Evidence map›Paper›PMID 32598356›Full record

ArticlePLoS computational biology2020

Resilience of three-dimensional sinusoidal networks in liver tissue.

Jens Karschau, André Scholich, Jonathan Wise, Hernán Morales-Navarrete, Yannis Kalaidzidis, Marino Zerial, Benjamin M Friedrich

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.3field-weighted citation impact, top 22% 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

7 citing papers in PubMed, 18 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Tryptophan Prevents the Development of Non-Alcoholic Fatty Liver Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023
    Article
  5. Article
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  7. 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 2 countries.

Jens Karschaucfaed, TU Dresden, Dresden, Germany.ORCID 0000-0002-6407-6667
André ScholichMax Planck Institute for the Physics of Complex Systems, Dresden, Germany.ORCID 0000-0002-9393-5459
Jonathan Wisecfaed, TU Dresden, Dresden, Germany.
Hernán Morales-NavarreteMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.ORCID 0000-0002-9578-2556
Yannis KalaidzidisMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.ORCID 0000-0002-6137-1193
Marino ZerialMax Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Benjamin M Friedrichcfaed, TU Dresden, Dresden, Germany.ORCID 0000-0002-9742-6555
Max Planck Institute of Molecular Cell Biology and Genetics · DETechnische Universität Dresden · DECentre National de la Recherche Scientifique · FRMax Planck Institute for the Physics of Complex Systems · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Can three-dimensional, microvasculature networks still ensure blood supply if individual links fail? We address this question in the sinusoidal network, a plexus-like microvasculature network, which transports nutrient-rich blood to every hepatocyte in liver tissue, by building on recent advances in high-resolution imaging and digital reconstruction of adult mice liver tissue. We find that the topology of the three-dimensional sinusoidal network reflects its two design requirements of a space-filling network that connects all hepatocytes, while using shortest transport routes: sinusoidal networks are sub-graphs of the Delaunay graph of their set of branching points, and also contain the corresponding minimum spanning tree, both to good approximation. To overcome the spatial limitations of experimental samples and generate arbitrarily-sized networks, we developed a network generation algorithm that reproduces the statistical features of 0.3-mm-sized samples of sinusoidal networks, using multi-objective optimization for node degree and edge length distribution. Nematic order in these simulated networks implies anisotropic transport properties, characterized by an empirical linear relation between a nematic order parameter and the anisotropy of the permeability tensor. Under the assumption that all sinusoid tubes have a constant and equal flow resistance, we predict that the distribution of currents in the network is very inhomogeneous, with a small number of edges carrying a substantial part of the flow-a feature known for hierarchical networks, but unexpected for plexus-like networks. We quantify network resilience in terms of a permeability-at-risk, i.e., permeability as function of the fraction of removed edges. We find that sinusoidal networks are resilient to random removal of edges, but vulnerable to the removal of high-current edges. Our findings suggest the existence of a mechanism counteracting flow inhomogeneity to balance metabolic load on the liver.

Indexed as

Models, BiologicalHumansLiverMicrovessels

Identifiers

PMID32598356
PMCPMC7351228
OpenAlexW3037432527

What OpenQuestion holds

Textmetadata
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.