Glioblastoma (GBM) is the most common and aggressive primary brain tumor. GBM contains a small subpopulation of glioma stem cells (GSCs) that are implicated in treatment resistance, tumor infiltration, and recurrence, and are thereby considered important therapeutic targets. Recent clinical studies have suggested that the choice of general anesthetic (GA), particularly propofol, during tumor resection, affects subsequent tumor response to treatments and patient prognosis. In this study, we investigated the molecular mechanisms underlying propofol’s anti-tumor effects on GSCs and their interaction with microglia cells. Propofol exerted a dose-dependent inhibitory effect on the self-renewal, expression of mesenchymal markers, and migration of GSCs and sensitized them to both temozolomide (TMZ) and radiation. At higher concentrations, propofol induced a large degree of cell death, as demonstrated using microfluid chip technology. Propofol increased the expression of the lncRNA BDNF-AS, which acts as a tumor suppressor in GBM, and silencing of this lncRNA partially abrogated propofol’s effects. Propofol also inhibited the pro-tumorigenic GSC-microglia crosstalk via extracellular vesicles (EVs) and delivery of BDNF-AS. In conclusion, propofol exerted anti-tumor effects on GSCs, sensitized these cells to radiation and TMZ, and inhibited their pro-tumorigenic interactions with microglia via transfer of BDNF-AS by EVs.

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.

Monitoring prognostic biomarkers is essential for evaluating cancer treatment efficacy in real time. Here, we present a rapid, proof-of-concept microfluidic assay for cancer biomarker quantification and functional therapy evaluation. The assay requires only 5 µL of sample, eliminates tedious sample manipulation, and uses commercial antibodies to track both biomarker concentration and functional activity. We demonstrated the successful detection of VEGF and EML4-ALK proteins in both lung cancer cell culture supernatant and serum-spiked samples, achieving a detection sensitivity 15 times greater than standard ELISA using the same antibody pairs for the respective antigens. Crucially, we showcase the platform’s distinct applicability to functional assays by monitoring dynamic phosphorylation changes in EML4-ALK following treatment with the tyrosine kinase inhibitor alectinib, successfully capturing a significant, rapid decrease in phosphorylation levels. At this preclinical stage, the platform demonstrates analytical and functional feasibility for highly sensitive and rapid biomarker monitoring, providing a foundation for future validation in patient-derived ALK-positive samples for therapeutic-response assessment.