Engineered Nanomaterials to Potentiate CRISPR/Cas9 Gene Editing for Cancer Therapy
Bibliographic record
Abstract
Clustered regularly interspaced short palindromic repeats/associated protein 9 (CRISPR/Cas9) gene-editing technology shows promise for manipulating single or multiple tumor-associated genes and engineering immune cells to treat cancers. Currently, most gene-editing strategies rely on viral delivery; yet, while being efficient, many limitations, mainly from safety and packaging capacity considerations, hinder the use of viral CRISPR vectors in cancer therapy. In contrast, recent advances in non-viral CRISPR/Cas9 nanoformulations have paved the way for better cancer gene editing, as these nanoformulations can be engineered to improve safety, efficiency, and specificity through optimizing the packaging capacity, pharmacokinetics, and targetability. In this review, the advance in non-viral CRISPR delivery is highlighted, and there is a discussion on how these approaches can be potentially used to treat cancers in addressing the aforementioned limitations, followed by the perspectives in designing a proper CRISPR/Cas9-based cancer nanomedicine system with translational potential.
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 | 0.000 | 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".