Kyasanur Forest disease virus non-mouse animal models: a pilot study
Bibliographic record
Abstract
OBJECTIVES: Mouse models have delivered variable recapitulation of Kyasanur Forest disease (KFD) pathology and consistently demonstrated neurological involvement which may be a limited feature of human disease. With the purpose of more accurately modelling human disease progression we infected several small-mammalian models: guinea pigs, hamsters and ferrets with a titered infectious dose of Kyasanur Forest disease virus (KFDV). Clinical indicators of disease severity were observed for seventeen days, on day eighteen a visual post-mortem analysis of visceral organs was conducted. Viral load in selected tissues was measured to infer disease signs and the establishment of viral replication. DATA DESCRIPTION: Daily monitoring did not reveal any observable signs of illness; weight loss was minimal across species and gross pathology did not indicate severe viral infection. Tissue specific tropism and establishment of viral infection was monitored by quantitative real-time polymerase chain reaction (qRT-PCR). No viral replication was detected in ferrets (n = 0/3), but was present in the spleen of guinea pigs (n = 3/3) and the brain of hamsters (n = 3/3). Low levels of viral RNA were detected in multiple hamster tissues (kidney, liver, lung and spleen) suggesting the possibility of viral tropism and possible adaptation to the host. No serological tests were performed.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".