Metallic biomaterials in the oral cavity are exposed to many factors such as saliva, bacterial microflora, food, temperature fluctuations, and mechanical forces. Extreme conditions present in the oral cavity affect biomaterial exploitation and significantly reduce its biofunctionality, limiting the time of exploitation stability. We mainly refer to friction, corrosion, and biocorrosion processes. Saliva plays an important role and is responsible for lubrication and biofilm formation as a transporter of nutrients for microorganisms. The presence of metallic elements in the oral cavity may lead to the formation of electro-galvanic cells and, as a result, may induce corrosion. Transitional microorganisms such as sulfate-reducing bacteria may also be present among the metabolic microflora in the oral cavity, which can induce biological corrosion. Microorganisms that form a biofilm locally change the conditions on the surface of biomaterials and contribute to the intensification of the biocorrosion processes. These processes may enhance allergy to metals, inflammation, or cancer development. On the other hand, the presence of saliva and biofilm may significantly reduce friction and wear on enamel as well as on biomaterials. This work summarizes data on the influence of saliva and oral biofilms on the destruction of metallic biomaterials.

Stroke is the second cause of death and more importantly first cause of disability in people over 40 years of age. Current therapeutic management of ischemic stroke does not provide fully satisfactory outcomes. Stroke management has significantly changed since the time when there were opened modern stroke units with early motor and speech rehabilitation in hospitals. In recent decades, researchers searched for biomarkers of ischemic stroke and neuroplasticity in order to determine effective diagnostics, prognostic assessment, and therapy. Complex background of events following ischemic episode hinders successful design of effective therapeutic strategies. So far, studies have proven that regeneration after stroke and recovery of lost functions may be assigned to neuronal plasticity understood as ability of brain to reorganize and rebuild as an effect of changed environmental conditions. As many neuronal processes influencing neuroplasticity depend on expression of particular genes and genetic diversity possibly influencing its effectiveness, knowledge on their mechanisms is necessary to understand this process. Epigenetic mechanisms occurring after stroke was briefly discussed in this paper including several mechanisms such as synaptic plasticity; neuro-, glio-, and angiogenesis processes; and growth of axon.

The aim of the study was to evaluate the effect of titanium bone fixations on mitochondrial activity, reactive oxygen species (ROS) production, glutathione metabolism, and selected markers of oxidative/nitrosative stress in the periosteum-like tissue of patients treated with mandible fractures. The study group consisted of 30 patients with bilateral fractures of the mandible body eligible for surgical treatment. Our study is the first one that indicates disturbances of mitochondrial activity as well as a higher production of ROS in the periosteum-like tissue covering titanium fixations of the mandible. We also found significantly higher levels of reduced glutathione and enhanced activity of glutathione reductase in the periosteum homogenates of patients in the study group compared to the control group. Levels of nitrosative (S-nitrosothiols, peroxynitrite, nitrotyrosine) and oxidative stress biomarkers (malondialdehyde, protein carbonyls, dityrosine, kynurenine, and N-formylkynurenine) were statistically elevated in periosteum-like tissue covering titanium fixations. Although exposure to titanium fixations induces local antioxidant mechanisms, patients suffer oxidative damage, and in the periosteum-like tissue the phenomenon of metallosis was observed. Titanium implants cause oxidative/nitrosative stress as well as disturbances in mitochondrial activity.

Motivation Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. The goal of this work is to predict whether mutations are likely to occur in the next flu season using historical glycoprotein hemagglutinin sequence data. One of the major challenges is to model the temporality and dimensionality of sequential influenza strains and to interpret the prediction results. Results In this article, we propose an efficient and robust time-series mutation prediction model (Tempel) for the mutation prediction of influenza A viruses. We first construct the sequential training samples with splittings and embeddings. By employing recurrent neural networks with attention mechanisms, Tempel is capable of considering the historical residue information. Attention mechanisms are being increasingly used to improve the performance of mutation prediction by selectively focusing on the parts of the residues. A framework is established based on Tempel that enables us to predict the mutations at any specific residue site. Experimental results on three influenza datasets show that Tempel can significantly enhance the predictive performance compared with widely used approaches and provide novel insights into the dynamics of viral mutation and evolution. Availability and implementation The datasets, source code and supplementary documents are available at: https://drive.google.com/drive/folders/15WULR5__6k47iRotRPl3H7ghi3RpeNXH. Supplementary information Supplementary data are available at Bioinformatics online.