Evidence map›Paper›PMID 35573256›Full record

ArticleFrontiers in bioengineering and biotechnology2022

Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization.

Jason K D Chan, Eric A Chadwick, Daisuke Taniguchi, Mohammadali Ahmadipour, Takaya Suzuki, David Romero, Cristina Amon, Thomas K Waddell, Golnaz Karoubi, Aimy Bazylak

Abstract read
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In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2022. 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. Targeted cell deposition for lung re-endothelialization.Biomechanics and modeling in mechanobiology · 2026
    Article
  2. Article
  3. Article
  4. Review
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

10 authors.

Jason K D ChanDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Eric A ChadwickDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Daisuke TaniguchiLatner Thoracic Surgery Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada.
Mohammadali AhmadipourLatner Thoracic Surgery Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada.
Takaya SuzukiDepartment of Thoracic Surgery, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
David RomeroDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Cristina AmonDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Thomas K WaddellLatner Thoracic Surgery Laboratories, Toronto General Hospital Research Institute, University Health Network, Toronto General Hospital, University of Toronto, Toronto, ON, Canada.
Golnaz KaroubiDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
Aimy BazylakDepartment of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demonstrated a 73% reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments based on common clinical practices. We enhanced the uniformity of cell distributions in the lung vasculature by increasing the injection flow rate from 3.81 ml/min to 9.40 ml/min. As a result, the cell seeding efficiency increased in both the numerical and experimental results by 42 and 66%, respectively.

Indexed as

cell seedingcomputational fluid dynamicsinertialung regenerationlung tissue engineeringre-endothelialization

Identifiers

PMID35573256
PMCPMC9092599

What OpenQuestion holds

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LicenceCC BY
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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.