Evidence map›Paper›PMID 41525405›Full record

ArticlePLoS computational biology2026

Complex relationship among vessel diameter, shear stress and blood pressure controlling vessel pruning during angiogenesis.

Vivek Kumar, Prashant Kumar, Takao Hikita, Mingqian Ding, Yukinori Kametani, Masanori Nakayama, Yosuke Hasegawa

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Vivek KumarCenter for Research on Innovative Simulation Software, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0002-3343-4134
Prashant KumarCenter for Research on Innovative Simulation Software, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.
Takao HikitaOffice of Innovative Medicine, Organization for Research Strategy and Development, Okayama University, Okayama, Japan.
Mingqian DingCenter for Research on Innovative Simulation Software, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0009-0007-2629-5977
Yukinori KametaniDepartment of Mechanical Engineering Informatics, School of Science and Technology, Meiji University, Kanagawa, Japan.
Masanori NakayamaOffice of Innovative Medicine, Organization for Research Strategy and Development, Okayama University, Okayama, Japan.
Yosuke HasegawaCenter for Research on Innovative Simulation Software, Institute of Industrial Science, The University of Tokyo, Tokyo, Japan.ORCID https://orcid.org/0000-0002-1878-972X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood vessel pruning during angiogenesis is the optimization process of the branching pattern to improve the transport properties of a vascular network. Recent studies show that part of endothelial cells (ECs) subjected to lower shear stress migrate toward vessels with higher shear stress in opposition to the blood flow for vessel regression. While dynamic changes of blood flow and local mechano-stress could coordinately modulate EC migration for vessel regression within the closed circulatory system, the effect of complexity of haemodynamic forces and vessel properties on vessel pruning remains elusive. Here, we reconstructed a 3-dimentsional (3D) vessel structure from 2D confocal images of the growing vessels in the mouse retina, and numerically obtained the local information of blood flow, shear stress and blood pressure in the vasculature. Moreover, we developed a predictive model for vessel pruning based on machine learning. We found that the combination of shear stress and blood pressure with vessel radius was tightly correlated to vessel pruning sites. Our results highlighted that orchestrated contribution of local haemodynamic parameters was important for the vessel pruning.

Indexed as

AngiogenesisBlood PressureNeovascularization, PhysiologicRetinal VesselsAnimalsComputational BiologyEndothelial CellsHemodynamicsMachine LearningMiceModels, CardiovascularShear StrengthStress, Mechanical

Identifiers

PMID41525405
PMCPMC13561423

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Registered trials

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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.