Evidence map›Paper›PMID 24842079›Full record

ReviewCurrent opinion in virology2014

Systems approaches to Coronavirus pathogenesis.

Alexandra Schäfer, Ralph S Baric, Martin T Ferris

Open access · greenAbstract readReview
In one paragraph

Review in Current opinion in virology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
0.7field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Animal models of Long Covid: A hit-and-run disease.Science translational medicine · 2024
    Review
  2. Article
  3. Article
  4. Article
  5. SARS-CoV-2 and COVID-19: A genetic, epidemiological, and evolutionary perspective.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020
    Review
  6. Article
  7. Article
  8. Update on Human Rhinovirus and Coronavirus Infections.Seminars in respiratory and critical care medicine · 2016
    Review
  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Alexandra SchäferDepartment of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, United States.
Ralph S BaricDepartment of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, United States.
Martin T FerrisDepartment of Genetics, School of Medicine, University of North Carolina at Chapel Hill, United States. Electronic address: mtferris@unc.edu.
University of North Carolina at Chapel Hill · US

Funding

Unlocking Zika Virus Immune Control and Pathogenesis with the Collaborative CrossU19AI100625 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI HEISE, MARK T · 2012 to 2021
$36.6M
Intramural NIH HHSNIAID NIH HHS U19 AI100625NIAID NIH HHS U19AI100625
6 · The paper itself

Abstract

Coronaviruses comprise a large group of emergent human and animal pathogens, including the highly pathogenic SARS-CoV and MERS-CoV strains that cause significant morbidity and mortality in infected individuals, especially the elderly. As emergent viruses may cause episodic outbreaks of disease over time, human samples are limited. Systems biology and genetic technologies maximize opportunities for identifying critical host and viral genetic factors that regulate susceptibility and virus-induced disease severity. These approaches provide discovery platforms that highlight and allow targeted confirmation of critical targets for prophylactics and therapeutics, especially critical in an outbreak setting. Although poorly understood, it has long been recognized that host regulation of virus-associated disease severity is multigenic. The advent of systems genetic and biology resources provides new opportunities for deconvoluting the complex genetic interactions and expression networks that regulate pathogenic or protective host response patterns following virus infection. Using SARS-CoV as a model, dynamic transcriptional network changes and disease-associated phenotypes have been identified in different genetic backgrounds, leading to the promise of population-wide discovery of the underpinnings of Coronavirus pathogenesis.

Indexed as

Host-Pathogen InteractionsSystems BiologyAnimalsCoronavirusCoronavirus InfectionsGeneticsGenetic TechniquesHumans

Identifiers

PMID24842079
PMCPMC4076299
OpenAlexW2080804122

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.