CRISPR/Cas9 Gene Editing: From Basic Mechanisms to Improved Strategies for Enhanced Genome Engineering In Vivo
Bibliographic record
Abstract
INTRODUCTION: Targeted genome editing using the CRISPR/Cas9 technology is becoming a major area of research due to its high potential for the treatment of genetic diseases. Our understanding of this approach has expanded in recent years yet several new challenges have presented themselves as we explore the boundaries of this exciting new technology. Chief among these is improving the efficiency but also the preciseness of genome editing. The efficacy of CRISPR/Cas9 technology relies in part on the use of one of the major DNA repair pathways, Homologous recombination (HR), which is primarily active in S and G2 phases of the cell cycle. Problematically, the HR potential is highly variable from cell type to cell type and most of the cells of interest to be targeted in vivo for precise genome editing are in a quiescent state. CONCLUSION: In this review, we discuss the recent advancements in improving targeted CRISPR/Cas9 based genome editing and the promising ways of delivering this technology in vivo to the cells of interest.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".