Establishment of a New Device for Electrical Stimulation of Non-Degenerative Cartilage Cells In Vitro

In cell-based therapies for cartilage lesions, the main problem is still the formation of fibrous cartilage, caused by underlying de-differentiation processes ex vivo. Biophysical stimulation is a promising approach to optimize cell-based procedures and to adapt them more closely to physiological conditions. The occurrence of mechano-electrical transduction phenomena within cartilage tissue is physiological and based on streaming and diffusion potentials. The application of exogenous electric fields can be used to mimic endogenous fields and, thus, support the differentiation of chondrocytes in vitro. For this purpose, we have developed a new device for electrical stimulation of chondrocytes, which operates on the basis of capacitive coupling of alternating electric fields. The reusable and sterilizable stimulation device allows the simultaneous use of 12 cavities with independently applicable fields using only one main supply. The first parameter settings for the stimulation of human non-degenerative chondrocytes, seeded on collagen type I elastin-based scaffolds, were derived from numerical electric field simulations. Our first results suggest that applied alternating electric fields induce chondrogenic re-differentiation at the gene and especially at the protein level of human de-differentiated chondrocytes in a frequency-dependent manner. In future studies, further parameter optimizations will be performed to improve the differentiation capacity of human cartilage cells.     

Prediction of Covid-19 Based on Chest X-Ray Images Using Deep Learning with CNN

The COVID-19 pandemic has caused trouble in people’s daily lives and ruined several economies around the world, killing millions of people thus far. It is essential to screen the affected patients in a timely and cost-effective manner in order to fight this disease. This paper presents the prediction of COVID-19 with Chest X-Ray images, and the implementation of an image processing system operated using deep learning and neural networks. In this paper, a Deep Learning, Machine Learning, and Convolutional Neural Network-based approach for predicting Covid-19 positive and normal patients using Chest X-Ray pictures is proposed. In this study, machine learning tools such as TensorFlow were used for building and training neural nets. Scikit-learn was used for machine learning from end to end. Various deep learning features are used, such as Conv2D, Dense Net, Dropout, Maxpooling2D for creating the model. The proposed approach had a classification accuracy of 96.43 percent and a validation accuracy of 98.33 percent after training and testing the X-Ray pictures. Finally, a web application has been developed for general users, which will detect chest x-ray images either as covid or normal. A GUI application for the Covid prediction framework was run. A chest X-ray image can be browsed and fed into the program by medical personnel or the general public.     

Revealing molecular-level surface structure of amyloid fibrils in liquid by means of frequency modulation atomic force microscopy

We have investigated the surface structure of islet amyloid polypeptide (IAPP) fibrils and α-synuclein protofibrils in liquid by means of frequency modulation atomic force microscopy (FM-AFM). ?ngstr?m-resolution FM-AFM imaging of isolated macromolecules in liquid is demonstrated for the first time. Individual β-strands aligned perpendicular to the fibril axis with a spacing of 0.5?nm are resolved in FM-AFM images, which confirms cross-β structure of IAPP fibrils in real space. FM-AFM images also reveal the existence of 4?nm periodic domains along the axis of IAPP fibrils. Stripe features with 0.5?nm spacing are also found in images of α-synuclein protofibrils. However, in contrast to the case for IAPP fibrils, the stripes are oriented 30° from the axis, suggesting the possibility of β-strand alignment in protofibrils different from that in mature fibrils or the regular arrangement of thioflavin T molecules present during the fibril preparation aligned at the surface of the protofibrils.     

Selective experimental parameters for preparative TREF of propylene-ethylene random copolymers: Limiting undercooling of PP

Temperature rising elution fractionation (TREF) is a highly valuable and widely used tool for chemical composition separation of semicrystalline polyolefins. However, TREF operational conditions should be carefully selected due to their high impact on separation quality. This is especially true for polypropylene (PP)-based polymers due to their slow crystallizing nature and defect distribution, which can further complicate the fractionation. In order to more fully understand this behavior, a systematic investigation was designed to determine the influence of support and cooling rate on preparative TREF of PP random copolymers. Possible measures to limit undercooling of PP and thus preventing so called co-elution of different microstructures in TREF were demonstrated using glass beads as support during fractionation and cooling rate of 0.02°C/min. Support presence during fractionation acted as a nucleating agent to limit the elution of long isotactic chains in the low temperature fractions. Slow cooling rate along with support presence further improved the separation quality by giving the polymer chains enough time to overcome the undercooling.     

Zeitverhalten der isothermen Reaktivdestillation beim thermischen Cracken und Desoxygenieren von Rapsöl

Thermal cracking of rapeseed oil under isothermal reactive distillation conditions allows the production of alternative liquid fuels. Temporal changes of the sump phase and the oil condensate show an increased higher heating value due to deoxygenation. The sump phase also shows an increasing thermal stability, accompanied by polymerization and aromatization. This is derived from a changing iodine value, H/C ratio as well as viscosity. The deoxygenation of the oil condensate is confirmed by a decreasing acid value and the reduction of detected carboxylic acids.     

Saccharomyces cerevisiae Gle2/Rae1 is involved in septin organization,essential for cell cycle progression

Gle2/Rae1 is highly conserved from yeast to humans and has been described as an mRNA export factor. Additionally, it is implicated in the anaphase‐promoting complex‐mediated cell cycle regulation in higher eukaryotes. Here we identify an involvement for Saccharomyces cerevisiae Gle2 in septin organization, which is crucial for cell cycle progression and cell division. Gle2 genetically and physically interacts with components of the septin ring. Importantly, deletion of GLE2 leads to elongated buds, severe defects in septin‐assembly and their cellular mislocalization. Septin‐ring formation is triggered by the septin‐regulating GTPase Cdc42, which establishes and maintains cell polarity. Additionally, activity of the master cell cycle regulator Cdc28 (Cdk1) is needed, which is, besides other functions, also required for G₂/M‐transition, and in yeast particularly responsible for initiating the apical–isotropic switch. We show genetic and physical interactions of Gle2 with both Cdc42 and Cdc28. Most importantly, we find that gle2? severely mislocalizes Cdc42, leading to defects in septin‐complex formation and cell division. Thus, our findings suggest that Gle2 participates in the efficient organization of the septin assembly network, where it might act as a scaffold protein. © 2017 The Authors. Yeast published by John Wiley & Sons, Ltd.     

Explanation for the mechanical strength of amyloid fibrils

The presence of “proteinaceous β-sheet rich fibrillar structures” and amyloidogenic material, has been alluded to extensively in the literature, in association with natural materials exhibiting superior mechanical strength per unit volume. Here we provide a clear experimental demonstration and explanation for why individual amyloid quaternary structures themselves have beneficial mechanical characteristics.