Evidence map›Paper›PMID 28904202›Full record

ArticleJournal of virology2017

Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity.

Ericka Keef, Li Ang Zhang, David Swigon, Alisa Urbano, G Bard Ermentrout, Michael Matuszewski, Franklin R Toapanta, Ted M Ross, Robert S Parker, Gilles Clermont

Abstract read
In one paragraph

Article in Journal of virology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Epidemic evolutionarily stable strategies within an age-structured host population.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
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  10. 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

10 authors.

Ericka KeefDepartment of Mathematics, Carlow University, Pittsburgh, Pennsylvania, USA edkeef@carlow.edu.
Li Ang ZhangDepartment of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
David SwigonDepartment of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Alisa UrbanoDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
G Bard ErmentroutDepartment of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Michael MatuszewskiDepartment of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Franklin R ToapantaCenter for Vaccine Research, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Ted M RossCenter for Vaccine Research, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Robert S ParkerDepartment of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Gilles ClermontDepartment of Chemical and Petroleum Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Funding

Model-Based Decisions in SepsisR01GM105728 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES · 2014 to 2017
$1.1M
Predictive Biosignatures for Complicated Novel H1N1 InfluenzaR56AI093930 · NIAID · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI CLERMONT, GILLES, ROSS, TED M · 2012 to 2012
$720k
NIAID NIH HHS R56 AI093930NIGMS NIH HHS R01 GM105728
6 · The paper itself

Abstract

Immunosenescence, an age-related decline in immune function, is a major contributor to morbidity and mortality in the elderly. Older hosts exhibit a delayed onset of immunity and prolonged inflammation after an infection, leading to excess damage and a greater likelihood of death. Our study applies a rule-based model to infer which components of the immune response are most changed in an aged host. Two groups of BALB/c mice (aged 12 to 16 weeks and 72 to 76 weeks) were infected with 2 inocula: a survivable dose of 50 PFU and a lethal dose of 500 PFU. Data were measured at 10 points over 19 days in the sublethal case and at 6 points over 7 days in the lethal case, after which all mice had died. Data varied primarily in the onset of immunity, particularly the inflammatory response, which led to a 2-day delay in the clearance of the virus from older hosts in the sublethal cohort. We developed a Boolean model to describe the interactions between the virus and 21 immune components, including cells, chemokines, and cytokines, of innate and adaptive immunity. The model identifies distinct sets of rules for each age group by using Boolean operators to describe the complex series of interactions that activate and deactivate immune components. Our model accurately simulates the immune responses of mice of both ages and with both inocula included in the data (95% accurate for younger mice and 94% accurate for older mice) and shows distinct rule choices for the innate immunity arm of the model between younger and aging mice in response to influenza A virus infection.

Indexed as

Host-Pathogen InteractionsImmunosenescenceModels, ImmunologicalAdaptive ImmunityAge FactorsAnimalsChemokinesCytokinesImmunity, InnateInfluenza A Virus, H1N1 SubtypeLungMiceMice, Inbred BALB COrthomyxoviridae InfectionsSurvival AnalysisChemokinesCytokineshost-pathogen interactionsimmunosenescenceinfluenzamathematical modelingviral clearance

Identifiers

PMID28904202
PMCPMC5686742

What OpenQuestion holds

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Registered trials

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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.