Background Bosnia and Herzegovina is among ten countries in the world with the highest mortality rate due to COVID-19. Lack of lockdown, open borders, high mortality rate, no vaccination plan, and strong domestic anti-vaccination movement present serious COVID-19 concerns in Bosnia and Herzegovina. In such circumstances, we set out to study 1) the willingness of general public to receive the vaccine, 2) factors that affect vaccine rejection, and 3) motivation for vaccine acceptance. Methods A cross-sectional study was conducted among 10471 adults in Bosnia and Herzegovina to assess the acceptance or rejection of participants toward COVID-19 vaccination. Using a logistic regression model, we examined the associations of sociodemographic characteristics with vaccine rejection, reasons for vaccine hesitancy, preferred vaccine manufacturer, and information sources. Results Surprisingly, only 25.7% of respondents indicated they would like to get a COVID-19 vaccine, while 74.3% of respondents were either hesitant or completely rejected vaccination. The vaccine acceptance increased with increasing age, education, and income level. Major motivation of pro-vaccination behavior was intention to achieve collective immunity (30.1%), while the leading incentive for vaccine refusal was deficiency of clinical data (30.2%). The Pfizer-BioNTech vaccine is shown to be eightfold more preferred vaccine compared to the other manufacturers. For the first time in Bosnia, vaccine acceptance among health care professionals has been reported, where only 39.4% of healthcare professionals expressed willingness to get vaccinated. Conclusion With the high share of the population unwilling to vaccinate, governmental impotence in securing the vaccines supplies, combined with the lack of any lockdown measures suggests that Bosnia and Herzegovina is unlikely to put COVID-19 pandemic under control in near future.

Bosnia and Herzegovina is among ten countries in the world with the highest mortality rate due to COVID-19 infection. Lack of lockdown, open borders, high mortality rate, no herd immunity, no vaccination plan, and strong domestic anti-vaccination movement present serious COVID-19 concerns in Bosnia and Herzegovina. In such circumstances, we set out to study if the population is willing to receive the vaccine. A cross-sectional study was conducted among 10,471 adults in Bosnia and Herzegovina to assess the attitude of participants toward COVID-19 vaccination. Using a logistic regression model, we assessed the associations of sociodemographic characteristics with vaccine rejection, reasons for vaccine hesitancy, preferred vaccine manufacturer, and information sources. Surprisingly, only 25.7% of respondents indicated they would like to get a COVID-19 vaccine, while 74.3% of respondents were either hesitant or completely rejected vaccination. The vaccine acceptance increased with increasing age, education, and income level. Major motivation of pro-vaccination behaviour was intention to achieve collective immunity (30.1%), while the leading incentive for vaccine refusal was deficiency of clinical data (30.2%). The Pfizer-BioNTech vaccine is shown to be eightfold more preferred vaccine compared to the other manufacturers. For the first time, vaccine acceptance among health care professionals has been reported, where only 39.4% of healthcare professionals expressed willingness to get vaccinated. With the high share of the population unwilling to vaccinate, governmental impotence in securing the vaccines supplies, combined with the lack of any lockdown measures suggests that Bosnia and Herzegovina is unlikely to put COVID-19 pandemic under control in near future.

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

This paper presents implemented artificial neural network (ANN) for diagnosing pulmonary tuberculosis progression and dynamics. Tuberculosis is an infectious disease caused in most cases by microorganism, called Mycobacterium tuberculosis. Tuberculosis is a huge problem in most low-income countries, and also in the Balkan region. The design of the artificial neural network is based on two strains of tuberculosis bacteria and multiple strains of tuberculosis. Training data sets contain 1000 reports for this artificial neural, 800 of them are used for estimation and 200 for validation. The ANN system is validated on 1400 patients from the Clinical Centre University of Sarajevo in the two years period. Out of 1315 patients, 99.24% are correctly classified as tuberculosis related patients. System was 100% successful on 85 patients were diagnosed with normal lung function. Sensitivity of 99.24% and specificity of 100% in tuberculosis classification are obtained. Our artificial neural network is a promising method for predicting diagnosis and possible treatment routine for tuberculosis disease.