Pathogenesis and Immune Response of Crimean-Congo Hemorrhagic Fever Virus in a STAT-1 Knockout Mouse Model
Bibliographic record
Abstract
Tick-borne Crimean-Congo hemorrhagic fever virus (CCHFV) causes a severe hemorrhagic syndrome in humans but not in its vertebrate animal hosts. The pathogenesis of the disease is largely not understood due to the lack of an animal model. Laboratory animals typically show no overt signs of disease. Here, we describe a new small-animal model to study CCHFV pathogenesis that manifests clinical disease, similar to that seen in humans, without adaptation of the virus to the host. Our studies revealed that mice deficient in the STAT-1 signaling molecule were highly susceptible to infection, succumbing within 3 to 5 days. After CCHFV challenge, mice exhibited fever, leukopenia, thrombocytopenia, and highly elevated liver enzymes. Rapid viremic dissemination and extensive replication in visceral organs, mainly in liver and spleen, were associated with prominent histopathologic changes in these organs. Dramatically elevated proinflammatory cytokine levels were detected in the blood of the animals, suggestive of a cytokine storm. Immunologic analysis revealed delayed immune cell activation and intensive lymphocyte depletion. Furthermore, this study also demonstrated that ribavirin, a suggested treatment in human cases, protects mice from lethal CCHFV challenge. In conclusion, our data demonstrate that the interferon response is crucial in controlling CCHFV replication in this model, and this is the first study that offers an in-depth in vivo analysis of CCHFV pathophysiology. This new mouse model exhibits key features of fatal human CCHF, proves useful for the testing of therapeutic strategies, and can be used to study virus attenuation.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".