Small Molecule‐Templated DNA Hydrogel with Record Stiffness Integrates and Releases DNA Nanostructures and Gene Silencing Nucleic Acids
Bibliographic record
Abstract
Abstract Deoxyribonucleic acid (DNA) hydrogels are a unique class of programmable, biocompatible materials able to respond to complex stimuli, making them valuable in drug delivery, analyte detection, cell growth, and shape‐memory materials. However, unmodified DNA hydrogels in the literature are very soft, rarely reaching a storage modulus of 103 Pa, and they lack functionality, limiting their applications. Here, a DNA/small‐molecule motif to create stiff hydrogels from unmodified DNA, reaching 105 Pa in storage modulus is used. The motif consists of an interaction between polyadenine and cyanuric acid—which has 3‐thymine like faces—into multimicrometer supramolecular fibers. The mechanical properties of these hydrogels are readily tuned, they are self‐healing and thixotropic. They integrate a high density of small, nontoxic molecules, and are functionalized simply by varying the molecule sidechain. They respond to three independent stimuli, including a small molecule stimulus. These stimuli are used to integrate and release DNA wireframe and DNA origami nanostructures within the hydrogel. The hydrogel is applied as an injectable delivery vector, releasing an antisense oligonucleotide in cells, and increasing its gene silencing efficacy. This work provides tunable, stimuli‐responsive, exceptionally stiff all‐DNA hydrogels from simple sequences, extending these materials’ capabilities.
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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) | 0.001 | 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".