Responsive Nucleic Acid-Based Organosilica Nanoparticles
Bibliographic record
Abstract
The development of smart nanoparticles (NPs) that encode responsive features in the structural framework promises to extend the applications of NP-based drugs, vaccines, and diagnostic tools. New nanocarriers would ideally consist of a minimal number of biocompatible components and exhibit multiresponsive behavior to specific biomolecules, but progress is limited by the difficulty of synthesizing suitable building blocks. Through a nature-inspired approach that combines the programmability of nucleic acid interactions and sol-gel chemistry, we report the incorporation of synthetic nucleic acids and analogs, as constitutive components, into organosilica NPs. We prepared different nanomaterials containing single-stranded nucleic acids that are covalently embedded in the silica network. Through the incorporation of functional nucleic acids into the organosilica framework, the particles respond to various biological, physical, and chemical inputs, resulting in detectable physicochemical changes. The one-step bottom-up approach used to prepare organosilica NPs provides multifunctional systems that combine the tunability of oligonucleotides with the stiffness, low cost, and biocompatibility of silica for different applications ranging from drug delivery to sensing.
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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".