Nucleocapsid protein-based vaccine provides protection in mice against lethal Crimean-Congo hemorrhagic fever virus challenge
Bibliographic record
Abstract
Crimean-Congo hemorrhagic fever (CCHF) is an acute, often fatal viral disease characterized by rapid onset of febrile symptoms followed by hemorrhagic manifestations.The etiologic agent, CCHF orthonairovirus (CCHFV), can infect several mammals in nature but only seems to cause clinical disease in humans.Over the past two decades there has been an increase in total number of CCHF case reports, including imported CCHF patients, and an expansion of CCHF endemic areas.Despite its increased public health burden there are currently no licensed vaccines or treatments to prevent CCHF.We here report the development and assessment of the protective efficacy of an adenovirus (Ad)-based vaccine expressing the nucleocapsid protein (N) of CCHFV (Ad-N) in a lethal immunocompromised mouse model of CCHF.The results show that Ad-N can protect mice from CCHF mortality and that this platform should be considered for future CCHFV vaccine strategies. Author summaryCrimean-Congo hemorrhagic fever (CCHF) is a tick-borne disease that can manifest as a viral hemorrhagic fever syndrome.The CCHF virus is widely spread throughout the African continent, the Balkans, the Middle East, Southern Russia and Western Asia where it remains a serious public health concern.Currently, there are no licensed treatments or vaccines available, and medical countermeasures are urgently needed.We developed an adenovirus vector vaccine based on the conserved structural nucleoprotein (N) as the antigen.A prime-boost approach showed promising
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 | 0.000 | 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".