Evidence map›Paper›PMID 25717396›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2014

Heterogeneity within and across Pediatric Pulmonary Infections: From Bipartite Networks to At-Risk Subphenotypes.

Suresh K Bhavnani, Bryant Dang, Maria Caro, Gowtham Bellala, Shyam Visweswaran, Asuncion Mejias, Rohit Divekar

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Suresh K BhavnaniInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Bryant DangInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Maria CaroInst. for Translational Sciences, Inst. for Human Infections and Immunity, Univ. of Texas Medical Branch, Galveston, TX.
Gowtham BellalaHewlett Packard Laboratories, Palo Alto, CA.
Shyam VisweswaranDepartment of Biomedical Informatics, Univ. of Pittsburgh, Pittsburgh, PA.
Asuncion MejiasDiv. of Pediatric Infectious Diseases, Ohio State University, Columbus, OH.
Rohit DivekarDivision of Allergic Diseases, Mayo Clinic, Rochester, MN.

Funding

UTMB Clinical and Translational Science AwardUL1TR000071 · NCATS · UNIVERSITY OF TEXAS MED BR GALVESTON · PI BRASIER, ALLAN R. · 2012 to 2014
$14.4M
NCATS NIH HHS UL1 TR000071
6 · The paper itself

Abstract

Although influenza (flu) and respiratory syncytial virus (RSV) infections are extremely common in children under two years and resolve naturally, a subset develop severe disease resulting in hospitalization despite having no identifiable clinical risk factors. However, little is known about inherent host-specific genetic and immune mechanisms in this at-risk subpopulation. We therefore conducted a secondary analysis of statistically significant, differentially-expressed genes from a whole genome-wide case-control study of children less than two years of age hospitalized with flu or RSV, through the use of bipartite networks. The analysis revealed three clusters of cases common to both types of infection: core cases with high expression of genes in the network core implicated in hyperimmune responsiveness; periphery cases with lower expression of the same set of genes indicating medium-responsiveness; and control-like cases with a gene signature resembling that of the controls, indicating normal-responsiveness. These results provide testable hypotheses for at-risk subphenotypes and their respective pathophysiologies in both types of infections. We conclude by discussing alternate hypotheses for the results, and insights about how bipartite networks of gene expression across multiple phenotypes can help to identify complex patterns related to subphenotypes, with the translational goal of identifying targeted therapeutics.

Identifiers

PMID25717396
PMCPMC4333711

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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.