Self‐Assembly and Biological Properties of Highly Fluorinated Oligonucleotide Amphiphiles
Bibliographic record
Abstract
Abstract Nucleic acids, used as therapeutics to silence disease‐related genes, offer significant advantages over small molecule drugs: they provide high specificity, the ability to target “undruggable” molecules, and adaptability to a wide range of disease phenotypes. However, their instability in biological media, as well their rapid clearance from the organism limit their applicability, necessitating the use of nanocarriers to overcome these challenges. Among these strategies, spherical nucleic acids (SNA)—composed of a densely packed corona of oligonucleotides around a nanoparticle—have emerged as a powerful tool, in particular when self‐assembled from DNA amphiphiles. This non‐covalent strategy however has caveats, especially when it comes to stability in complex biological media, where these SNAs disassemble in contact to serum proteins. Here, we developed highly fluorinated DNA amphiphiles that readily self‐assemble into SNAs and have tunable stability profiles in biological media. They are made of branched fluorinated moieties with potentially improved biodegradability as compared to their linear counterparts. Depending on the number of fluorophilic interactions, the self‐assembled SNAs can have excellent serum stabilities—up to days—and readily deliver nucleic acid therapeutics for gene silencing applications. These systems show great potential as promising candidates for nucleic acid‐based therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".