AQSA-Algorithm for Automatic Quantification of Spheres Derived from Cancer Cells in Microfluidic Devices.

Sphere formation assay is an accepted cancer stem cell (CSC) enrichment method. CSCs play a crucial role in chemoresistance and cancer recurrence. Therefore, CSC growth is studied in plates and microdevices to develop prediction chemotherapy assays in cancer.

Detection of disease-specific signatures in B cell repertoires of lymphomas using machine learning.

The classification of B cell lymphomas-mainly based on light microscopy evaluation by a pathologist-requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples-nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)-alongside with 291 control samples.

Reaching the ceiling? Empirical scaling behaviour for deep EEG pathology classification.

Machine learning techniques, particularly deep convolutional neural networks (ConvNets), are increasingly being used to automate clinical EEG analysis, with the potential to reduce the clinical burden and improve patient care. However, further research is required before they can be used in clinical settings, particularly regarding the impact of the number of training samples and model parameters on their testing error. To address this, we present a comprehensive study of the empirical scaling behaviour of ConvNets for EEG pathology classification.

BSEO-Semiautomatic Method for Determination of Oil Recovery with Nanofluids in Microfluidic Devices.

Microfluidic models have become essential instruments for studying enhanced oil recovery techniques through fluid and chemical injection into micromodels to observe interactions with pore structures and resident fluids. The widespread use of cost-effective lab-on-a-chip devices, known for efficient data extraction and minimal reagent usage, has driven demand for efficient data management methods crucial for high-performance data and image analyses. This article introduces a semiautomatic method for calculating oil recovery in polymeric nanofluid flooding experiments based on the background subtraction (BSEO).

Droplets for Gene Editing Using CRISPR-Cas9 and Clonal Selection Improvement Using Hydrogels.

Gene editing tools have triggered a revolutionary transformation in the realms of cellular and molecular physiology, serving as a fundamental cornerstone for the evolution of disease models and assays in cell culture reactions, marked by various enhancements. Concurrently, microfluidics has emerged over recent decades as a versatile technology capable of elevating performance and reducing costs in daily experiments across diverse scientific disciplines, with a pronounced impact on cell biology. The amalgamation of these groundbreaking techniques holds the potential to amplify the generation of stable cell lines and the production of extracellular matrix hydrogels.

Microdevices for cancer stem cell culture as a predictive chemotherapeutic response platform.

Microfluidic platforms for clinical use are a promising translational strategy for cancer research specially for drug screening. Identifying cancer stem cells (CSC) using sphere culture techniques in microfluidic devices (MDs) showed to be better reproducing physiological responses than other in vitro models and allow the optimization of samples and reagents. We evaluated individual sphere proliferation and stemness toward chemotherapeutic treatment (CT) with doxorubicin and cisplatin in bladder cancer cell lines (MB49-I and J82) cultured in MDs used as CSC treatment response platform.

Simple Microcontact Printing Technique to Obtain Cell Patterns by Lithography Using Grayscale, Photopolymer Flexographic Mold, and PDMS.

Microcontact printing using PDMS embossing tools and its variations have aroused the interest of a wide spectrum of research fields, hence the feasibility of defining micro and nanoscale patterns. In this work, we have proposed and demonstrated a novel lithography method based on grayscale patterns printed in a flexographic photopolymer mold and transferred to epoxy resin and a single PDMS stamp to obtain different microprint pattern structures. The geometry of the patterns can be modified by adjusting the layout and grayscale of the stamp patterns.