DNA Nanotubes with Hydrophobic Environments: Toward New Platforms for Guest Encapsulation and Cellular Delivery
Bibliographic record
Abstract
Natural systems combine different supramolecular interactions in a hierarchical manner to build structures. In contrast, DNA nanotechnology relies almost exclusively on DNA base pairing for structure generation. Introducing other supramolecular interactions can expand the structural and functional range of DNA assemblies, but this requires an understanding of the interplay between these interactions. Here, an economic strategy to build DNA nanotubes functionalized with lipid-like polymers is reported. When these polymers are linked to the nanotube using a spacer, they fold inside to create a hydrophobic environment within the nanotube; the nanotube can encapsulate small molecules and conditionally release them when specific DNA strands are added, as monitored by single-molecule fluorescence microscopy. When the polymers are directly linked to the nanostructure without spacers, they interact intermolecularly to form a network of DNA bundles. This morphological switch can be directly observed using a strand displacement strategy. The two association modes result in different cellular uptake behavior. Nanotubes with internal hydrophobic association show dye-mediated mitochondrial colocalization inside cells; while the bundles disassemble into smaller polymer-coated structures that reduce the extent of nonspecific cellular uptake. This approach uncovers parameters to direct the hierarchical assembly of DNA nanostructures, and produces promising materials for targeted drug delivery.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".