Development and comparison of procedures for the selection of delta ribozyme cleavage sites within the hepatitis B virus
Bibliographic record
Abstract
Delta ribozyme possesses several unique features related to the fact that it is the only catalytic RNA known to be naturally active in human cells. This makes it attractive as a therapeutic tool for the inactivation of clinically relevant RNAs. However, several hurdles must be overcome prior to the development of useful gene-inactivation systems based on delta ribozyme. We have developed three procedures for the selection of potential delta ribozyme target sites within the hepatitis B virus (HBV) pregenome: (i) the use of bioinformatic tools coupled to biochemical assays; (ii) RNase H hydrolysis with a pool of oligonucleotides; and (iii) cleavage assays with a pool of ribozymes. The results obtained with delta ribozyme show that these procedures are governed by several rules, some of which are different from those both for other catalytic RNAs and antisense oligonucleotides. Together, these procedures identified 12 sites in the HBV pregenome that can be cleaved by delta ribozymes, although with different efficiencies. Clearly, both target site accessibility and the ability to form an active ribozyme-substrate complex constitute interdependent factors that can best be addressed using a combinatorial library of either oligonucleotides or ribozymes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 0.000 |
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".