CRISPR-Cas9 delivery strategies with engineered extracellular vesicles
Bibliographic record
Abstract
Therapeutic genome editing has the potential to cure diseases by directly correcting genetic mutations in tissues and cells. Recent progress in the CRISPR-Cas9 systems has led to breakthroughs in gene editing tools because of its high orthogonality, versatility, and efficiency. However, its safe and effective administration to target organs in patients is a major hurdle. Extracellular vesicles (EVs) are endogenous membranous particles secreted spontaneously by all cells. They are key actors in cell-to-cell communication, allowing the exchange of select molecules such as proteins, lipids, and RNAs to induce functional changes in the recipient cells. Recently, EVs have displayed their potential for trafficking the CRISPR-Cas9 system during or after their formation. In this review, we highlight recent developments in EV loading, surface functionalization, and strategies for increasing the efficiency of delivering CRISPR-Cas9 to tissues, organs, and cells for eventual use in gene therapies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".