Reverse Genetics for Crimean-Congo Hemorrhagic Fever Virus
Bibliographic record
Abstract
The widespread geographical distribution of Crimean-Congo hemorrhagic fever (CCHF) virus (more than 30 countries) and its ability to produce severe human disease with high mortality rates (up to 60%) make CCHF a major public health concern worldwide. We describe here the successful establishment of a reverse genetics technology for CCHF virus, a member of the genus Nairovirus, family BUNYAVIRIDAE: The RNA polymerase I (pol I) system was used to generate artificial viral RNA genome segments (minigenomes), which contained different reporter genes in antisense (virus RNA) or sense (virus-complementary RNA) orientation flanked by the noncoding regions of the CCHF virus S segment. Reporter gene expression was observed in different eukaryotic cell lines following transfection and subsequent superinfection with CCHF virus, confirming encapsidation, transcription, and replication of the pol I-derived minigenomes. The successful transfer of reporter gene activity to fresh cells demonstrated the generation of recombinant CCHF viruses, thereby confirming the packaging of the pol I-derived minigenomes into progeny viruses. The system offers a unique opportunity to study the biology of nairoviruses and to develop therapeutic and prophylactic measures against CCHF infections. In addition, we demonstrated for the first time that the human pol I system can be used to develop reverse genetics approaches for viruses in the family BUNYAVIRIDAE: This is important since it might facilitate the manipulation of bunyaviruses with cell and host tropisms restricted to primates.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".