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The 3D hydrogel-based tumor model demonstrates significant potential in replicating the physiological characteristics oftumor environments for mechanistic studies and drug testing. However, the challenge persists in accurately mimicking a vascularized microtumor with a compartmentalized structure in a controlled, heterogeneous, and high-throughput manner. This study introduces a vascularized 3D tumor model that incorporates an endothelial cell (EC) barrier, created by encapsulating glioma cells and human umbilical vein endothelial cells (HUVECs) within the core (6% gelatin) and shell (10% GelMa) of core-shell microbeads, respectively. Upon culture, the tumor cells develop spheroids within the liquid core, while the HUVECs in the shell migrate and adhere to the GelMa surface, ultimately forming an EC barrier. This 3D microengineered tumor model exhibits angiogenesis in solid tumor spheroids, effectively mirroring thestructure and providing relevant biochemical and biophysical properties. Notably, in comparison to 2D cell cultures, the vascularized tumor model shows significantly higher half-maximal inhibitory concentrations for the anticancer drug doxorubicin. Collectively, these findings highlight the considerable potential of engineered 3D tumor models in drug testing.
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http://dx.doi.org/10.1088/1758-5090/adebb5 | DOI Listing |
Cell Physiol Biochem
September 2025
Department of General Practice, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China, E-Mail:
Background/aims: Ubiquitin D (UBD), a member of the ubiquitin-like modifier (UBL) family, is significantly overexpressed in various cancers and is positively correlated with tumor progression. However, the role and underlying mechanisms of UBD in rheumatoid arthritis (RA) remain poorly understood. This study aimed to investigate the effects of UBD knockdown on the progression of RA.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Pathology, First Clinical College, Changzhi Medical College, Changzhi 046000.
Objectives: Acute lung injury (ALI) is an acute respiratory failure syndrome characterized by impaired gas exchange. Due to the lack of effective targeted drugs, it is associated with high mortality and poor prognosis. (TW) has demonstrated anti-inflammatory activity in the treatment of various diseases.
View Article and Find Full Text PDFZhong Nan Da Xue Xue Bao Yi Xue Ban
May 2025
Department of Geriatric Pulmonary and Critical Care Medicine, Xiangya Hospital, Central South University; National Clinical Research Center for Geriatric Disorders (Xiangya Hospital), Changsha 410008.
Objectives: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the and genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility.
View Article and Find Full Text PDFBMB Rep
September 2025
Basic Research Laboratory, Department of Physiology, College of Medicine, Smart Marine Therapeutic Center, Cardiovascular and Metabolic Disease Core Research Center, Inje University, Busan 47392, Korea; Department of Health Science and Technology, College of Medicine, Inje University, Busan 47392, K
Patients with multiple myeloma develop resistance to thalidomide during therapy, and the mechanisms to counteract thalidomide resistance remain elusive. Here, we explored the interaction between cereblon and mitochondrial function to mitigate thalidomide resistance in multiple myeloma. Measurements of cell viability, ATP production, mitochondrial membrane potential, mitochondrial ROS, and protein expression via western blotting were conducted in vitro using KSM20 and KMS26 cells to assess the impact of thalidomide on multiple myeloma.
View Article and Find Full Text PDFProteomics Clin Appl
September 2025
AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.
Background: Endometrial carcinoma (EC) represents a significant clinical challenge due to its pronounced molecular heterogeneity, directly influencing prognosis and therapeutic responses. Accurate classification of molecular subtypes (CNV-high, CNV-low, MSI-H, POLE) and precise tumor mutational burden (TMB) assessment is crucial for guiding personalized therapeutic interventions. Integrating proteomics data with advanced machine learning (ML) techniques offers a promising strategy for achieving precise, clinically actionable classification and biomarker discovery in EC.
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