Evidence map›Paper›PMID 31621854›Full record

ArticleThe Journal of infectious diseases2020

Immune Predictors of Mortality After Ribonucleic Acid Virus Infection.

Jessica B Graham, Jessica L Swarts, Vineet D Menachery, Lisa E Gralinski, Alexandra Schäfer, Kenneth S Plante, Clayton R Morrison, Kathleen M Voss, Richard Green, Gabrielle Choonoo and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The Journal of infectious diseases, 2020. 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
–field-weighted citation impact
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.

  1. Animal models of Long Covid: A hit-and-run disease.Science translational medicine · 2024
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
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

20 authors.

Jessica B GrahamVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Jessica L SwartsVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Vineet D MenacheryDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Lisa E GralinskiDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Alexandra SchäferDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Kenneth S PlanteDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Clayton R MorrisonDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Kathleen M VossCenter for Innate Immunity and Immune Disease, Department of Immunology, University of Washington School of Medicine, Seattle, Washington, USA.
Richard GreenCenter for Innate Immunity and Immune Disease, Department of Immunology, University of Washington School of Medicine, Seattle, Washington, USA.
Gabrielle ChoonooDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Sophia JengDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Darla R MillerDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Michael A MooneyDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Shannon K McWeeneyDivision of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA.
Martin T FerrisDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Fernando Pardo-Manuel de VillenaDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Michael GaleCenter for Innate Immunity and Immune Disease, Department of Immunology, University of Washington School of Medicine, Seattle, Washington, USA.
Mark T HeiseDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Ralph S BaricDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Jennifer M LundVaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

Funding

Unlocking Zika Virus Immune Control and Pathogenesis with the Collaborative CrossU19AI100625 · NIAID · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BARIC, RALPH S, HEISE, MARK T · 2012 to 2021
$36.6M
Using the Collaborative Cross to discover immunoregulatory mechanisms that control WNV infectionR21AI145359 · NIAID · UNIVERSITY OF WASHINGTON · PI GALE, MICHAEL · 2019 to 2020
$485k
NIAID NIH HHS R21 AI145359NIAID NIH HHS U19 AI100625
6 · The paper itself

Abstract

backgroundVirus infections result in a range of clinical outcomes for the host, from asymptomatic to severe or even lethal disease. Despite global efforts to prevent and treat virus infections to limit morbidity and mortality, the continued emergence and re-emergence of new outbreaks as well as common infections such as influenza persist as a health threat. Challenges to the prevention of severe disease after virus infection include both a paucity of protective vaccines as well as the early identification of individuals with the highest risk that may require supportive treatment.

methodsWe completed a screen of mice from the Collaborative Cross (CC) that we infected with influenza, severe acute respiratory syndrome-coronavirus, and West Nile virus.

resultsThe CC mice exhibited a range of disease manifestations upon infections, and we used this natural variation to identify strains with mortality after infection and strains exhibiting no mortality. We then used comprehensive preinfection immunophenotyping to identify global baseline immune correlates of protection from mortality to virus infection.

conclusionsThese data suggest that immune phenotypes might be leveraged to identify humans at highest risk of adverse clinical outcomes upon infection, who may most benefit from intensive clinical interventions, in addition to providing insight for rational vaccine design.

Indexed as

MortalityAnimalsCollaborative Cross MiceCytokinesDisease Models, AnimalFemaleHumansInfluenza A virusInfluenza, HumanMaleMiceOrthomyxoviridae InfectionsRNARNA Virus InfectionsSevere Acute Respiratory SyndromeSevere acute respiratory syndrome-related coronavirusCytokinesRNAViral VaccinesCollaborative Crossimmune correlates of mortalityRNA virus infection

Identifiers

PMID31621854
PMCPMC7107456

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.