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Record W3217364940 · doi:10.1128/jvi.01568-21

Nucleoside-Modified mRNA Vaccines Protect IFNAR <sup>–/–</sup> Mice against Crimean-Congo Hemorrhagic Fever Virus Infection

2021· article· en· W3217364940 on OpenAlexaff

Bibliographic record

VenueJournal of Virology · 2021
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Vectors
Canadian institutionsAcuitas Therapeutics (Canada)
Fundersnot available
KeywordsImmunizationVaccinationImmune systemVirusAntibodyDNA vaccinationNucleoproteinViral hemorrhagic fever

Abstract

fetched live from OpenAlex

Crimean-Congo hemorrhagic fever virus (CCHFV) is a zoonotic pathogen causing Crimean-Congo hemorrhagic fever (CCHF), a severe fever disease. CCHFV has a wide distribution and is endemic in several areas around the world. Cases of CCHF are also being reported in new areas, indicating an expansion of the disease, which is of high concern. Dispersion of the disease, high fatality rate, and no approved vaccine makes CCHF a threat to global health. The development of a vaccine is thus of great importance. Here we show 100% protection against lethal CCHFV infection in mice immunized with mRNA-LNP encoding for different CCHFV proteins. The vaccination showed both robust humoral and cellular immunity. mRNA-LNP vaccines combine the ability to induce an effective immune response, the safety of a transient carrier, and the flexibility of genetic vaccines. This and our results from the current study support the development of a mRNA-LNP based vaccine against CCHFV.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.861

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.290
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations67
Published2021
Admission routes1
Has abstractyes

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