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.

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

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.

An Integrated Microfluidics Approach for Personalized Cancer Drug Sensitivity and Resistance Assay.

Cancer is the second leading cause of death globally. Matching proper treatment and dosage is crucial for a positive outcome. Any given drug may affect patients with similar tumors differently. Personalized medicine aims to address this issue. Unfortunately, most cancer samples cannot be expanded in culture, limiting conventional cell-based testing. Herein, presented is a microfluidic device that combines a drug microarray with cell microscopy. The device can perform 512 experiments to test chemosensitivity and resistance to a drug array. MCF7 and 293T cells are cultured inside the device and their chemosensitivity and resistance to docetaxel, applied at various concentrations, are determined. Cell mortality is determined as a function of drug concentration and exposure time. It is found that both cell types form cluster morphology within the device, not evident in conventional tissue culture under similar conditions. Cells inside the clusters are less sensitive to drugs than dispersed cells. These findings support a heterogenous response of cancer cells to drugs. Then demonstrated is the principle of drug microarrays by testing cell response to four different drugs at four different concentrations. This approach may enable the personalization of treatment to the particular tumor and patient and may eventually improve final patient outcome.