Diabetes mellitus (DM) affects considerable number of people in the world and the number of cases is increasing every year. Due to a strong link to the genetic basis of the disease, it is extremely difficult to cure. However, it can be controlled to prevent severe consequences, such as organ damage. Therefore, diabetes diagnosis and monitoring of its treatment is very important. In this paper, we have proposed a non-invasive diagnosis support system for DM. The system determines whether or not diabetes is present by determining the cardiac health of a patient using heart rate variability (HRV) analysis. This analysis was based on nine nonlinear features namely: Approximate Entropy (ApEn), largest Lyapunov exponet (LLE), detrended fluctuation analysis (DFA) and recurrence quantification analysis (RQA). Clinically significant measures were used as input to classification algorithms, namely AdaBoost, decision tree (DT), fuzzy Sugeno classifier (FSC), k-nearest neighbor algorithm (k-NN), probabilistic neural network (PNN) and support vector machine (SVM). Ten-fold stratified cross-validation was used to select the best classifier. AdaBoost, with least squares (LS) as weak learner, performed better than the other classifiers, yielding an average accuracy of 90%, sensitivity of 92.5% and specificity of 88.7%.
Since most starting materials for tissue engineering are in powder form, using powder-based additive manufacturing methods is attractive and practical. The principal point of employing additive manufacturing (AM) systems is to fabricate parts with arbitrary geometrical complexity with relatively minimal tooling cost and time. Selective laser sintering (SLS) and inkjet 3D printing (3DP) are two powerful and versatile AM techniques which are applicable to powder-based material systems. Hence, the latest state of knowledge available on the use of AM powder-based techniques in tissue engineering and their effect on mechanical and biological properties of fabricated tissues and scaffolds must be updated. Determining the effective setup of parameters, developing improved biocompatible/bioactive materials, and improving the mechanical/biological properties of laser sintered and 3D printed tissues are the three main concerns which have been investigated in this article.
Haemorrhage remains the leading cause of potentially survivable death in both military and civilian populations. Although a large variety of hemostatic agents have been developed, many of them have an inadequate capacity to induce hemostasis and are not effective in killing bacteria. In recent years, mesoporous bioactive glasses (MBGs) were found to be effective in inducing hemostasis. However, the materials may not be considered as ideal hemostats since they do not offer antimicrobial activity. The gallium ion (Ga+3) not only exhibits antibacterial properties but also accelerates the blood coagulation cascade. The aim of this study was to develop MBGs containing various concentrations of Ga2O3 (1, 2 & 3 mol%) via the evaporation-induced self-assembly (EISA) process and investigate whether the addition of Ga3+ would induce both hemostatic and antibacterial effects. The results indicated that the incorporation of lower Ga2O3 content (1 mol%) into the MBG system improved structural properties including the specific surface area, mesopore size and pore volume as well as the release of silicon and calcium ions. The bioactive glass was found to stimulate blood coagulation, platelet adhesion and thrombus generation and exerted an antibacterial effect against both Escherichia coli and Staphylococcus aureus. Likewise, Ga-doped MBGs showed excellent cytocompatibility even after 3 days, with the 1% Ga2O3-containing MBG attaining the best biocompatibility that render them safe hemostatic agents for stopping bleeding. This study demonstrated that the lowest Ga2O3-substituted MBG can be a potent candidate for controlling haemorrhage and wound infection.