[Antisense oligonucleotides resistance to coxsackievirus B3 infection in HeLa cells].
Bibliographic record
Abstract
OBJECTIVE: Antisense oligodeoxynucleotides (ODNs) hold great promise as therapeutic agents for the treatment of many human diseases. Oligonucleotides specifically hybrid with a selected sequence of RNA or DNA and inhibit the flow of genetic information DNA (or RNA) to proteins. In accordance with the present study, several oligonucleotide sequences that targeted CVB(3) RNA coding Vp(4), Vp(2), Vp(1), cleavage enzyme (P2A) or untranslational region were designed for observation. METHODS: Antiviral activities of these oligonucleotides were evaluated by plaque reduction assay and cytopathic effect (CPE). Cells were also treated with random oligonucleotides as a specificity control. RESULTS: Original ODNs, displaying sequence-specific inhibition ranging from 50% to 90% were identified. Three ODNs (at 5 microM concentration), which targeting at the pyrimidine - rich tract (S(CB1)), structural protein VP(1) (S(CB4)) and initiation of translation (S(CB2)) were shown to exhibit inhibition of viral infection 75% to 90% at 1 MOI. There was only low antiviral activity in S(CB5) and S(CB3), and random oligonucleotide were not found having antiviral activity. Oligonucleotides in this study were not toxic at 5 microM concentration. CONCLUSION: A better antiviral antisense oligonucleotides as S(CB1) and S(CB4), S(CB2), could provide candidate antisense oligonucleotides for the treatment of CVB(3) infection.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".