Evidence map›Paper›PMID 42583021›Full record

ArticlePNAS nexus2026

Computational fluid dynamics enables predictable scale-up of perfusion bioreactors for microvessel production.

Pouyan Vatani, Kasinan Suthiwanich, Zidong Han, David A Romero, Sara S Nunes, Cristina H Amon

Abstract read
In one paragraph

Article in PNAS nexus, 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
–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

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

6 authors.

Pouyan VataniMechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, Canada M5S 3G8.ORCID https://orcid.org/0000-0002-9046-4495
Kasinan SuthiwanichDivision of Experimental Therapeutics, Toronto General Hospital Research Institute, University Health Network, 101 College St, Toronto, ON, Canada M5G 1L7.ORCID https://orcid.org/0000-0003-3653-5050
Zidong HanMechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, Canada M5S 3G8.
David A RomeroMechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, Canada M5S 3G8.ORCID https://orcid.org/0000-0002-3603-7361
Sara S NunesDivision of Experimental Therapeutics, Toronto General Hospital Research Institute, University Health Network, 101 College St, Toronto, ON, Canada M5G 1L7.
Cristina H AmonMechanical & Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, Canada M5S 3G8.ORCID https://orcid.org/0000-0003-4314-8120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scaling up microvessel culture systems is essential for producing clinically relevant vascularized tissues, yet conventional microphysiological platforms offer limited insight into how to maintain flow conditions during scale-up. Here, we present a computational-experimental framework using computational fluid dynamics (CFD) to guide the design and scaling of microvessel bioreactors. Interstitial flow (IF) distributions were predicted in two perfusion-based platforms-a permeable well-plate insert and a rhomboidal microfluidic chamber-across multiple scaling factors and hydrostatic pressures. CFD identified IF ranges conducive to microvessel network formation and quantified how geometry and pressure modulate the flow field. IF-preserving scale-up in permeable well-plate inserts generated microvessel networks with consistent morphology metrics across a more than 30-fold increase in culture volume. In microfluidic rhomboidal chambers, regions with distinct CFD-predicted IF velocity fields showed significant differences in average lumen diameter and total vessel length per area under otherwise matched experimental conditions, supporting an association between local IF environment and regional morphology. Together, these results show that CFD can predict and compare IF environments during scale-up, that preserving IF conditions during scale-up can result in similar morphology metrics, and that IF distributions inside a device can correlate with regional network morphology.

Indexed as

computational fluid dynamicsin silicointerstitial flowtissue engineeringvasculogenesis

Identifiers

PMID42583021
PMCPMC13458951

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.