Background: During the process of the treatment of COVID-19 hospitalized patients, physicians still face a lot of unknowns and problems. Despite the application of the treatment protocol, it is still unknown why the medical status of a certain number of patients worsens and ends with death. Many factors were analyzed for the prediction of the clinical outcome of the patients using different methods. The aim of this paper was to develop a prediction model based on initial laboratory blood test results, accompanying comorbidities, and demographics to help physicians to better understand the medical state of patients with respect to possible clinical outcomes using neural networks, hypothesis testing, and confidence intervals. Methods: The research had retrospective-prospective, descriptive, and analytical character. As inputs for this research, 12 components of laboratory blood test results, six accompanying comorbidities, and demographics (age and gender) data were collected from hospital information system in Sarajevo for each patient from a sample of 634 hospitalized patients. Clinical outcome of the hospitalized patients, survival or death, was recorded 30 days after admission to the hospital. The prediction model was designed using a neural network. In addition, formal hypothesis tests were performed to investigate whether there were significant differences in laboratory blood test results and age between patients who died and those who survived, including the construction of 95% confidence intervals. Results: In this paper, 11 neural networks were developed with different threshold values to determine the optimal neural network with the highest prediction performance. The performances of the neural networks were evaluated by accuracy, precision, sensitivity, and specificity. Optimal neural network model evaluation metrics are: accuracy = 87.78%, precision = 96.37%, sensitivity = 90.07%, and specificity = 62.16%. Significantly higher values (P < 0.05) of blood laboratory result components and age were detected in patients who died. Conclusion: Optimal neural network model, results of hypothesis tests, and confidence intervals could help to predict, analyze, and better understand the medical state of COVID-19 hospitalized patients and thus reduce the mortality rate.

Background: Angiotensin-converting enzyme 2 (ACE2) is not only an enzyme but also a functional receptor on cell surfaces through which Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). The exact mechanism by which arterial hypertension (particularly regulated) could affect the presentation and outcome of Coronavirus disease-19 (COVID-19) has not been fully elucidated. Objective: The aim of this study was to analyze the parameters of patients with verified COVID-19 and existing arterial hypertension at the time of hospital admission and to develop neural network model. Methods: The research had a cross-sectional descriptive and analytical character, and included patients (n=634) who were hospitalized in the General Hospital “Prim. dr. Abdulah Nakas” in Sarajevo, Bosnia and Herzegovina, in the period from 01 Sep 2020 to 01 May 2021. From the hospital information system, which is used in everyday clinical work, laboratory parameters at admission were verified, along with demographic data, the comorbidities, while the outcome (recovery, death) was recorded thirty days after the admission. Results: Out of the total number, in 314 patients (200 males), arterial hypertension was verified, out of which, 56 (17.83%) patients died. Patients were divided into two groups, according to outcome, i.e., whether they survived COVID-19 infection or not. A significant difference in age (p = 0.00), erythrocyte count (p = 0.03), haemoglobin (p = 0.05), hematocrit (p = 0.03), platelets count (p = 0.00), leukocytes (p = 0.01), neutrophils (p = 0.00), lymphocytes (p = 0.00), monocytes (p = 0.00), basophils (p = 0.00), eosinophils (p = 0.00), C-reactive protein (p = 0.00) and D-dimer (p = 0.01) was noted. When patients who died and had hypertension were compared with those who died and did not have hypertension (n = 15), out of alll the analyzed parameters, the only significant difference was established in the patient’s age (p = 0.00). In case when patients with hypertension who died were compared to patients with hypertension and diabetes mellitus who died no significant differences were found between features. Conclusion: Patients with hypertension and COVID-19 who died were older, had higher values of erythrocytes, hemoglobin, hematocrit, leukocytes, neutrophils, CRP and D-dimer, and lower values of platelets, lymphocytes, monocytes, basophils and eosinophils count at admission. Compared to deaths without hypertension, the only difference that was established was that patients with hypertension were older.

Background: Sedentary behavior carries the risk of musculoskeletal problems, especially in the lumbosacral region of the spinal column.  According to modern lifestyle, this has begun to be a public health issue. Objective: To point to the health risks of working at the computer and present an ergonomic analysis of the typical and improved position of workers in front of the computer, thereby reducing the chances of emergence occupational diseases. Results:  Changing the position of the subjects led to a change in lumbar pressure from 2,818 N/m2 to 351 N/m2. Software analysis of the changed position indicates that this position is acceptable, both for the lumosacral region of the spine and for the abdominal muscles. Conclusions:  A change in body position will decrease lumbar moment and the load on the lumbosacral region of the spine. Work chair with lumbar support, the right desk height, setting the appropriate position of the monitor, selecting the optimal keyboard and mouse, dividing the workspace into appropriate zones, as well as changing lifestyle and habits should be part of the management of people who spend most of their working time in a sitting position.

