Bibliographic record
Abstract
CRISPR/Cas9 is a gene editing tool that is rapidly replacing previous technologies, such as ZFNs and TALENS, to rectify disease-causing mutations. Oftentimes, these diseases have treatment methods that target the symptoms rather than the cause. Recent innovations in bioengineering suggest that novel gene therapies may provide a better alternative than existing treatments, by correcting the cause of the disorder directly: the genomic DNA. The excitement around gene therapy, however, is abated by the challenges in how to deliver the technology. Currently, there are two methods of delivery, viral and non-viral vectors, of which non-viral vectors are considered safer and more practical. Such non-viral carriers include gold nanoparticles, lipid nanoparticles, and polymeric carriers, of which will be the focus of this paper. This review examines the efficacy of these existing non-viral carriers through a comprehensive literature analysis. We compare the percentage of cells showing the targeted change in vivo and in vitro across the different vehicles in an attempt to understand how efficacy changes across vectors. Overall, we highlight that the optimal delivery platform is likely dependent on the disease model and target tissue. In the future, researchers can use this analysis to assess currently available designs and develop new carriers for transporting CRISPR/Cas9 to specific targets in vivo and in vitro.
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 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) | 0.000 | 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 teacher head, 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".