MétaCan
Menu
Back to cohort
Record W7132942936

Development of Vascularized Heart-on-a-chip System for Studying Immune-mediated Cardiovascular Dysfuncion

2023· dissertation· W7132942936 on OpenAlexfundno aff
Xing Ze Lu

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of TorontoNational Institutes of HealthConnaught FundCalifornia HIV/AIDS Research Program
KeywordsCrosstalkImmune systemCardiac cellAngiogenesisCell typeEndotheliumHomeostasisEndothelial stem cell
DOInot available

Abstract

fetched live from OpenAlex

The heart is one of the most vascularized organs in the body, with an equivalent number of cardiomyocytes and endothelial cells present in the adult myocardium. Given their proximity, the complex network of the vasculature not only helps maintain heart homeostasis and its high-energy demands but also regulates inflammatory equilibrium. However, our inability to capture the daunting complex tissue vascularization and immune responses has prevented us from studying these interactions and has slowed the progress of this field. With advancements in microfluidic technology and the convergence of organ-on-a-chip engineering, engineers have expanded their toolbox to create miniaturized organs that can better recapitulate native biological functions. Within this philosophy, I hypothesize that establishing an endothelial barrier in a vascularized heart-on-a-chip, along with the perfusion of immune cells, will enable the development of a model system that faithfully recapitulates cell-cell interactions, thereby facilitating the investigation of intercellular crosstalk under pathological conditions such as inflammation. In this study, we first designed a three-dimensional (3D) platform, also known as the InVADE system, which supports the combination of dense iPSC-derived cardiac tissue with a vascular interface to unravel the impact of nanoscale air pollution on both endothelial cells and cardiac tissue. Our results suggest the utility of the InVADE system in delineating the complex direct and indirect effects of nanoparticles that underlie the progression of endothelial cell dysfunction upon nanoparticle exposure and the subsequent effects on cardiac tissue functions. Then with the following specific aim, we developed an approach to integrate circulating immune cells into the InVADE platform to provide a physiologically relevant assessment of endothelial and heart functions in the setting of SARS-CoV-2 infection. The introduction of immune cells into the vasculature exacerbates cytokine-induced endothelial cell and heart function upon the SARS-CoV-2 challenge, demonstrating the compounding effects of intercellular crosstalk between endothelial and immune cells in facilitating the hyperinflammatory state. We identified angiopoietin-1 derived peptide (QHREDGS) as a potential therapeutic agent capable of profoundly attenuating the inflammatory state of cells, thereby improving vascular barrier function and heart function against SARS-CoV-2 infection. Finally, we captured the complex cascade of viral infections and the subsequent myocardial inflammation. We demonstrated that PBMCs and CD3+ T-lymphocytes infiltrated the cardiac tissue from the vascular compartment, which led to electromechanical dysfunction of the engineered cardiac construct. Using a myocarditis-on-a-chip model, we found that treatment of HUVEC extracellular vesicles to myocarditis heart mitigate inflammatory responses by suppressing toll-like receptor-mediated NF-kB transcriptional activation, which, in turn, alleviated mitochondrial stress and improved heart function.

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 score0.005

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.053
GPT teacher head0.335
Teacher spread0.281 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTSpaceSame topic3D Printing in Biomedical ResearchFrench-language works237,207