Development of Ribozyme-Based Gene-Inactivations; The Example of the Hepatitis Delta Virus Ribozyme
Bibliographic record
Abstract
The development of gene-inactivation systems is an active and important field for both functional genomics and gene therapy. Towards this end, ribozymes (i.e. RNA enzymes), that specifically recognize and subsequently catalyze the cleavage of other target RNA molecules, are attractive molecular tools. Ribozymes represent an interesting alternative to the RNA interference (RNAi) approach for gene inactivation, especially given the fact that RNAi seems to trigger an immunological response and has demonstrated off-target effects. However, the design and optimization of a ribozyme-based gene-inactivation system is not a straightforward procedure. Several aspects need to be considered in the experimental design in order to provide a suitable suppression system. In this review we present the advances in this domain made available from work using the hepatitis delta virus (HDV) ribozyme as a cis-acting RNA motif in molecular biology, as well as a trans-acting molecular scissor for the development of a gene-inactivation system. This HDV ribozyme technology possesses several unique features that are all related to the fact that it is the only catalytic cleaving RNA motif that has been discovered in humans.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".