Bibliographic record
Abstract
Introduction and Definition: Zinc finger nucleases (ZFNs) are a group of artificial restriction enzymes which are crucial for various processes in gene editing. They can target diseases based on gene therapy approaches and can be applied to a variety of eukaryotic cells through the precise introduction of double-strand breaks at specific genomic locations. Zinc Fingers were discovered in 1985 with ZFNs being discovered shortly after that in 1996. Body: ZFNs are engineered by fusing DNA-binding domains with the nuclease domain of a Fokl restriction enzyme. This results in custom-designed enzymes capable of binding to the target DNA sequence and inducing a double-strand break at the desired site. This binding is possible due to the high degree of specificity to recognize specific DNA sequences within the genome associated with disease-causing mutations. The DNA repair mechanisms, including both non-homologous end joining (NHEJ) and homology-directed repair (HDR), are triggered in response to double-strand breaks induced by ZFNs at specific genomic locations inside the cell. Cells possess innate mechanisms to mend such breaks. In the case of (NHEJ), the broken DNA ends are often rejoined, resulting in small insertions or deletions (indels) at the break site, potentially disrupting the function of the targeted gene. Alternatively, (HDR) can be employed, utilising an external DNA template to accurately introduce desired genetic modifications. ZFNs can be delivered to target cells using various methods and depends on factors such as the target tissue, specific diseases being targeted, and safety considerations. Viral vectors such as adeno-associated viruses or lentiviruses are used to deliver ZFNs into target cells. Lipid nanoparticles are synthetic encapsulates that allow repeat administration and transient delivery of ZFNs. Direct delivery involves physically introducing ZFNs which can allow a high degree of precision.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".