SEARCH · PubMed Health

Results for “3D bioprinting”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

3 recordsLinked to original sources

PubMed2016

A synergistic approach to the design, fabrication and evaluation of 3D printed micro and nano featured scaffolds for vascularized bone tissue repair.

3D bioprinting has begun to show great promise in advancing the development of functional tissue/organ replacements. However, to realize the true potential of 3D bioprinted tissues for clinical use requires the fabrication of an interconnected and effective vascular network. Solving this challenge is critical, as human tissue relies on an adequate network of blood vessels to transport oxygen, nutrients, other chemicals, biological factors and waste, in and out of the tissue. Here, we have successfully designed and printed a series of novel 3D bone scaffolds with both bone formation supporting structures and highly interconnected 3D microvascular mimicking channels, for efficient and enhanced osteogenic bone regeneration as well as vascular cell growth. Using a chemical functionalization process, we have conjugated our samples with nano hydroxyapatite (nHA), for the creation of novel micro and nano featured devices for vascularized bone growth. We evaluated our scaffolds with mechanical testing, hydrodynamic measurements and in vitro human mesenchymal stem cell (hMSC) adhesion (4 h), proliferation (1, 3 and 5 d) and osteogenic differentiation (1, 2 and 3 weeks). These tests confirmed bone-like physical properties and vascular-like flow profiles, as well as demonstrated enhanced hMSC adhesion, proliferation and osteogenic differentiation. Additional in vitro experiments with human umbilical vein endothelial cells also demonstrated improved vascular cell growth, migration and organization on micro-nano featured scaffolds.

Bone Regeneration↗

PubMed2026

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive genomic, cellular, spatial, and microenvironmental heterogeneity. Multi-omics studies have revealed that GBM follows complex evolutionary trajectories involving genetic, epigenetic, transcriptional, and immune-microenvironmental remodeling as tumors grow, adapt to the brain microenvironment, and acquire therapeutic resistance. Increasing evidence suggests that GBM may originate from aberrant neural stem or progenitor cells, including those residing in the subventricular zone, and that glioblastoma stem cells (GSCs) contribute to tumor propagation, heterogeneity, and recurrence. A key conceptual challenge is to reconcile hierarchical cancer stem cell models, in which GSCs are viewed as relatively stable tumor-propagating subpopulations, with dynamic state plasticity models, in which stem-like properties can be reversibly acquired or lost during transitions among proneural-like, mesenchymal-like, invasive, and therapy-tolerant states. Recent advances in single-cell profiling, spatial transcriptomics, lineage tracing, organoid culture, 3D bioprinting, genetically engineered models, and artificial intelligence (AI)-assisted computational modeling have substantially improved the ability to study these processes. However, no currently available model fully recapitulates human GBM heterogeneity, recurrence, treatment history, and tumor-microenvironment interactions. Therefore, model selection should be guided by clearly defined mechanistic questions rather than by reliance on any single platform. This review summarizes current advances in in vitro, ex vivo, in vivo, and computational models for studying GBM evolution and heterogeneity, and discusses how integrated model pipelines may improve preclinical drug testing, treatment-response prediction, and precision neuro-oncology.

Humans↗

PubMed2025

Granular Hydrogels as Brittle Yield Stress Fluids.

While granular hydrogels are increasingly used in biomedical applications, methods to capture their rheological behavior generally consider shear-thinning and self-healing properties or produce ensemble metrics (e.g., dynamic moduli) while neglecting transient yielding and unyielding processes. Combining oscillatory shear testing with Brittility (Bt) via the Kamani-Donley-Rogers (KDR) model, this work shows that granular hydrogels behave as brittle yield stress fluids. This work quantifies steady and transient rheology as a function of microgel properties and granular composition for polyethylene glycol and gelatin microgels. The KDR model with Bt captures granular hydrogel behavior for a wide range of design parameters, reducing the complex rheology to a determination of model parameters. In granular mixtures, this work observes monotonic dependencies of the elastic modulus, structural viscosity, and brittility upon granular composition, while the yield stress is lower for mixtures. Microgel size distribution and polymer fraction are the most influential parameters in monolithic granular hydrogels, while microgel size and packing density are less impactful. The model robustly captures self-healing behavior and reveals that granular hydrogel relaxation accelerates with an increased small-amplitude strain rate. This quantitative framework is an important step toward rational design of granular hydrogels for applications ranging from injection and in situ stabilization to 3D bioprinting.

brittility↗