Evidence map›Paper›PMID 27900388›Full record

ArticleMolecular medicine (Cambridge, Mass.)2017

Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure.

Ruben Zamora, Yoram Vodovotz, Qi Mi, Derek Barclay, Jinling Yin, Simon Horslen, David Rudnick, Kathleen M Loomes, Robert H Squires

Open access · goldAbstract read
In one paragraph

Article in Molecular medicine (Cambridge, Mass.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

20 citing papers in PubMed, 41 citations in OpenAlex.

  1. Article
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  3. Review
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  6. IRF3 Signaling within the Mouse Stroma Influences Sepsis Pathogenesis.Journal of immunology (Baltimore, Md. : 1950) · 2021
    Article
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  10. Observational
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  16. Article
  17. A Learning Collaborative Approach Increases Specificity of Diagnosis of Acute Liver Failure in Pediatric Patients.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2018
    Observational
  18. Observational
  19. Article
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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

9 authors at 5 institutions in 1 country.

Ruben ZamoraDepartment of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
Yoram VodovotzDepartment of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
Qi MiDepartment of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
Derek BarclayDepartment of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
Jinling YinDepartment of Surgery, University of Pittsburgh, Pittsburgh, PA 15213.
Simon HorslenSeattle Children's Hospital, Seattle, WA 98105.
David RudnickSt Louis Children's Hospital, St. Louis, MO 63110.
Kathleen M LoomesChildren's Hospital of Philadelphia, Philadelphia, PA 19104.
Robert H SquiresDepartment of Pediatrics, University of Pittsburgh, Pittsburgh, PA 15213.
University of Pittsburgh · USChildren's Hospital of Philadelphia · USMcGowan Institute for Regenerative Medicine · USSeattle Children's Hospital · USSt. Louis Children's Hospital · US

Funding

Multi-Center Group to Study Acute Liver Failure in ChildU01DK072146 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SQUIRES, ROBERT H · 2005 to 2014
$23.2M
NIDDK NIH HHS U01 DK072146
6 · The paper itself

Abstract

Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.

Indexed as

biomarkercomputational analysisinflammationliver diseasenetworks

Identifiers

PMID27900388
PMCPMC5263056
OpenAlexW2560651831

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.