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Rapidly dividing tumors, embryos, and wounded tissues require more oxygen than usual, lowering the oxygen concentration in the blood. At low oxygen or hypoxic conditions, an oxygen-sensitive transcription factor called the hypoxia-inducible factor 1 or HIF1 is activated. HIF1 is a dimeric protein of alpha (ɑ) and beta (β) subunits.  Under optimal oxygen conditions, HIF1β is present in the nucleus while HIF1ɑ remains in the cytosol. HIF1ɑ is hydroxylated by prolyl...
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Predicted Effects of Patient Variability and Notch Signaling on In Situ Vascular Tissue Engineering.

Jordy G M van Asten1,2, Cecilia M Sahlgren1,2,3, Jay D Humphrey4

  • 1Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.

Annals of Biomedical Engineering
|November 8, 2025
PubMed
Summary
This summary is machine-generated.

Computational models reveal that inflammation, scaffold degradation, and pre-stretch significantly impact tissue-engineered vascular graft (TEVG) variability. Manipulating the Notch pathway shows potential for improving TEVG function, but requires further optimization.

Keywords:
Computational modelingGrowth and remodelingMechanosensingNotch signalingVascular tissue engineering

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Area of Science:

  • Biomedical Engineering
  • Regenerative Medicine
  • Vascular Biology

Background:

  • In situ vascular tissue engineering seeks to create functional blood vessel replacements using biodegradable scaffolds.
  • Tissue-engineered vascular grafts (TEVGs) face challenges with limited functionality, high failure rates, and outcome variability.
  • Current optimization methods are insufficient, and key sources of TEVG variability remain unidentified.

Purpose of the Study:

  • To computationally investigate sources of variability in tissue-engineered vascular graft (TEVG) outcomes.
  • To explore the impact of manipulating the Notch signaling pathway on TEVG development and function.
  • To identify key factors contributing to TEVG outcome variability under patient-specific conditions.

Main Methods:

  • Simulated the evolution of TEVGs from degradable scaffolds using immuno-mechano-mediated growth and remodeling.
  • Incorporated patient-specific conditions and the Notch signaling pathway into the computational model.
  • Analyzed the effects of differential inflammatory production, scaffold degradation, and axial pre-stretch on TEVG outcomes.

Main Results:

  • Differential inflammatory production, scaffold degradation rate, and scaffold axial pre-stretch were identified as major sources of TEVG outcome variability.
  • Immobilizing Jagged ligands to the scaffold did not significantly reduce outcome variability but improved certain aspects of TEVG functionality.
  • The computational model successfully simulated TEVG evolution under various conditions, highlighting the influence of Notch signaling.

Conclusions:

  • The developed computational model can advance TEVG optimization by simulating Notch pathway manipulations under patient-specific conditions.
  • Addressing identified sources of variability, such as inflammation and scaffold properties, is crucial for improving TEVG success rates.
  • Targeting the Notch pathway, potentially in combination with other strategies, offers a promising avenue for enhancing TEVG functionality.