CRISPR-Cas and CRISPR-based screening system for precise gene editing and targeted cancer therapy
Bibliographic record
Abstract
Target cancer therapy has been developed for clinical cancer treatment based on the discovery of CRISPR (clustered regularly interspaced short palindromic repeat) -Cas system. This forefront and cutting-edge scientific technique improves the cancer research into molecular level and is currently widely utilized in genetic investigation and clinical precision cancer therapy. In this review, we summarized the genetic modification by CRISPR/Cas and CRISPR screening system, discussed key components for successful CRISPR screening, including Cas enzymes, guide RNA (gRNA) libraries, target cells or organs. Furthermore, we focused on the application for CAR-T cell therapy, drug target, drug screening, or drug selection in both ex vivo and in vivo with CRISPR screening system. In addition, we elucidated the advantages and potential obstacles of CRISPR system in precision clinical medicine and described the prospects for future genetic therapy.In summary, we provide a comprehensive and practical perspective on the development of CRISPR/Cas and CRISPR screening system for the treatment of cancer defects, aiming to further improve the precision and accuracy for clinical treatment and individualized gene 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".