Disease modeling for Ebola and Marburg viruses
Bibliographic record
Abstract
The filoviruses Ebola and Marburg are zoonotic agents that are classified as both biosafety level 4 and category A list pathogens. These viruses are pathogenic in humans and cause isolated infections or epidemics of viral hemorrhagic fever, mainly in Central Africa. Their natural reservoir has not been definitely identified, but certain species of African bat have been associated with Ebola and Marburg infections. Currently, there are no licensed options available for either treatment or prophylaxis. Different animal models have been developed for filoviruses including mouse, guinea pig and nonhuman primates. The 'gold standard' animal models for pathogenesis, treatment and vaccine studies are rhesus and cynomolgus macaques. This article provides a brief overview of the clinical picture and the pathology/pathogenesis of human filovirus infections. The current animal model options are discussed and compared with regard to their value in different applications. In general, the small animal models, in particular the mouse, are the most feasible for high biocontainment facilities and they offer the most options for research owing to the greater availability of immunologic and genetic tools. However, their mimicry of the human diseases as well as their predictive value for therapeutic efficacy in primates is limited, thereby making them, at best, valuable initial screening tools for pathophysiology, treatment and vaccine studies.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".