Evidence map›Paper›PMID 37544654›Full record

ReviewWIREs mechanisms of disease

Mathematical models of cystic fibrosis as a systemic disease.

Daniel V Olivença, Jacob D Davis, Carla M Kumbale, Conan Y Zhao, Samuel P Brown, Nael A McCarty, Eberhard O Voit

Open access · greenAbstract readReview
In one paragraph

Review in WIREs mechanisms of disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.3field-weighted citation impact, top 18% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

7 authors at 4 institutions in 1 country.

Daniel V OlivençaCenter for Engineering Innovation, The University of Texas at Dallas, Richardson, Texas, USA.
Jacob D DavisDepartment of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Carla M KumbaleDepartment of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Conan Y ZhaoMayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, Minnesota, USA.
Samuel P BrownDepartment of Biological Sciences, Georgia Tech and Emory University, Atlanta, Georgia, USA.
Nael A McCartyDepartment of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA.
Eberhard O VoitDepartment of Biomedical Engineering, Georgia Tech and Emory University, Atlanta, Georgia, USA.ORCID 0000-0003-1378-3043
Georgia Institute of Technology · USEmory University · USMayo Clinic · USThe University of Texas at Dallas · US

Funding

Building re-usable phage and antibiotic treatments via exploitation of bacteria-phage co-evolutionary dynamicsR21AI156817 · NIAID · GEORGIA INSTITUTE OF TECHNOLOGY · PI BROWN, SAMUEL PAUL · 2021 to 2022
$434k
CDC HHS 75D30120C-09782NIAID NIH HHS R21 AI156817
6 · The paper itself

Abstract

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Indexed as

Cystic FibrosisCystic Fibrosis Transmembrane Conductance RegulatorDisease ProgressionHumansLungModels, TheoreticalCystic Fibrosis Transmembrane Conductance Regulatorcystic fibrosismathematical modelsystems biology

Identifiers

PMID37544654
PMCPMC10843793
OpenAlexW4385613165

What OpenQuestion holds

Textmetadata
LicenceTDM
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.