Two-step sinter-crystallization of K2O–CaO–P2O5–SiO2 (45S5-K) bioactive glass

Bioactive glasses and glass-ceramics (GCs) effectively regenerate bone tissue, however most GCs show improved mechanical properties. In this work, we developed and tested a rarely studied bioactive glass composition (24.4K₂O-26.9CaO-46.1SiO₂-2.6P₂O₅ mol%, identified as 45S5-K) with different particle sizes and heating rates to obtain a sintered GC that combines good fracture strength, low elastic modulus, and bioactivity. We analyzed the influence of the sintering processing conditions in the elastic modulus, Vickers microhardness, density, and crystal phase formation in the GC. The best GC shows improved properties compared with its parent glass. This glass achieves a good densification degree with a two-step viscous flow sintering approach and the resulting GC shows as high bioactivity as that of the standard 45S5 Bioglass®. Furthermore, the GC elastic modulus (56 GPa) is relatively low, minimizing stress shielding. Therefore, we unveiled the glass sintering behavior with concurrent crystallization of this complex bioactive glass composition and developed a potential GC for bone regeneration.     

Nanocomposite powders of hydroxyapatite-graphene oxide for biological applications

The repair of bone fractures is a clinical challenge for patients with impaired healing, such as osteoporosis. Currently, different strategies have been developed to design new biomaterials, enhancing their interactions with biological systems and conducting the cellular behavior in the desired direction to help fracture healing. In the present work, hydroxyapatite-graphene oxide (HA-GO) nanocomposites were produced and the morphological and physicochemical influences of the addition of 0.5 wt%, 1.0 wt% and 1.5 wt% of GO to HA were observed. FEG-SEM and TEM analyses of HA-GO nanocomposites showed HA nanoparticles adhered to the surface of the GO sheets, suggesting an effective method to form nanostructured graphene-based biomaterials. As confirmation, physicochemical analyses by Raman, FTIR and TGA demonstrated a strong affinity between HA and GO, according to the increase of concentration from 0.5 wt% to 1.5 wt% GO in the HA-GO nanocomposites. Also, in order to evaluate the HA-GO nanocomposites behavior under biological microenvironment, in vitro bioactivity and indirect cytotoxicity tests were performed. FEG-SEM analyses confirmed the positive results for the bioactivity properties of HA-GO nanocomposite and indirect cytotoxicity demonstrated that even with a decrease in the hDPSCs viability and proliferation, when increasing to 1.5 wt% of GO concentration, high level of cell viability was exhibited by HA-GO nanocomposites. These biological results suggested the 0.5 wt% HA-GO nanocomposite as a potential bioactive bone graft and a promising biomaterial for bone tissue regeneration, when compared to the pure HA.     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Double emulsions (W/O/W): physical characteristics and perceived intensity of salty taste

This study addressed the correlation between physical characteristics of double emulsions and sensory perception, as the microstructure of these systems may provide the mechanism to understanding the initial cause of the altered salty taste. Thus, double emulsions (W/O/W) were prepared using different volumes of the internal aqueous phase while maintaining the same fat and sodium contents in the evaluated systems. Polyglycerol polyricinoleate (PGPR) and tween 80 were used as hydrophobic and hydrophilic emulsifiers, respectively. After preparation, the samples were stored at 25 °C for 4 days and submitted to analysis of optical microscopy, distribution and polydispersity of the oil droplets size, electrical conductivity, rheological behaviour and sensorial analysis. It was found that the use of different emulsifier concentrations (PGPR) did not influence the physical characteristics of the emulsions with the same formulation, but emulsions with different internal aqueous phase concentrations presented different results. These distinct characteristics may have influenced sensorial perception, as the emulsion with higher internal phase concentration was considered saltier. Thus, it can be concluded that structural differences of the double emulsions can be used to decrease the sodium contents without perceivable changes in salty taste.