MétaCan
Menu
Back to cohort
Record W2087246172 · doi:10.1160/th05-05-0335

The role of reverse genetics systems in studying viral hemorrhagic fevers

2005· review· en· W2087246172 on OpenAlexaff
Allison Groseth, Gabriele Neumann, Yoshihiro Kawaoka, Hideki Ebihara, Heinz Feldmann

Bibliographic record

VenueThrombosis and Haemostasis · 2005
Typereview
Languageen
FieldMedicine
TopicViral Infections and Outbreaks Research
Canadian institutionsUniversity of ManitobaPublic Health Agency of Canada
FundersNational Institute of Allergy and Infectious Diseases
KeywordsReverse geneticsVirologyPathogenesisBiologyViral pathogenesisViral replicationVirulenceVirusViral hemorrhagic feverImmunologyGeneticsGeneMutant

Abstract

fetched live from OpenAlex

Viral hemorrhagic fever (VHF) is an infectious syndrome in humans often associated with high fatality rates. For most VHFs there are no specific and effective therapies or vaccines available and, in general, there is a lack of knowledge regarding the biology and pathogenesis of the causative agents. Therefore, a more detailed understanding of the molecular basis of VHF pathogenesis, including the identification of viral virulence determinants and host interactions and responses, will be important to enhance our ability to control VHF infections. The recently developed "reverse genetics systems" for several VHF causing viruses have allowed the generation of infectious viruses from cloned cDNA and thus, the generation of virus mutants. Here we review the existing reverse genetics systems for VHF causing viruses and discuss their use in studying viral replication, pathogenesis, and the development of antivirals and vaccines.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.002

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 head0.135
GPT teacher head0.412
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThrombosis and HaemostasisSame topicViral Infections and Outbreaks ResearchFrench-language works237,207