are reconfigurable and molecule-responsive, e.g., cages can open and close on demand with specific RNA sequences that are overexpressed in cancer cells. The encapsulation of nanoparticles and small molecules and their release when specific DNA strands are added is demonstrated, and their cellular uptake, gene silencing and nuclease resistance are examined. We describe the synthesis of DNA-polymer conjugates, in which the polymeric block is monodisperse and fully sequence-controlled. The sequence order of monomer units in these new materials plays a major role in determining their self-assembly, and their association with DNA cages gives rise to protein-inspired behavior. The molecules shown here represent a new class of selective cellular probes and drug delivery tools, and can assist the development of targeted therapeutic routes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".