Bibliographic record
Abstract
When CRISPR-Cas9, short for Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) with CRISPR-associated protein 9 (Cas9), was successfully harnessed for genome editing in the early 2010s, it marked a new era for biotechnology. The high precision, efficiency, and adaptability of CRISPR-Cas9 have unlocked extraordinary potential in medicine, agriculture, and industrial biology, underscored by the awarding of the Nobel Prize in Chemistry in 2020 to its pioneers. This paper reviews follow-on advancements to the technology addressing challenges, including off-target effects and inefficient delivery systems, and explores its transformative applications in treating genetic disorders, including sickle cell disease, transfusion-dependent β-thalassemia, and cystic fibrosis. Additionally, it highlights ongoing hurdles management of such as high costs and safety and efficacy of heritable gene editing. This study shows that addressing these challenges and fostering ethical and collaborative advancements will be essential for CRISPR technologies, which can fulfill their transformative potential in improving human life quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".