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In this study, a bio-fabrication method has been developed for the preparation of 3D graphene–alginate composite scaffolds with great potential for neural tissue engineering.

Neural tissue engineering aims to restore the function of nervous system tissues using biocompatible cell-seeded scaffolds. Graphene-based scaffolds combined with stem cells deserve special attention to enhance tissue regeneration in a controlled manner. However, it is believed that minor changes in scaffold biomaterial composition, internal porous structure, and physicochemical properties can impact cellular growth and adhesion. The current work aims to investigate in vitro biological effects of three-dimensional (3D) graphene oxide (GO)/sodium alginate (GOSA) and reduced GOSA (RGOSA) scaffolds on dental pulp stem cells (DPSCs) in terms of cell viability and cytotoxicity. Herein, the effects of the 3D scaffolds, coating conditions, and serum supplementation on DPSCs functions are explored extensively. Biodegradation analysis revealed that the addition of GO enhanced the degradation rate of composite scaffolds. Compared to the 2D surface, the cell viability of 3D scaffolds was higher (p < 0.0001), highlighting the optimal initial cell adhesion to the scaffold surface and cell migration through pores. Moreover, the cytotoxicity study indicated that the incorporation of graphene supported higher DPSCs viability. It is also shown that when the mean pore size of the scaffold increases, DPSCs activity decreases. In terms of coating conditions, poly-L-lysine was the most robust coating reagent that improved cell-scaffold adherence and DPSCs metabolism activity. The cytotoxicity of GO-based scaffolds showed that DPSCs can be seeded in serum-free media without cytotoxic effects. This is critical for human translation as cellular transplants

The effects of passive tilt on the power spectrum of heart rate variability was studied in healthy subjects. A significant decrease from 0.10 to 0.087 Hz of the centre frequency of the low frequency component of the power spectrum was observed with tilt. In order to better understand the nature of this frequency shif, a multivariate model of the effects of respiration and blood pressure on heart rate was applied to data from one individual. The contribution to the LF component of the HRV power spectrum by blood pressure variations was found to be responsible for the frequency shif.

This paper presents an application of a hybrid approach (the genetic algorithms and the k-nearest neighbour) proposed by Ishbuchi to Wisconsin breast cancer data. For the diagnosis of breast cancer, the determination of the presence of benign/malignant breast tumors represents a very complex problem (even for an experienced cytologist). Therefore the automatic classification of benign and malignant symptoms is highly desirable as a valuable aid to assist oncologists in the decision making of the diagnosis of breast cancer. In this paper, the genetic algorithm based k-nearest neighbour method for classification of benign and malignant breast tumors is presented. The genetic-algorithm (GA) is used for finding a compact reference set by selecting a small number of reference patterns from a large number of training patterns in nearest neighbor classification. The GA simultaneously performs feature selection and pattern selection and prunes unnecessary features. The goal is to maximize the classification performance of the reference set and minimize the number of selected patterns and features. Results are also compared with a fuzzy-genetic approach where each reference patten represents a fuzzy if-then rule with a circular-cone-type membership function.