Evidence map›Paper›PMID 27729864›Full record

ArticleFrontiers in pharmacology2016

Inflammation Following Traumatic Brain Injury in Humans: Insights from Data-Driven and Mechanistic Models into Survival and Death.

Andrew Abboud, Qi Mi, Ava Puccio, David Okonkwo, Marius Buliga, Gregory Constantine, Yoram Vodovotz

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 7% 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

24 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.

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  17. A review of inflammatory mechanism in airway diseases.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2019
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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

7 authors at 4 institutions in 1 country.

Andrew AbboudDepartment of Surgery, University of Pittsburgh Pittsburgh, PA, USA.
Qi MiDepartment of Sports Medicine and Nutrition, University of PittsburghPittsburgh, PA, USA; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of PittsburghPittsburgh, PA, USA.
Ava PuccioDepartment of Neurological Surgery, University of Pittsburgh Pittsburgh, PA, USA.
David OkonkwoDepartment of Neurological Surgery, University of Pittsburgh Pittsburgh, PA, USA.
Marius BuligaDepartment of Mathematics, University of Pittsburgh Bradford, PA, USA.
Gregory ConstantineCenter for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of PittsburghPittsburgh, PA, USA; Department of Mathematics and Department of Statistics, University of PittsburghPittsburgh, PA, USA.
Yoram VodovotzDepartment of Surgery, University of PittsburghPittsburgh, PA, USA; Center for Inflammation and Regenerative Modeling, McGowan Institute for Regenerative Medicine, University of PittsburghPittsburgh, PA, USA.
McGowan Institute for Regenerative Medicine · USUniversity of Pittsburgh · USNeurological Surgery · USUniversity of Pittsburgh at Bradford · US

Funding

Regulation of Intestinal Mucosal Injury & Repair After Trauma/Hemorrhagic ShockP50GM053789 · NIGMS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BILLIAR, TIMOTHY R · 1997 to 2013
$18.0M
NIGMS NIH HHS P50 GM053789
6 · The paper itself

Abstract

Inflammation induced by traumatic brain injury (TBI) is a complex mediator of morbidity and mortality. We have previously demonstrated the utility of both data-driven and mechanistic models in settings of traumatic injury. We hypothesized that differential dynamic inflammation programs characterize TBI survivors vs. non-survivors, and sought to leverage computational modeling to derive novel insights into this life/death bifurcation. Thirteen inflammatory cytokines and chemokines were determined using Luminex™ in serial cerebrospinal fluid (CSF) samples from 31 TBI patients over 5 days. In this cohort, 5 were non-survivors (Glasgow Outcome Scale [GOS] score = 1) and 26 were survivors (GOS > 1). A Pearson correlation analysis of initial injury (Glasgow Coma Scale [GCS]) vs. GOS suggested that survivors and non-survivors had distinct clinical response trajectories to injury. Statistically significant differences in interleukin (IL)-4, IL-5, IL-6, IL-8, IL-13, and tumor necrosis factor-α (TNF-α) were observed between TBI survivors vs. non-survivors over 5 days. Principal Component Analysis and Dynamic Bayesian Network inference suggested differential roles of chemokines, TNF-α, IL-6, and IL-10, based upon which an ordinary differential equation model of TBI was generated. This model was calibrated separately to the time course data of TBI survivors vs. non-survivors as a function of initial GCS. Analysis of parameter values in ensembles of simulations from these models suggested differences in microglial and damage responses in TBI survivors vs. non-survivors. These studies suggest the utility of combined data-driven and mechanistic models in the context of human TBI.

Indexed as

data-driven modelinginflammationinflammatory mediators in CNSmathematical modelingmortalityTBI outcome

Identifiers

PMID27729864
PMCPMC5037938
OpenAlexW2526897305

What OpenQuestion holds

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LicenceCC BY
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.