Introduction: Major advancements in DNA sequencing methods introduced in the first decade of the new millennium initiated a rapid expansion of sequencing studies, which yielded a tremendous amount of DNA sequence data, including whole sequenced genomes of various species, including plants. A set of novel sequencing platforms, often collectively named as “next-generation sequencing” (NGS) completely transformed the life sciences, by allowing extensive throughput, while greatly reducing the necessary time, labor and cost of any sequencing endeavor. Purpose: of this paper is to present an overview NGS platforms used to produce the current compendium of published draft genomes of various plants, namely the Roche/454, ABI/SOLiD, and Solexa/Illumina, and to determine the most frequently used platform for the whole genome sequencing of plants in light of genotypization of immortelle plant. Materials and methods: 45 papers were selected (with 47 presented plant genome draft sequences), and utilized sequencing techniques and NGS platforms (Roche/454, ABI/SOLiD and Illumina/Solexa) in selected papers were determined. Subsequently, frequency of usage of each platform or combination of platforms was calculated. Results: Illumina/Solexa platforms are by used either as sole sequencing tool in 40.42% of published genomes, or in combination with other platforms - additional 48.94% of published genomes, followed by Roche/454 platforms, used in combination with traditional Sanger sequencing method (10.64%), and never as a sole tool. ABI/SOLiD was only used in combination with Illumina/Solexa and Roche/454 in 4.25% of publications. Conclusions: Illumina/Solexa platforms are by far most preferred by researchers, most probably due to most affordable sequencing costs. Taking into consideration the current economic situation in the Balkans region, Illumina Solexa is the best (if not the only) platform choice if the sequencing of immortelle plant (Helichrysium arenarium) is to be performed by the researchers in this region.

The hormone erythropoietin (EPO) is essential for the survival, proliferation and differentiation of the erythrocytic progenitors. The EPO receptor (EPO-R) of erythrocytic cells belongs to the cytokine class I receptor family and signals through various protein kinases and STAT transcription factors. The EPO-R is also expressed in many organs outside the bone marrow, suggesting that EPO is a pleiotropic anti-apoptotic factor. The controversial issue as to whether the EPO-R is functional in tumor tissue is critically reviewed. Importantly, most studies of EPO-R detection in tumor tissue have provided falsely positive results because of the lack of EPO-R specific antibodies. However, endogenous EPO appears to be necessary to maintain the viability of endothelial cells and to promote tumor angiogenesis. This review paper reviews EPO use in cancer patients and its management of anemia. While the findings promise beneficial effects of endogenous EPO and its therapeutic analogues as tissue-protective factors, for example in ischemic and degenerative heart and brain diseases, fear has also arisen that EPO may promote tumor cell survival and stimulate tumor growth. If the cancer patient is being treated with curative intent, the use of ESAs should be avoided. If the treatment plan is more conservative or palliative, ESA should be considered for anemia treatment, but the treatment should

The purpose of this paper is to provide an overview of genes that have an impact on athletic performance. In recent years, there is a visible progress in molecular biology techniques, which facilitate researches in the field of genetics related to the sport performance. The paper focuses on 2 genes which are most intensively studied in relation to the athletic ability – angiotensine I-converting enzyme (ACE) and alpha-actinin 3 (ACTN3). There are shown results from many researches, and they indicate that genetic factors have effect on sports performance, but also impact of training and environment is important. With new approaches, new polymorphisms are discovered, so research of this area of genetics is still in progress. KeywordsAthletic Performance; Genetics; Polymorphism; Genotype; Endurance; Strength; ACE; ACTN3.

Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies are important in silico methods in rational drug design. The aim of this methods are to optimize the existing leads in order to improve their biological activities and physico-chemical properties. Also, to predict the biological activities of untested and sometimes yet unavailable compounds. This article is a general review of different QSAR/QSPR studies in different previous researches. R2 and Q2 parameters are used in some studies to predict the predictability and robustness of the constructed models. In all mentioned articles QSAR study were good prediction tool for investigation drug activity or binding mode on specific receptors. Keywords— Drug design, QSAR, QSPR, Molecular Descriptor, Coefficient of Determination R2, Squared Correlation Coefficient Q2.

This paper presents the overview of machine learning techniques in classification of diabetes and cardiovascular diseases (CVD) using Artificial Neural Networks (ANNs) and Bayesian Networks (BNs). The comparative analysis was performed on selected papers that are published in the period from 2008 to 2017. The most commonly used type of ANN in selected papers is multilayer feedforward neural network with Levenberg-Marquardt learning algorithm. On the other hand, the most commonly used type of BN is Na'ive Bayesian network which shown the highest accuracy values for classification of diabetes and CVD, 99.51% and 97.92% retrospectively. Moreover, the calculation of mean accuracy of observed networks has shown better results using ANN, which indicates that higher possibility to obtain more accurate results in diabetes and/or CVD classification is when it is applied to ANN.

The relationship between single nucleotide polymorphisms (SNPs) and phenotypes is noisy and cryptic due to the abundance of genetic factors and the influence of environmental factors on complex traits, which makes the idea of applying artificial neural networks (ANNs) as universal approximates of complex functions promising. In this study, we compared different ANN architectures and input parameters to predict the adult length of Pacific lampreys, which is the primary indicator of their total migratory distance. Feedforward and simple recurrent network architectures with a different range of input parameters and different sizes of hidden layers were compared. Results indicate that the highest performing ANN had an accuracy of 67.5% in discriminating between long and short specimens. Sensitivity and specificity were 62.16% and 70.73%, respectively. Our results imply that feedforward ANN architecture with a single hidden neuron is enough to solve the problem of specimen classification. Nonetheless, while ANNs are useful at approximating functions with unknown relationships in the case of SNP data, additional work needs to be performed to ensure that the chosen SNP markers are related to functional regions related to the examined trait, as the use of non-specific markers will result in the introduction of noise into the dataset.

The medical device industry has grown rapidly and incessantly over the past century. The sophistication and complexity of the designed instrumentation is nowadays rising and, with it, has also increased the need to develop some better, more effective and efficient maintenance processes, as part of the safety and performance requirements. This paper presents the results of performance tests conducted on 50 mechanical ventilators and 50 infant incubators used in various public healthcare institutions. Testing was conducted in accordance to safety and performance requirements stated in relevant international standards, directives and legal metrology policies. Testing of output parameters for mechanical ventilators was performed in 4 measuring points while testing of output parameters for infant incubators was performed in 7 measuring points for each infant incubator. As performance criteria, relative error of output parameters for mechanical ventilators and absolute error of output parameters for infant incubators was calculated. The ranges of permissible error, for both groups of devices, are regulated by the Rules on Metrological and Technical Requirements published in the Official Gazette of Bosnia and Herzegovina No. 75/14, which are defined based on international recommendations, standards and guidelines. All ventilators and incubators were tested by etalons calibrated in an ISO 17025 accredited laboratory, which provides compliance to international standards for all measured parameters.The results show that 30% of the tested medical devices are not operating properly and should be serviced, recalibrated and/or removed from daily application.