No abstract

No abstract

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, timeconsuming, 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

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.

Unsaturated polyester/styrene (UP) resin was filled with nanodiamonds (NDs) containing carboxyl and methacrylate functionalities using mechanical mixing. Field emission SEM exhibited a uniform dispersion of tightly bound aggregates of nanosized spherical NDs with good interfacial interaction. Rheological measurements exhibited a step increment in the shear viscosity of a UP/ND suspension at 0.6 wt% ND resembling a percolation state at this loading. Shear viscosity data supported by dynamic mechanical analysis results suggested the development of effective ND particles in which ND aggregates were covered by only polyester macromolecules. Accordingly, the morphology of UP/ND composites approached a quasi‐percolation state at 0.6 wt% in which effective ND particles were connected thoroughly, instead of direct ND−ND contact, forming a co‐continuous polyester phase covering the ND particles. Based on such morphology, DSC and Fourier transform infrared analysis suggested the development of heterogeneous microgels in cured UP resin containing NDs which in turn governed the overall mechanical properties of the composites. © 2017 Society of Chemical Industry