Highly Hybridizable Spherical Nucleic Acids by Tandem Glutathione Treatment and Polythymine Spacing
Bibliographic record
Abstract
Gold nanoparticle (AuNP)-templated spherical nucleic acids (SNAs) have been demonstrated as an important functional material in bionanotechnology. Fabrication of SNAs having high hybridization capacity to their complementary sequences is critical to ensure their applicability in areas such as antisense gene therapy and cellular sensing. The traditional salt-aging procedure is effective but tedious, requiring 1-3 days to complete. The rapid low-pH assisted protocol is efficient, but causes concerns related to nonspecific DNA adsorption to the AuNP core. To address these issues, we systematically compared the SNAs prepared by these two methods (salt-aging method and low-pH protocol). In terms of the number of complementary DNA that each SNA can bind and the average binding affinity of each thiolated DNA probe to its complementary strand, both methods yielded comparable hybridizability, although higher loading capacity was witnessed with SNAs made using the low-pH method. Additionally, it was found that nonspecific DNA binding could be eliminated almost completely by a simple glutathione (GSH) treatment of SNAs. Compared to conventional methods using toxic mercapto-hexanol or alkanethiols to remove nonspecific DNA adsorption, GSH is mild, cost-effective, and technically easy to use. In addition, GSH-passivated SNAs minimize the toxicity concerns related to AuNP-induced GSH depletion and therefore offer a more biocompatible alternative to previously reported SNAs. Moreover, rational design of probe sequences through inclusion of a polythymine spacer into the DNA sequences resulted in enhanced DNA loading capacity and stability against salt-induced aggregation. This work provides not only efficient and simple technical solutions to the issue of nonspecific DNA adsorption, but also new insights into the hybridizability of SNAs.
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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".