A pigtailed macaque model of Kyasanur Forest disease virus and Alkhurma hemorrhagic disease virus pathogenesis
Bibliographic record
Abstract
Kyasanur Forest disease virus (KFDV) and the closely related Alkhurma hemorrhagic disease virus (AHFV) are emerging flaviviruses that cause severe viral hemorrhagic fevers in humans. Increasing geographical expansion and case numbers, particularly of KFDV in southwest India, class these viruses as a public health threat. Viral pathogenesis is not well understood and additional vaccines and antivirals are needed to effectively counter the impact of these viruses. However, current animal models of KFDV pathogenesis do not accurately reproduce viral tissue tropism or clinical outcomes observed in humans. Here, we show that pigtailed macaques (Macaca nemestrina) infected with KFDV or AHFV develop viremia that peaks 2 to 4 days following inoculation. Over the course of infection, animals developed lymphocytopenia, thrombocytopenia, and elevated liver enzymes. Infected animals exhibited hallmark signs of human disease characterized by a flushed appearance, piloerection, dehydration, loss of appetite, weakness, and hemorrhagic signs including epistaxis. Virus was commonly present in the gastrointestinal tract, consistent with human disease caused by KFDV and AHFV where gastrointestinal symptoms (hemorrhage, vomiting, diarrhea) are common. Importantly, RNAseq of whole blood revealed that KFDV downregulated gene expression of key clotting factors that was not observed during AHFV infection, consistent with increased severity of KFDV disease observed in this model. This work characterizes a nonhuman primate model for KFDV and AHFV that closely resembles human disease for further utilization in understanding host immunity and development of antiviral countermeasures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".