MétaCan
Menu
Back to cohort

Development of a Functional 3D Bioprinted Vascular Smooth Muscle Tissue Model Using a Stiffness‐Modifiable Alginate‐Collagen‐Fibrinogen Based Bioink

2020· article· en· W3017087640 on OpenAlexaffabout
Sanjana Syeda, Jeffery Osagie, Emily Turner-Brannen, J. Eric Gordon, Adrian R. West

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of Manitoba
Fundersnot available
KeywordsFibrinBiomedical engineeringChemistryExtracellular matrix3D bioprintingFibrinogenTissue engineeringBiophysicsCell biologyMedicineBiologyBiochemistryImmunology

Abstract

fetched live from OpenAlex

Background Blood vessels are soft tissues whose cellular functions are regulated in part by mechanical signals from the extracellular matrix. Structural defects including increased vascular wall stiffness are known contributors to the initiation and progression of diseases like pulmonary hypertension and atherosclerosis. However, studying the effects of wall stiffness using traditional 2 dimensional (2D) cell culture models pose challenges: the flat plastic surface is very stiff and incapable of accurately replicating altered tissue structure. Using 3D bioprinting technology and a stiffness‐modifiable alginate‐collagen‐fibrinogen bioink, we aimed to fabricate functional tissue mimicking the medial smooth muscle layer of healthy and diseased blood vessels. Methods Pulmonary and coronary arterial smooth muscle cells (PASM and CASM respectively) were encapsulated at 2.5x10 7 cells/mL in a bioink comprised of 0.25% to 1.0% w/v sodium alginate, 1 mg/mL collagen‐I, and 5 mg/mL fibrinogen. Tissues were bioprinted with an Aspect Biosystems RX‐1 bioprinter as an 8–10 mm ring, free‐floating or constrained within a stiff (0.75–1.25% alginate) acellular load bearing frame, then treated with thrombin (1.25 U/mL, 30 min) for fibrin polymerisation. To assess tissue integrity and function, tissue compaction was assessed by reduction of lumen area and cell organization was determined using filamentous actin staining. Results Stiff (1% alginate) PASM biorings without a frame were mechanically stable, but cells remained ‘balled up’ and were unable to spread within the structure. Softer (0.25% and 0.5% alginate) PASM biorings showed signs of cell spreading but exhibited excessive compaction (>70% lumen area reduction) within 24 hours. Addition of a 1% alginate frame to PASM biorings reduced compaction to 6.94% (0.25% cellular alginate) and 3.69% (0.5% cellular alginate), while still allowing cells to elongate and form cell‐cell networks. Similar results were observed with CASM biorings, which compacted >50% when printed without a frame. We demonstrated that the degree of tissue compaction is controllable using frames of different stiffnesses; soft (0.375% alginate) CASM biorings printed with a 0.75% alginate frame had >15% compaction, whereas biorings with stiffer 1% and 1.25% alginate frames exhibited <5% compaction. In all cases, cells printed in soft biorings with stiff frames had well‐organised bundles of actin filaments consistent with real vascular smooth muscle. Conclusion Our stiffness‐modifiable 3D bioprinted smooth muscle represents a novel experimental model for studying vascular tissue. The bioink composition and physical design, where muscle compaction can be easily controlled by altering the acellular load bearing frame, allows us to better mimic the structural defects seen in vascular diseases than is possible with 2D models. This makes our model a powerful tool that will enable us to understand how wall stiffness affects the initiation and progression of vascular diseases. Support or Funding Information NSERC Discovery Grant (ARW), Research Manitoba Studentship (SS, JO), CHRIM Operating Grant

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.266
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB JournalSame topic3D Printing in Biomedical ResearchFrench-language works237,207