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Influenza viruses are significant human respiratory pathogens that cause infections and unpredictable pandemic outbreaks. M2 ion-channel protein, participating in the transmission of viral genetic materials into infected cells, is considered to be the crucial target for old-generation drugs such as rimantadine and amantadine. Neuraminidase protein, which is responsible for the replication of the influenza virus, is affected by the new generation of drugs, including oseltamivir (Tamiflu) and zanamivir (Relenza). The virus mutations that cause oseltamivir resistance are also described. This review presents the details concerning the treatment of influenza neuraminidase inhibitors against the H5N1 strain. It also describes virus mutations that cause resistance to oseltamivir and presents a new drug, peramivir, which is a neuraminidase inhibitor that was introduced against the H1N1 epidemic. This work specifies the details of the pharmacokinetics, dosing, toxicity, side effects, and efficiency of the drugs being used against influenza A virus infections.

The fuzzy oil drop model was applied to characterize the hydrophobic core structure in plant carboxylesterase. The characteristics revealed the status of β-sheets in the central part of the molecule as discordant as opposed to the expected hydrophobicity distribution. Particularly, the β-strands and helices in close proximity to the enzymatic residues recognized as discordant with respect to the ideal hydrophobicity distribution of hydrophobic core are of high importance. It is assumed that this local irregularity is the form of coding the specificity of enzymes. The protein under consideration appears to be the next example proving this assumption.

Background Hydroxysteroid dehydrogenase enzymes belong to the short-chain dehydrogenase/reductase (SDR) superfamily and aldo-keto reductases (AKRs). SDR is involved in the metabolism of many compounds (hormones, lipids, etc.) and is present in almost all studied genomes. Two hundred members of hydroxysteroid dehydrogenases have been analysed in terms of natural mutational variability. The second superfamily comprises AKR superfamily group enzymes whose function is catalysing the oxidation and reduction of many substrates by binding NAD(P)H as a cofactor. This kind of study is the first approach for the hydroxysteroid dehydrogenase family. This information grants practical meaning to designing potential specific drugs to fight specific diseases caused by mutations. Methods In the research, amino acid sequences of representatives of the hydroxysteroid dehydrogenase family were extracted from the UniProt database. In total, the analysed 200 sequences with the highest degree of similarity were shown by BLAST searches. In the sequence analyses, we used the following software: ClustalX (multiple sequence alignment), Consensus Constructor (creating consensus sequence), and CORM (finding correlated mutations). Results The CORM program identified potential sites of correlated mutations in hydroxysteroid dehydrogenases. This program generated 18 tables of results that contain the amino acid positions of mutations. Seven of these are presented in this paper. Conclusions The primary structure of the hydroxysteroid dehydrogenase family shows high variation.

A new method of predicting the properties of carbon nanomaterials from carbon nanotubes and graphene oxide, using electrophoretic deposition (EPD) on a metal surface, was investigated. The main goal is to obtain the basis for nervous tissue stimulation and regeneration. Because of the many variations of the EPD method, costly and time-consuming experiments are necessary for optimization of the produced systems. To limit such costs and workload, we propose a neural network-based model that can predict the properties of selected carbon nanomaterial systems before they are produced. The choice of neural networks as predictive learning models is based on many studies in the literature that report neural models as good interpretations of real-life processes. The use of a neural network model can reduce experimentation with unpromising methods of systems processing and preparation. Instead, it allows a focus on experiments with these systems, which are promising according to the prediction given by the neural model. The performed tests showed that the proposed method of predictive learning of carbon nanomaterial properties is easy and effective. The experiments showed that the prediction results were consistent with those obtained in the real system.

Background: Proper, early, and exact identification of gait impairments and their causes is regarded as a prerequisite for specific therapy and a useful control tool to assess efficacy of rehabilitation. There is a need for simple tools allowing for quickly detecting general tendencies. Objective: The aim of this paper is to present the outcomes of traditional and fuzzy-based analysis of the outcomes of post-stroke gait reeducation using the NeuroDevelopmental Treatment-Bobath (NDT-Bobath) method. Materials and methods: The research was conducted among 40 adult people: 20 of them after ischemic stroke constituted the study group, and 20 healthy people constituted the reference group. Study group members were treated through 2 weeks (10 therapeutic sessions) using the NDT-Bobath method. Spatio-temporal gait parameters were assessed before and after therapy and compared using novel fuzzy-based assessment tool. Results: Achieved results of rehabilitation, observed as changes of gait parameters, were statistically relevant and reflected recovery. One-number outcomes from the proposed fuzzy-based estimator proved moderate to high consistency with the results of the traditional gait assessment. Conclusions: Observed statistically significant and favorable changes in the health status of patients, described by gait parameters, were reflected also in outcomes of fuzzy-based analysis. Proposed fuzzy-based measure increases possibility of the clinical gait assessment toward more objective clinical reasoning based on common use of the mHealth solutions.

Because an average human spends one third of his life asleep, it is apparent that the quality of sleep has an important impact on the overall quality of life. To properly understand the influence of sleep, it is important to know how to detect its disorders such as snoring, wheezing, or sleep apnea. The aim of this study is to investigate the predictive capability of a dual-modality analysis scheme for methods of sleep-related breathing disorders (SRBDs) using biosignals captured during sleep. Two logistic regressions constructed using backward stepwise regression to minimize the Akaike information criterion were extensively considered. To evaluate classification correctness, receiver operating characteristic (ROC) curves were used. The proposed classification methodology was validated with constructed Random Forests methodology. Breathing sounds and electrocardiograms of 15 study subjects with different degrees of SRBD were captured and analyzed. Our results show that the proposed classification model based on selected parameters for both logistic regressions determine the different types of acoustic events during sleep. The ROC curve indicates that selected parameters can distinguish normal versus abnormal events during sleep with high sensitivity and specificity. The percentage of prediction for defined SRBDs is very high. The initial assumption was that the quality of result is growing with the number of parameters included in the model. The best recognition reached is more than 89% of good predictions. Thus, sleep monitoring of breath leads to the diagnosis of vital function disorders. The proposed methodology helps find a way of snoring rehabilitation, makes decisions concerning future treatment, and has an influence on the sleep quality.