Evidence map›Paper›PMID 39858670›Full record

ArticleMicromachines2024

In Vitro Model of Vascular Remodeling Under Microfluidic Perfusion.

Kotaro Nishikata, Kimisato Doi, Nobuyoshi Kaneoya, Masataka Nakamura, Nobuyuki Futai

Abstract read
In one paragraph

Article in Micromachines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

5 authors.

Kotaro NishikataDepartment of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.ORCID 0000-0003-3207-0030
Kimisato DoiDepartment of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.ORCID 0009-0008-7440-6421
Nobuyoshi KaneoyaDepartment of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.ORCID 0009-0008-5043-9173
Masataka NakamuraDepartment of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.ORCID 0000-0001-8848-5388
Nobuyuki FutaiDepartment of Mechanical Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo 135-8548, Japan.ORCID 0000-0001-9341-7304

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed a portable microfluidic system that combines spontaneous lumen formation from human umbilical endothelial cells (HUVECs) in fibrin-collagen hydrogels with active perfusion controlled by a braille actuator. Adaptive interstitial flow and feedthrough perfusion switching enabled the successful culture of spontaneously formed naturally branched lumens for more than one month. We obtained many large-area (2 mm × 3 mm) long-term (more than 30 days per run) time-lapse image datasets of the in vitro luminal network using this microfluidic system. We also developed an automatic image analysis pipeline to extract the morphology of the lumen network and node-edge network structure weighted with segmentwise flow parameters. The automatic lumen area measurements revealed that almost all lumens were successfully cultured in this system for approximately 50 days, following the meshwork, sprouting, remodeling, stability, and erosion stages. We found that the optimization of the lumen network during the remodeling stage can be explained by the decrease in the betweenness centrality of the WSS-weighted network and the increase in the strength centrality of the flow-rate-weighted network.

Indexed as

centralitymicrofluidicremodelingspontaneousvascular network

Identifiers

PMID39858670
PMCPMC11767722

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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.