Limited Effects of Type I Interferons on Kyasanur Forest Disease Virus in Cell Culture
Bibliographic record
Abstract
BACKGROUND: The tick-borne flavivirus, Kyasanur Forest disease virus (KFDV) causes seasonal infections and periodic outbreaks in south-west India. The current vaccine offers poor protection with reported issues of coverage and immunogenicity. Since there are no approved prophylactic therapeutics for KFDV, type I IFN-α/β subtypes were assessed for antiviral potency against KFDV in cell culture. METHODOLOGY/PRINCIPAL FINDINGS: The continued passage of KFDV-infected cells with re-administered IFN-α2a treatment did not eliminate KFDV and had little effect on infectious particle production whereas the IFN-sensitive, green fluorescent protein-expressing vesicular stomatitis virus (VSV-GFP) infection was controlled. Further evaluation of the other IFN-α/β subtypes versus KFDV infection indicated that single treatments of either IFN-αWA and IFN-αΚ appeared to be more effective than IFN-α2a at reducing KFDV titres. Concentration-dependent analysis of these IFN-α/β subtypes revealed that regardless of subtype, low concentrations of IFN were able to limit cytopathic effects (CPE), while significantly higher concentrations were needed for inhibition of virion release. Furthermore, expression of the KFDV NS5 in cell culture before IFN addition enabled VSV-GFP to overcome the effects of IFN-α/β signalling, producing a robust infection. CONCLUSIONS/SIGNIFICANCE: Treatment of cell culture with IFN does not appear to be suitable for KFDV eradication and the assay used for such studies should be carefully considered. Further, it appears that the NS5 protein is sufficient to permit KFDV to bypass the antiviral properties of IFN. We suggest that other prophylactic therapeutics should be evaluated in place of IFN for treatment of individuals with KFDV disease.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".