Differentiating Engineered Tissue Images and Experimental Factors to Classify Cardiomyocyte Content.

In this study, we used machine learning (ML) to classify the cardiomyocyte (CM) content on day 10 of the differentiation of human-induced pluripotent stem cell (hiPSC)-laden microspheroids using easily acquirable nondestructive phase-contrast images taken in the middle of differentiation and tunable experimental parameters. Scale-up suspension culture, use of engineered tissues to support stem cell differentiation, and CM production for improved control over cellular microenvironment in the suspension system need nondestructive methods to track engineered tissue development. The ability to couple images that capture experimenter perceived "good" or "bad" batches based on visualization at early differentiation time points with actual experimental outcomes in an unbiased way is a step toward building these methods. In recent years, ML techniques have been successfully applied to identify critical process parameters and use this information to build models that describe process outcomes in cell production and hiPSC differentiation. Building upon these successes, here, we utilize convolutional neural networks (CNNs) to build a binary classifier model for CM content on differentiation day 10 (dd10) for hiPSC-CMs. We consider two separate data sets as potential input features for the classification models. The first set includes phase-contrast images of microspheroid tissues taken on days 3 and 5 of the differentiation batches at different experimental conditions. The second set supplements the images with tunable experimental differentiation parameters, such as cell concentration and microspheroids' size. The CM content classes were sufficient and insufficient. The accuracy of the CNN classifier using images only was 63%. The addition of experimental features increased the accuracy to 85%, indicating the importance of tunable parameters in predicting CM content. Impact statement Machine learning approaches were used to predict the final cardiomyocyte (CM) content class (sufficient vs. insufficient) of engineered cardiac tissue microspheroids produced through suspension-based cardiac differentiation of human-induced pluripotent stem cell-laden engineered tissue microspheroids. The models used specified experimental features and data collected using nondestructive inexpensive methods, specifically phase-contrast images taken during the initial days of differentiation as inputs. The best model was a convolutional neural network trained using experimental features and differentiation day 5 images. It classified the CM content with 85% accuracy and replicated and formalized experimenter's visual intuition about differentiation outcomes by incorporating images from early time points.

Human Induced Pluripotent Stem Cell Encapsulation Geometry Impacts Three-Dimensional Developing Human Engineered Cardiac Tissue Functionality.

Cardiac tissue engineering has been working to alleviate the immense burden of cardiovascular disease for several decades. To improve cardiac tissue homogeneity and cardiomyocyte (CM) maturation, in this study, we investigated altering initial encapsulation geometry in a three-dimensional (3D) direct cardiac differentiation platform. Traditional engineered cardiac tissue production utilizes predifferentiated CMs to produce 3D cardiac tissue and often involves various cell selection and exogenous stimulation methods to promote CM maturation. Starting tissue formation directly with human induced pluripotent stem cells (hiPSCs), rather than predifferentiated CMs, simplifies the engineered cardiac tissue formation process, making it more applicable for widespread implementation and scale-up. In this study, hiPSCs were encapsulated in poly (ethylene glycol)-fibrinogen in three tissue geometries (disc-shaped microislands, squares, and rectangles) and subjected to established cardiac differentiation protocols. Resulting 3D engineered cardiac tissues (3D-ECTs) from each geometry displayed similar CM populations (∼65%) and gene expression over time. Notably, rectangular tissues displayed less tissue heterogeneity and suggested more advanced features of maturing CMs, including myofibrillar alignment and Z-line formation. In addition, rectangular tissue showed significantly higher anisotropic contractile properties compared to square and microisland tissues (MI 0.28 ± 0.03, SQ 0.35 ± 0.05, RT 0.79 ± 0.04). This study demonstrates a straightforward method for simplifying and improving 3D-ECT production without the use of exogenous mechanical or electrical pacing and has the potential to be utilized in bioprinting and drug testing applications. Impact statement Current methods for improving cardiac maturation postdifferentiation remain tedious and complex. In this study, we examined the impact of initial encapsulation geometry on improvement of three-dimensional engineered cardiac tissue (3D-ECT) production and postdifferentiation maturation for three tissue geometries, including disc-shaped microislands, squares, and rectangles. Notably, rectangular 3D-ECTs displayed less tissue heterogeneity and more advanced features of maturing cardiomyocytes, including myofibrillar alignment, Z-line formation, and anisotropic contractile properties, compared to microisland and square tissues. This study demonstrates an initial human induced pluripotent stem cell-encapsulated rectangular tissue geometry can improve cardiac maturation, rather than implementing cell selection or tedious postdifferentiation manipulation, including exogenous mechanical and/or electrical pacing.

