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Record W2771239011 · doi:10.32396/usurj.v4i1.263

Investigating the molecular basis of rubella virus-induced teratogenesis: a literature review

2017· review· en· W2771239011 on OpenAlexaffvenue
Mariam Goubran

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2017
Typereview
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRubellaZika virusMeaslesRubella virusMicrocephalyCongenital rubella syndromePregnancyDiseaseMedicineVirologyImmunizationMeasles virusVirusIncidence (geometry)ImmunologyPediatricsBiologyVaccinationAntibodyGeneticsPathology

Abstract

fetched live from OpenAlex

Rubella virus (RV) is the etiologic agent of rubella, a disease more commonly known as German measles. The 1940 rubella epidemic in Australia allowed for the identification of RV as a teratogenic agent: infection early in pregnancy causes a variety of birth defects collectively referred to as congenital rubella syndrome (CRS). Although rigorous immunization policies have dramatically reduced the incidence of CRS, it is still estimated that around 100,000 infants are born with CRS every year. Furthermore, in light of the recent Zika virus epidemic which is now known to be a causative agent of microcephaly and other birth defects, a deeper understanding of RV may help elucidate the paradigm of viral teratogenesis and aid in the development of therapeutic agents to prevent the development of birth defects in fetuses after maternal infection. This review aims to give a summary of the current knowledge regarding the molecular biology of the virus followed by an overview of potential mechanisms of RV-induced teratogenesis as well as suggestions for possible future directions for research.

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)0.001
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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.121
GPT teacher head0.387
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2017
Admission routes2
Has abstractyes

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