Biological properties of copolymer of 2-Hydroxyethyl methacrylate with sulfopropyl methacrylate

Interaction of organism with non-toxic implanted polymers depends on the physicochemical properties of the implant surface, which influence the adsorption of bioactive proteins and subsequently adhesion and growth of cells. The synthetic hydrogels are known as poorly adhesive surfaces. In this study we demonstrated the adsorption of albumin, fibrinogen, fibronectin, basic fibroblast growth factor, heparin-binding epidermal growth factor-like growth factor and epidermal growth factor to poly(2-hydroxyethyl methacrylate) (pHEMA) and copolymer of 2-hydroxyethyl methacrylate (HEMA) and potassium salt of 3-sulfopropyl methacrylate (SPMAK). The adhesion and growth of 3T3 cells and human keratinocytes on surface of these polymers was tested without and with pretreatment of polymers with heparin-binding epidermal growth factor-like growth factor. The adhesion of mixture of human granulocytes and monocytes to these surfaces was also tested. The strips of both polymers were subcutaneously and intracerebrally implanted into the rat and the extent of foreign body reaction and brain biocompatibility was evaluated. The results showed the extensive adsorption of basic fibroblast growth factor and heparin-binding epidermal growth factor-like growth factor to copolymer containing SPMAK. However the adhesion (and growth) of cells to this type of copolymers was very low. Preadsorption of human plasma to pHEMA clearly stimulated the leukocyte adhesion in contrary to copolymer containing SPMAK. The extent of foreign-body reaction was significantly higher against the pHEMA compared to tested copolymer p(HEMA-co-SPMAK). In conclusion, the tested copolymer was a poorly adhesive substrate that is only poorly recognized by the non-specific immunity, although the adsorption of basic growth factors to this substrate is highly significant. Both polymers were well tolerated by the brain tissue. The phenotype of surrounding neurons was more close to the control neurons in the brain tissue surrounding the p(HEMA-co-SPMAK) implants. © 2001 Kluwer Academic Publishers     

Data-Driven Self-Learning Controller for Power-Aware Mobile Monitoring IoT Devices

Nowadays, there is a significant need for maintenance free modern Internet of things (IoT) devices which can monitor an environment. IoT devices such as these are mobile embedded devices which provide data to the internet via Low Power Wide Area Network (LPWAN). LPWAN is a promising communications technology which allows machine to machine (M2M) communication and is suitable for small mobile embedded devices. The paper presents a novel data-driven self-learning (DDSL) controller algorithm which is dedicated to controlling small mobile maintenance-free embedded IoT devices. The DDSL algorithm is based on a modified Q-learning algorithm which allows energy efficient data-driven behavior of mobile embedded IoT devices. The aim of the DDSL algorithm is to dynamically set operation duty cycles according to the estimation of future collected data values, leading to effective operation of power-aware systems. The presented novel solution was tested on a historical data set and compared with a fixed duty cycle reference algorithm. The root mean square error (RMSE) and measurements parameters considered for the DDSL algorithm were compared to a reference algorithm and two independent criteria (the performance score parameter and normalized geometric distance) were used for overall evaluation and comparison. The experiments showed that the novel DDSL method reaches significantly lower RMSE while the number of transmitted data count is less than or equal to the fixed duty cycle algorithm. The overall criteria performance score is 40% higher than the reference algorithm base on static confirmation settings.     

Study of Linkage between Glutathione Pathway and the Antibiotic Resistance of Escherichia coli from Patients’ Swabs

In this work, we focused on the differences between bacterial cultures of E. coli obtained from swabs of infectious wounds of patients compared to laboratory E. coli. In addition, blocking of the protein responsible for the synthesis of glutathione (γ-glutamylcysteine synthase—GCL) using 10 mM buthionine sulfoximine was investigated. Each E. coli showed significant differences in resistance to antibiotics. According to the determined resistance, E. coli were divided into experimental groups based on a statistical evaluation of their properties as more resistant and more sensitive. These groups were also used for finding the differences in a dependence of the glutathione pathway on resistance to antibiotics. More sensitive E. coli showed the same kinetics of glutathione synthesis while blocking GCL (K_m 0.1 µM), as compared to non-blocking. In addition, the most frequent mutations in genes of glutathione synthetase, glutathione peroxidase and glutathione reductase were observed in this group compared to laboratory E.coli. The group of “more resistant” E. coli exhibited differences in K_m between 0.3 and 0.8 µM. The number of mutations compared to the laboratory E. coli was substantially lower compared to the other group.