Bibliographic record
Abstract
With the introduction of precision genome editing using CRISPR-Cas9 technology, we have entered a new era of genetic engineering and gene therapy. With RNA-guided endonucleases, such as Cas9, it is possible to engineer DNA double strand breaks (DSB) at specific genomic loci. DSB repair by the error-prone non-homologous end-joining (NHEJ) pathway can disrupt a target gene by generating insertions and deletions. Alternatively, Cas9-mediated DSBs can be repaired by homology-directed repair (HDR) using an homologous DNA repair template, thus allowing precise gene editing by incorporating genetic changes into the repair template. HDR can introduce gene sequences for protein epitope tags, delete genes, make point mutations, or alter enhancer and promoter activities. In anticipation of adapting this technology for gene therapy in human somatic cells, much focus has been placed on increasing the fidelity of CRISPR-Cas9 and increasing HDR efficiency to improve precision genome editing. In this review, we will discuss applications of CRISPR technology for gene inactivation and genome editing with a focus on approaches to enhancing CRISPR-Cas9-mediated HDR for the generation of cell and animal models, and conclude with a discussion of recent advances and challenges towards the application of this technology for gene therapy in humans.
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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 | 0.001 | 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".