Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

The development of advanced in vitro systems to replicate vascularized tissue environments is critical for studying cancer progression, immune interactions, and therapeutic responses. Traditional models often lack physiological perfusion, scale flexibility, and compatibility with complex microenvironments, limiting their translational impact. The small Vessel Environment Bioreactor (sVEB) represents a promising advancement in microfluidic and organ-on-chip technologies, enabling the replication of dynamic environments that mimic key features of vascular and tumor biology. Validated through in vitro experiments and computational flow simulations, the sVEB supports vascular network formation, dynamic cell cultures, and tumor-immune interactions. iPSC-derived endothelial cells in the sVEB formed stable perfusable microvessels with secondary branching into the surrounding matrix, while fluidic simulations confirmed laminar flow and shear stress conditions compatible with physiological parameters. In parallel, breast cancer organoids were assembled within the hydrogel compartment surrounding the sVEB and cultured under dynamic flow conditions. Moreover, CD8 T lymphocytes were delivered using a magnetic nanoparticle-based approach, enabling immune-tumor contact within the model. Advancing this technology will require continued efforts on biomaterial development, integration of patient-derived cells, and standardized protocols to ensure scalability and reproducibility, ultimately establishing the sVEB as a versatile platform for precision medicine capable of modeling patient-specific microenvironments to support the discovery of innovative therapeutic approaches.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.biomaterials.2025.123441DOI Listing

Publication Analysis

Top Keywords

breast cancer
8
small vessel
8
vessel environment
8
environment bioreactor
8
bioreactor sveb
8
sveb
6
modeling breast
4
cancer dynamics
4
dynamics modulable
4
modulable small
4

Similar Publications

Purpose: Breast cancer remains a significant public health challenge globally, as well as in India, where it is the most frequently diagnosed cancer in females. Significant disparities in incidence, mortality, and access to health care across India's sociodemographically diverse population highlight the need for increased awareness, policy reform, and research.

Design: This review consolidates data from national cancer registries, global cancer databases, and institutional findings from a tertiary care center to examine the epidemiology, clinical challenges, and management gaps specific to India.

View Article and Find Full Text PDF

ObjectiveTo study the implications of implementing artificial intelligence (AI) as a decision support tool in the Norwegian breast cancer screening program concerning cost-effectiveness and time savings for radiologists.MethodsIn a decision tree model using recent data from AI vendors and the Cancer Registry of Norway, and assuming equal effectiveness of radiologists plus AI compared to standard practice, we simulated costs, effects and radiologist person-years over the next 20 years under different scenarios: 1) Assuming a €1 additional running cost of AI instead of the €3 assumed in the base case, 2) varying the AI-score thresholds for single vs. double readings, 3) varying the consensus and recall rates, and 4) reductions in the interval cancer rate compared to standard practice.

View Article and Find Full Text PDF

Background: Among childhood cancer survivors, germline rare variants in autosomal dominant cancer susceptibility genes (AD CSGs) could increase subsequent neoplasm (SNs) risks, but risks for rarer SNs and by age at onset are not well understood.

Methods: We pooled the Childhood Cancer Survivor Study and St Jude Lifetime Cohort (median follow-up = 29.7 years, range 7.

View Article and Find Full Text PDF

MicroRNAs (miRNAs) are critical regulators of gene expression in cancer biology, yet their spatial dynamics within tumor microenvironments (TMEs) remain underexplored due to technical limitations in current spatial transcriptomics (ST) technologies. To address this gap, we present STmiR, a novel XGBoost-based framework for spatially resolved miRNA activity prediction. STmiR integrates bulk RNA-seq data (TCGA and CCLE) with spatial transcriptomics profiles to model nonlinear miRNA-mRNA interactions, achieving high predictive accuracy (Spearman's ρ > 0.

View Article and Find Full Text PDF

Objective: This study employs integrated network toxicology and molecular docking to investigate the molecular basis underlying 4-nonylphenol (4-NP)-mediated enhancement of breast cancer susceptibility.

Methods: We integrated data from multiple databases, including ChEMBL, STITCH, Swiss Target Prediction, GeneCards, OMIM and TTD. Core compound-disease-associated target genes were identified through Protein-Protein Interaction (PPI) network analysis.

View Article and Find Full Text PDF