Vascularized Biomaterial Constructs and Computational Network Models: From Microvascular Fragment Self-Assembly to AI-Powered Clinical Translation

Gabriel Gruionu1, Lucian G. Gruionu2

1Clinical and Translational Science Institute | Indiana University, Indianapolis, IN, USA; ggruionu@iu.edu

2Department of Mechanical Engineering, University of Craiova, Craiova, Romania

Introduction. Functional integration of implanted biomaterials requires the establishment of a sustained, perfusion-competent microcirculation at the implant interface — a challenge central to tissue engineering and regenerative medicine. This work synthesizes three interconnected research programs addressing vascularized construct design, tumor microenvironment modeling, and AI-driven clinical hemodynamic analysis, each grounded in fundamental principles of microvascular network self-assembly and structural adaptation. 

Experimental. Microvascular constructs (MVCs) of isolated microvessel fragments suspended in type I collagen gels were implanted around expanded polytetrafluoroethylene (ePTFE) to evaluate peri-implant vascularization and macrophage activation. Polydimethylsiloxane (PDMS) tissue isolation chambers (TICs) were fabricated and implanted in murine dorsal and cranial window models to enable longitudinal intravital imaging of angiogenesis and collagen remodeling. Patient-derived pancreatic tumor explants were maintained ex vivo within vascularized stromal support tissue for drug sensitivity testing. For clinical translation, preintervention coronary angiograms from 64 STEMI patients were analyzed using a YOLOv8based deep learning classifier, combined with patient-specific Navier–Stokes hemodynamic simulations incorporating collateral vessel network topology. 

Results and Discussion. MVCs maintained perfusion-competent vessel densities comparable to native granulation tissue around ePTFE while significantly reducing activated macrophage density, demonstrating simultaneous pro-vascular and immunomodulatory bioactivity [1]. TICs enabled visualization of angiogenic contact guidance along collagen fibers and recapitulated tumor-associated collagen signatures including TACS-3 configurations associated with poor prognosis in human malignancy [2]. Vascularized stromal support tissue preserved explant histoarchitecture and differential chemotherapeutic sensitivity in patient-derived pancreatic tumors [3]. Deep learning analysis of pre-intervention coronary angiograms achieved 90.2% classification accuracy (AUC = 0.94) for intramyocardial hemorrhage prediction; hemodynamic simulations demonstrated that collateral network topology attenuates distal pressure loading during reperfusion, linking coronary vascular architecture to microvascular injury risk [4].

Conclusions. These findings articulate a unified translational framework — from microvascular self-assembly principles applied to biomaterial integration and tumor microenvironment modeling, to AI-driven computational analysis of coronary vascular networks in acute cardiovascular disease — demonstrating that structural principles governing microvascular network formation have direct and measurable consequences at the clinical scale. 

References. 

[1] Gruionu, G.; Stone, A.L.; Schwartz, M.A.; Hoying, J.B.; Williams, S.K. Encapsulation of ePTFE in prevascularized collagen leads to peri-implant vascularization with reduced inflammation. J. Biomed. Mater. Res. A 2010, 95, 811–818. 

[2] Gruionu, G.; Bazou, D.; Maimon, N.; Onita-Lenco, M.; Gruionu, L.G.; Huang, P.; Munn, L.L. Implantable tissue isolation chambers for deconvolving angiogenesis in vivo. Lab Chip 2016, 16, 1840–1851. 

[3] Bazou, D.; Maimon, N.; Gruionu, G.; Grahovac, J.; Seano, G.; Liu, H.; Evans, C.L.; Munn, L.L. Vascular beds maintain pancreatic tumour explants for ex vivo drug screening. J. Tissue Eng. Regen. Med. 2017. 

[4] Gruionu, G.; Gruionu, L.G.; Vora, K.P.; Youssef, K.; Dharmakumar, R. Patient-specific topological and hemodynamic analysis of coronary vasculature for assessing intramyocardial hemorrhage risk. J. Cardiovasc. Magn. Reson. 2025, 27.