Broad-spectrum and virus-specific nucleic acid-based antivirals against influenza
Bibliographic record
Abstract
Rapid increase in drug-resistant influenza virus isolates, and pandemic threat posed by highly pathogenic avian influenza A and swine flu viruses provide clear and compelling reasons for fast tracking development of novel antiviral drugs. Nucleic acid-based drugs represent a promising class of novel antiviral agents that can be designed to target various seasonal, pandemic and avian influenza viruses. Nucleic acids can be designed to elicit broad-spectrum antiviral responses in the host, by suppressing viral gene expression, or by inducing cleavage or degradation of viral RNA. Immunomodulating nucleic acids, such as double stranded RNA and CpG oligonucleotides, can be potent anti-influenza agents that work by eliciting protective innate and adaptive immunity in the host. By activating the toll-like receptor signaling pathways, these drugs can activate the host's antiviral and inflammatory defenses to combat influenza viruses. Antisense oligonucleotides, small interfering RNAs (siRNA), and nanoRNAs represent sequence specific gene-silencing approaches that could be deployed to suppress or inhibit viral protein gene expression. Lastly, catalytic nucleic acids such as DNAzymes and/or ribozymes can suppress viral replication by repeatedly cleaving viral mRNAs and template RNAs. In summary, nucleic acid-based antiviral agents are versatile, diverse and could complement existing antiviral drugs in combating influenza.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".