Propofol Inhibits Glioma Stem Cell Growth and Migration and Their Interaction with Microglia via BDNF-AS and Extracellular Vesicles

Propofol Protects the Blood-Brain Barrier After Traumatic Brain Injury by Stabilizing the Extracellular Matrix via Prrx1: From Neuroglioma to Neurotrauma

The purpose of this study is to explore the shared molecular pathogenesis of traumatic brain injury (TBI) and high-grade glioma and investigate the mechanism of propofol (PF) as a potential protective agent. By analyzing the Chinese glioma genome atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases, we compared the transcriptomic data of high-grade glioma and TBI patients to identify common pathological mechanisms. Through bioinformatics analysis, in vitro experiments and in vivo TBI model, we investigated the regulatory effect of PF on extracellular matrix (ECM)-related genes through Prrx1 under oxidative stress. The impact of PF on BBB integrity under oxidative stress was investigated using a dual-layer BBB model, and we explored the protective effect of PF on tight junction proteins and ECM-related genes in mice after TBI. The study found that high-grade glioma and TBI share ECM instability as an important molecular pathological mechanism. PF stabilizes the ECM and protects the BBB by directly binding to Prrx1 or indirectly regulating Prrx1 through miRNAs. In addition, PF reduces intracellular calcium ions and ROS levels under oxidative stress, thereby preserving BBB integrity. In a TBI mouse model, PF protected BBB integrity through up-regulated tight junction proteins and stabilized the expression of ECM-related genes. Our study reveals the shared molecular pathogenesis between TBI and glioblastoma and demonstrate the potential of PF as a protective agent of BBB. This provides new targets and approaches for the development of novel neurotrauma therapeutic drugs.

Cancer Metastasis-on-a-Chip for Modeling Metastatic Cascade and Drug Screening

Microfluidic chips are valuable tools for studying intricate cellular and cell-microenvironment interactions. Traditional in vitro cancer models lack accuracy in mimicking the complexities of in vivo tumor microenvironment. However, cancer-metastasis-on-a-chip (CMoC) models combine the advantages of 3D cultures and microfluidic technology, serving as powerful platforms for exploring cancer mechanisms and facilitating drug screening. These chips are able to compartmentalize the metastatic cascade, deepening the understanding of its underlying mechanisms. This article provides an overview of current CMoC models, focusing on distinctive models that simulate invasion, intravasation, circulation, extravasation, and colonization, and their applications in drug screening. Furthermore, challenges faced by CMoC and microfluidic technologies are discussed, while exploring promising future directions in cancer research. The ongoing development and integration of these models into cancer studies are expected to drive transformative advancements in the field.

Roles of extracellular vesicles in glioblastoma: foes, friends and informers

Glioblastoma (GB) tumors are one of the most insidious cancers which take over the brain and defy therapy. Over time and in response to treatment the tumor and the brain cells in the tumor microenvironment (TME) undergo many genetic/epigenetic driven changes in their phenotypes and this is reflected in the cellular contents within the extracellular vesicles (EVs) they produce. With the result that some EVs try to subdue the tumor (friends of the brain), while others participate in the glioblastoma takeover (foes of the brain) in a dynamic and ever changing process. Monitoring the contents of these EVs in biofluids can inform decisions based on GB status to guide therapeutic intervention. This review covers primarily recent research describing the different cell types in the brain, as well as the tumor cells, which participate in this EV deluge. This includes EVs produced by the tumor which manipulate the transcriptome of normal cells in their environment in support of tumor growth (foes), as well as responses of normal cells which try to restrict tumor growth and invasion, including traveling to cervical lymph nodes to present tumor neo-antigens to dendritic cells (DCs). In addition EVs released by tumors into biofluids can report on the status of living tumor cells via their cargo and thus serving as biomarkers. However, EVs released by tumor cells and their influence on normal cells in the tumor microenvironment is a major factor in immune suppression and coercion of normal brain cells to join the GB "band wagon". Efforts are being made to deploy EVs as therapeutic vehicles for drugs and small inhibitory RNAs. Increasing knowledge about EVs in the TME is being utilized to track tumor progression and response to therapy and even to weaponize EVs to fight the tumor.

Preclinical Models and Technologies in Glioblastoma Research: Evolution, Current State, and Future Avenues

Glioblastoma is the most common malignant primary central nervous system tumor and one of the most debilitating cancers. The prognosis of patients with glioblastoma remains poor, and the management of this tumor, both in its primary and recurrent forms, remains suboptimal. Despite the tremendous efforts that are being put forward by the research community to discover novel efficacious therapeutic agents and modalities, no major paradigm shifts have been established in the field in the last decade. However, this does not mirror the abundance of relevant findings and discoveries made in preclinical glioblastoma research. Hence, developing and utilizing appropriate preclinical models that faithfully recapitulate the characteristics and behavior of human glioblastoma is of utmost importance. Herein, we offer a holistic picture of the evolution of preclinical models of glioblastoma. We further elaborate on the commonly used in vitro and vivo models, delving into their development, favorable characteristics, shortcomings, and areas of potential improvement, which aids researchers in designing future experiments and utilizing the most suitable models. Additionally, this review explores progress in the fields of humanized and immunotolerant mouse models, genetically engineered animal models, 3D in vitro models, and microfluidics and highlights promising avenues for the future of preclinical glioblastoma research.

PTOLEMI: Personalized Cancer Treatment through Machine Learning-Enabled Image Analysis of Microfluidic Assays

Contemporary personalized cancer diagnostic approaches encounter multiple challenges. The presence of cellular and molecular heterogeneity in patient samples introduces complexities to analysis protocols. Conventional analyses are manual, reliant on expert personnel, time-intensive, and financially burdensome. The copious data amassed for subsequent analysis strains the system, obstructing real-time diagnostics at the "point of care" and impeding prompt intervention. This study introduces PTOLEMI: Python-based Tensor Oncological Locator Examining Microfluidic Instruments. PTOLEMI stands out as a specialized system designed for high-throughput image analysis, particularly in the realm of microfluidic assays. Utilizing a blend of machine learning algorithms, PTOLEMI can process large datasets rapidly and with high accuracy, making it feasible for point-of-care diagnostics. Furthermore, its advanced analytics capabilities facilitate a more granular understanding of cellular dynamics, thereby allowing for more targeted and effective treatment options. Leveraging cutting-edge AI algorithms, PTOLEMI rapidly and accurately discriminates between cell viability and distinct cell types within biopsy samples. The diagnostic process becomes automated, swift, precise, and resource-efficient, rendering it well-suited for point-of-care requisites. By employing PTOLEMI alongside a microfluidic cell culture chip, physicians can attain personalized diagnostic and therapeutic insights. This paper elucidates the evolution of PTOLEMI and showcases its prowess in analyzing cancer patient samples within a microfluidic apparatus. While the integration of machine learning tools into biomedical domains is undoubtedly in progress, this study's innovation lies in the fusion of PTOLEMI with a microfluidic platform-an integrated, rapid, and independent framework for personalized drug screening-based clinical decision-making.