Evaluation of the Efficacy And Toxicity of RNAs Targeting HIV-1 Production for Use in Gene or Drug Therapy
Bibliographic record
Abstract
Small RNA therapies targeting post-integration steps in the HIV-1 replication cycle are among the top candidates for gene therapy and have the potential to be used as drug therapies for HIV-1 infection. Post-integration inhibitors include ribozymes, short hairpin (sh) RNAs, small interfering (si) RNAs, U1 interference (U1i) RNAs and RNA aptamers. Many of these have been identified using transient co-transfection assays with an HIV-1 expression plasmid and some have advanced to clinical trials. In addition to measures of efficacy, small RNAs have been evaluated for their potential to affect the expression of human RNAs, alter cell growth and/or differentiation, and elicit innate immune responses. In the protocols described here, a set of transient transfection assays designed to evaluate the efficacy and toxicity of RNA molecules targeting post-integration steps in the HIV-1 replication cycle are described. We have used these assays to identify new ribozymes and optimize the format of shRNAs and siRNAs targeting HIV-1 RNA. The methods provide a quick set of assays that are useful for screening new anti-HIV-1 RNAs and could be adapted to screen other post-integration inhibitors of HIV-1 replication.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".