Prediction of Human Induced Pluripotent Stem Cell Cardiac Differentiation Outcome by Multifactorial Process Modeling.

Human cardiomyocytes (CMs) have potential for use in therapeutic cell therapy and high-throughput drug screening. Because of the inability to expand adult CMs, their large-scale production from human pluripotent stem cells (hPSC) has been suggested. Significant improvements have been made in understanding directed differentiation processes of CMs from hPSCs and their suspension culture-based production at chemically defined conditions. However, optimization experiments are costly, time-consuming, and highly variable, leading to challenges in developing reliable and consistent protocols for the generation of large CM numbers at high purity. This study examined the ability of data-driven modeling with machine learning for identifying key experimental conditions and predicting final CM content using data collected during hPSC-cardiac differentiation in advanced stirred tank bioreactors (STBRs). Through feature selection, we identified process conditions, features, and patterns that are the most influential on and predictive of the CM content at the process endpoint, on differentiation day 10 (dd10). Process-related features were extracted from experimental data collected from 58 differentiation experiments by feature engineering. These features included data continuously collected online by the bioreactor system, such as dissolved oxygen concentration and pH patterns, as well as offline determined data, including the cell density, cell aggregate size, and nutrient concentrations. The selected features were used as inputs to construct models to classify the resulting CM content as being "sufficient" or "insufficient" regarding pre-defined thresholds. The models built using random forests and Gaussian process modeling predicted insufficient CM content for a differentiation process with 90% accuracy and precision on dd7 of the protocol and with 85% accuracy and 82% precision at a substantially earlier stage: dd5. These models provide insight into potential key factors affecting hPSC cardiac differentiation to aid in selecting future experimental conditions and can predict the final CM content at earlier process timepoints, providing cost and time savings. This study suggests that data-driven models and machine learning techniques can be employed using existing data for understanding and improving production of a specific cell type, which is potentially applicable to other lineages and critical for realization of their therapeutic applications.

Polymer/nanodiamond composites - a comprehensive review from synthesis and fabrication to properties and applications.

Nanodiamond (ND) is an allotrope of carbon nanomaterials which exhibits many outstanding physical, mechanical, thermal, optical and biocompatibility characteristics. Meanwhile, ND particles possess unique spherical shape containing diamond-like structure at the core with graphitic carbon outer shell which intuitively contains many oxygen-containing functional groups at the outer surface. Such superior properties and unique structural morphology of NDs are essentially attractive to develop polymer composites with multifunctional properties. However, despite a long history from the discovery of NDs, which is dated back to the1960s, this nanoparticle has been less explored in the field of polymer (nano)composites compared with other carbon nanomaterials, e.g. carbon nanotube (CNT) and graphene. However, open literature indicates that research works in the field of polymer/ND (PND) composites have gained great momentum in the past half a decade. The present article provides a comprehensive review on recent achievements in ND based polymer composites. This review covers a very broad aspect from the synthesis, purification and functionalization of NDs to dispersion, preparation and fabrication of polymer/ND (PND) composites with a look in their recent applications for both structural and functional basis. Therefore, the review would be useful to pave the way for researchers to take some advancing steps in this respect.

Deep sedation in patients undergoing atrioventricular nodal reentry tachycardia ablation.

BACKGROUND: General anesthesia and deep sedation can be used during cardiac EPS to relief pain and provide comfort and immobility, but many electrophysiologists avoid sedation for better arrhythmia induction. OBJECTIVE: To determine anesthesia effects in ablation procedures in adults, we used intravenous anesthetic agents in patients who underwent slow pathway ablation. PATIENTS AND METHODS: One hundred patients who were to undergo radiofrequency catheter ablation were randomly assigned to with and without intravenous anesthesia groups. All patients had palpitation with a documented electrocardiography (ECG) compatible with atrio-ventricular nodal reentrant tachycardia (AVNRT). We used propofol, fentanyl and midazolam for intravenous sedation. Electrophysiological parameters were checked for the two groups and compared before and after the ablation. RESULTS: Electrophysiological parameters were not significantly different in the two groups. In the anesthetic group, patients were more satisfied with the procedure (P value < 0. 001). CONCLUSIONS: Intravenous anesthesia could be done safely in patients who underwent electrophysiological procedures. It had no effect on arrhythmia induction or slow pathway ablation in patients with documented AVNRT.