Ruvidar®—An Effective Anti-Herpes Simplex Virus Agent
Bibliographic record
Abstract
Infectious agents account for millions of deaths every year. The Herpes Simplex Viruses (HSVs) are large double-stranded DNA viruses that infect more than 90% of the human population and can establish life-long latency in human hosts. Currently, effective FDA approved anti-herpetic drugs include acyclovir and later-generation derivatives (valacyclovir and famciclovir), which inhibit viral DNA synthesis. In previous work, we demonstrated that the small molecule Ruvidar® could inhibit numerous pathogenic human viruses when added to solutions of viruses both with and without light activation. In these experiments, we evaluated the ability of Ruvidar® to restrict HSV-1 replication in Vero cells, both by itself and in combination with acyclovir and metformin in the absence of light activation to mimic deep tissue. Ruvidar® successfully inhibited HSV-1 replication at significantly lower concentrations and more effectively than either acyclovir or metformin alone. We also discovered additive and synergistic anti-HSV-1 effects when combinational therapy was tested. Ruvidar® also restricted HSV-1 replication in human U251 glioblastoma astrocytoma cells, remained highly effective against acyclovir-resistant HSV-1 mutants, and protected infected cells from virus-induced cytopathology.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".