Supramolecular Nucleic Acid-Based Organosilica Nanoparticles Responsive to Physical and Biological Inputs
Bibliographic record
Abstract
Organosilica nanoparticles that contain responsive organic building blocks as constitutive components of the silica network offer promising opportunities for the development of innovative drug formulations, biomolecule delivery, and diagnostic tools. However, the synthetic challenges required to introduce dynamic and multifunctional building blocks have hindered the realization of biomimicking nanoparticles. In this study, capitalizing on our previous research on responsive nucleic acid-based organosilica nanoparticles, we combine the supramolecular programmability of nucleic acid (NA) interactions with sol-gel chemistry. This approach allows us to create dynamic supramolecular bridging units of nucleic acids in a silica-based scaffold. Two peptide nucleic acid-based monoalkoxysilane derivatives, which self-assemble into a supramolecular bis-alkoxysilane through direct base pairing, were chosen as the noncovalent units inserted into the silica network. In addition, a bridging functional NA aptamer leads to the specific recognition of ATP molecules. In a one-step bottom-up approach, the resulting supramolecular building blocks can be used to prepare responsive organosilica nanoparticles. The supramolecular Watson-Crick-Franklin interactions of the organosilica nanoparticles result in a programmable response to external physical (i.e., temperature) and biological (i.e., DNA and ATP) inputs and thus pave the way for the rational design of multifunctional silica materials with application from drug delivery to theranostics.
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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".