CRISPR innovations in tissue engineering and gene editing
Bibliographic record
Abstract
The CRISPR/Cas9 system is a powerful tool for genome editing, utilizing the Cas9 nuclease and programmable single guide RNA (sgRNA). However, the Cas9 nuclease activity can be disabled by mutation, resulting in catalytically deactivated Cas9 (dCas9). By combining the customizable sgRNA with dCas9, researchers can inhibit specific gene expression (CRISPR interference, CRISPRi) or activate the expression of a target gene (CRISPR activation, CRISPRa). In this review, we present the principles and recent advancements of these CRISPR technologies, as well as their delivery vectors. We also explore their applications in stem cell engineering and regenerative medicine, with a focus on in vitro stem cell fate manipulation and in vivo treatments. These include the prevention of retinal and muscular degeneration, neural regeneration, bone regeneration, cartilage tissue engineering, and the treatment of blood, skin, and liver diseases. Furthermore, we discuss the challenges of translating CRISPR technologies into regenerative medicine and provide future perspectives. Overall, this review highlights the potential of CRISPR in advancing regenerative medicine and offers insights into its application in various areas of research and therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".