Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.

Today’s modelling approaches in Systems Medicine are increasingly multiscale, containing two or more submodels, where each operates on different temporal and/or spatial scales. In addition, as these models become increasingly sophisticated, they tend to be run as multiscale computing applications using computational infrastructures such as clusters, supercomputers, grids or clouds. Constructing, validating and deploying such applications is far from trivial, and communities in different scientific disciplines have chosen very diverse approaches to address these challenges. Within this paper we reflect on the use of Multiscale Computing within the context of Systems Medicine. Multiscale Computing is widely applied within this area, and instead of summarizing the field as a whole we will highlight a set of challenges that we believe are of key relevance to the Systems Medicine community.

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.

Nanotechnology has shown its great potential in different fields of science such as medicine and pharmacy. This paper presents a review on artificial neural networks used in nanotechnology based on information gathered from different research. It is important to understand applications of artificial neural networks so that they can be used even more efficiently in future applications. Research papers summarized and compared here show different results in two fields of science. Artificial neural networks were made and proven to be useful in diagnostics and tracing diseases. The pharmaceutical industry has also shown to be a good candidate for the development of ANNs on the nanotechnology level. Regression analysis was used as a statistical method for presenting the best results from both fields observed. Root mean square error and mean error were calculated to measure the differences between values predicted by a model and the values actually observed from the environment that was being modelled. Based on individual results, each of the ANNs made were accurate enough to be considered as a diagnostic tool in fields of medicine and pharmacy. Performance is greater than 90% 10 out of 12 times, which is viewed in this paper. The multilayer perceptron ANN is mostly used. Based on the latest results, in upcoming years, one can expect better understanding and more research in the field of ANN applications in nanotechnology.

This paper presents an overview of a study in which the immunity and susceptibility of life-supporting medical equipment was evaluated by exposing the equipment to electromagnetic interference (EMI). Electromagnetic interference, in the past, has been proven to influence the activity and reliability of certain medical devices. Since there has been dramatic increase in the use of cellular phones, these electromagnetic emitting devices have become part of the environment of medical devices. In the present paper, the influence of cellular phones at various distances and proportions on a wide range of medical devices were studied. In total 136 devices were tested after being exposed to cellular phones working in different operating modes. Testing was performed in Healthcare institutions of Bosnia and Herzegovina. Out of all 136 devices, a significant interference was detected in only one of the various defibrillators tested. Other EMI encountered in two electrocardiograms, during the tests, were negligible which proves that current medical devices are designed to operate more safely, with higher immunity and lower susceptibility. The improved designs of medical devices are results of strict electromagnetic compatibility standards to which the devices must comply.

The usage of Artificial Neural Networks (ANNs) for genome-enabled classifications and establishing genome-phenotype correlations have been investigated more extensively over the past few years. The reason for this is that ANNs are good approximates of complex functions, so classification can be performed without the need for explicitly defined input-output model. This engineering tool can be applied for optimization of existing methods for disease/syndrome classification. Cytogenetic and molecular analyses are the most frequent tests used in prenatal diagnostic for the early detection of Turner, Klinefelter, Patau, Edwards and Down syndrome. These procedures can be lengthy, repetitive; and often employ invasive techniques so a robust automated method for classifying and reporting prenatal diagnostics would greatly help the clinicians with their routine work. The database consisted of data collected from 2500 pregnant woman that came to the Institute of Gynecology, Infertility and Perinatology “Mehmedbasic” for routine antenatal care between January 2000 and December 2016. During first trimester all women were subject to screening test where values of maternal serum pregnancy-associated plasma protein A (PAPP-A) and free beta human chorionic gonadotropin (β-hCG) were measured. Also, fetal nuchal translucency thickness and the presence or absence of the nasal bone was observed using ultrasound. The architectures of linear feedforward and feedback neural networks were investigated for various training data distributions and number of neurons in hidden layer. Feedback neural network architecture out performed feedforward neural network architecture in predictive ability for all five aneuploidy prenatal syndrome classes. Feedforward neural network with 15 neurons in hidden layer achieved classification sensitivity of 92.00%. Classification sensitivity of feedback (Elman’s) neural network was 99.00%. Average accuracy of feedforward neural network was 89.6% and for feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.