DNA Nanostructures: Current Challenges and Opportunities for Cellular Delivery
Bibliographic record
Abstract
DNA nanotechnology has produced a wide range of self-assembled structures, offering unmatched possibilities in terms of structural design. Because of their programmable assembly and precise control of size, shape, and function, DNA particles can be used for numerous biological applications, including imaging, sensing, and drug delivery. While the biocompatibility, programmability, and ease of synthesis of nucleic acids have rapidly made them attractive building blocks, many challenges remain to be addressed before using them in biological conditions. Enzymatic hydrolysis, low cellular uptake, immune cell recognition and degradation, and unclear biodistribution profiles are yet to be solved. Rigorous methodologies are needed to study, understand, and control the fate of self-assembled DNA structures in physiological conditions. In this review, we describe the current challenges faced by the field as well as recent successes, highlighting the potential to solve biology problems or develop smart drug delivery tools. We then propose an outlook to drive the translation of DNA constructs toward preclinical design. We particularly believe that a detailed understanding of the fate of DNA nanostructures within living organisms, achieved through thorough characterization, is the next required step to reach clinical maturity.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".