Lipid-Nanoparticle-Based Delivery of CRISPR/Cas9 Genome-Editing Components
Bibliographic record
Abstract
Gene editing mediated by CRISPR/Cas9 systems is due to become a beneficial therapeutic option for treating genetic diseases and some cancers. However, there are challenges in delivering CRISPR components which necessitate sophisticated delivery systems for safe and effective genome editing. Lipid nanoparticles (LNPs) have become an attractive nonviral delivery platform for CRISPR-mediated genome editing due to their low immunogenicity and application flexibility. In this review, we provide a background of CRISPR-mediated gene therapy, as well as LNPs and their applicable characteristics for delivering CRISPR components. We then highlight the challenges of CRISPR delivery, which have driven the significant development of new, safe, and optimized LNP formulations in the past decade. Finally, we discuss considerations for using LNPs to deliver CRISPR and future perspectives on clinical translation of LNP-CRISPR gene editing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".