DNA Nanostructures: Current Challenges and Opportunities for Cellular Delivery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it