Stimuli-responsive nanoformulations for CRISPR-Cas9 genome editing
Bibliographic record
Abstract
The CRISPR-Cas9 technology has changed the landscape of genome editing and has demonstrated extraordinary potential for treating otherwise incurable diseases. Engineering strategies to enable efficient intracellular delivery of CRISPR-Cas9 components has been a central theme for broadening the impact of the CRISPR-Cas9 technology. Various non-viral delivery systems for CRISPR-Cas9 have been investigated given their favorable safety profiles over viral systems. Many recent efforts have been focused on the development of stimuli-responsive non-viral CRISPR-Cas9 delivery systems, with the goal of achieving efficient and precise genome editing. Stimuli-responsive nanoplatforms are capable of sensing and responding to particular triggers, such as innate biological cues and external stimuli, for controlled CRISPR-Cas9 genome editing. In this Review, we overview the recent advances in stimuli-responsive nanoformulations for CRISPR-Cas9 delivery, highlight the rationale of stimuli and formulation designs, and summarize their biomedical applications.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".