ReviewScience advances2026
Engineering neurovascular thrombosis: Light-based bioprinting for patient-specific modeling and women's cerebrovascular health.
Review in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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.
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0 citing papers in PubMed.
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebral venous sinus thrombosis (CVST) and carotid web (CW) are distinct cerebrovascular disorders where vascular geometry, disturbed hemodynamics, and endothelial activation interact to drive thrombosis. Both conditions disproportionately affect women, yet remain understudied because of the inability of traditional platforms to replicate complex three-dimensional lumen profiles. This perspective positions light-based lithography as a powerful biofabrication strategy for patient-specific modeling. Digital light processing and volumetric bioprinting render enclosed, perfusable hydrogel networks with high-fidelity surface topography. When integrated into dynamic vessel-on-chip perfusion loops guided by clinical imaging and computational fluid dynamics, these platforms simulate pathological flow stasis and recirculation zones, heterogeneous shear-stress gradients, and site-specific cellular aggregation. This biofabrication approach enables personalized disease modeling and translationally relevant risk assessment, providing a critical platform for advancing women's cerebrovascular health.
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Registered trials
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 OpenQuestion graph.