Background: The problem of heavy school bags is a global problem recognized in many countries in Europe and the world, including in Bosnia and Herzegovina. In addition to poor posture habits, "sedentary lifestyles" and insufficient physical activity, school bags is one of the main causes of low back pain and deformity in pupils. The recommendation of the World Health Organization (WHO) is that the weight of the school bag should not exceed 10% of the student's weight. However, in practice these limitations are far from reality with the obvious problems caused by too heavy bags. The aim of the paper is to identify and analyze the backbone load caused by the overweight school backpacks in real school work conditions and eliminate them by creating new solutions that are in line with ergonomic and biomechanical principles, as well as the recommendation given by WHO. Methods: The research included first grade primary school students at the age of seven, including their parents. The research began by interviewing parents with relevant questions, as well as measuring the students’ height and weight and the weight of their school backpacks. The analysis was performed in CATIA v5 software package (Dassault Systemes, Velizy-Villacoublay, France) using its advanced biomechanical modules. By knowing the anthropometric and work environment data with ergonomic design and analysis, the biomechanical analysis, rapid upper limb assessment (RULA) and carry analysis were performed. Results: The conducted survey showed that 84% of students walk from home to school nineteen minutes on average and that 77% of them carry their school backpacks independently. Based on the measurements, it has been shown that, on average, the weight of the school backpacks is well above the WHO recommendation. A study conducted on a representative sample of students confirmed the relation between fatigue and spinal pain caused by carrying a heavy school bag. Computer analysis showed excessive loads on the spinal segment of L4/L5 that were outside the normal range of 3,400 N. Conclusions: A simulated computer analysis using RULA and biomechanical analysis with calculations of maximum loads in the lumbar segment of students found that school backpacks carried by students were too heavy for their age and well beyond the normal limits and WHO recommendations. The analysis showed that it is necessary to reduce the weight of the bag by about 30%.

Background: The aim of the article was to create an appropriate computer model based on the real status of the mortar operator's workplace and to analyze the workplace. After that, for any possible exceedances from the aspect of the organism's load and safety, the aim is to redesign the workplace and bring it within the limits of the permissible load, and therefore the required safety. The aim is also to identify the characteristic work movements performed by the soldier and to carry out an ergonomic analysis of the soldier's efforts and to propose appropriate improvements. Methods: The analysis is performed on a total of 20 soldiers, from which is determined an average model of the following characteristics: 180 cm in height and 85 kg in weight. The task is to take a mine from the shell containing the mines, then transfer it to the mortar and fill the mortar barrel. The weight of the 120 mm mortar grenade is 14.8 kg. The average soldier is 26 years old and his military exercise lasts 4 hours. The CATIA software package (Dassault Systemes, Velizy-Villacoublay, France) is used for analysis. By knowing the anthropometric and work environment data, with ergonomic design and analysis, the following analyses were made: biomechanical analysis, rapid upper limb assessment (RULA) and carry analysis (option from CATIA software). Results: The proposed modification of the position resulted in a decrease in the L4/L5 torque from 316 Nm to 154 Nm along with decreasing of the compression force on the L4/ L5 from 5779 N to 3038 N (the compression force allowed is 3400 N), and while the RULA analysis is from the red color position 1 (score 7; maximum load requiring rapid repositioning of such position), revised final score 4 made in yellow (a solution acceptable for this work place). Conclusions: By ergonomic analysis, obtained proposal will lead to less chance of injury, prevention of burn out syndrome, fewer chances of illness, decreasing the fatigue, greater safety, less energy spent and better preparedness for all necessary tasks.

Introduction: Neonatal or newborn period includes the first 28 days after the birth of a child. The immune sistem of a newborn is not fully developed and can not be completely effective oppose to pathogens which the infant can be exposed perinatally or in the period just before birth and seven days after. Goal : The aim of this study was to identify the most common causative agents of neonatal infections and the movement of infections through the observed period. Material and methods : This retrospective analytical study recruited 160 patients admitted to the Neonatology Department of the Cantonal Hospital „Dr Irfan Ljubijankic“ in Bihac, in the period from January 2013 to December 2014 on suspicion of neonatal infection. Results : Of 160 neonates suspected on neonatal infections in 123 (76,9%) was confirmed. Most neonates were admitted in the period from October to December 2013 (n=31, 19,4%). Most neonates with confirmed neonatal infection were admitted in the period from January to March 2013 (n=25, 20.3%). The most common diagnosis was Infectio perinatalis (n=57, 28,5%). Of the 87 isolated pathogens 65% (n=57) were Gram-positive, from which the most common were Staphylococcus aureus and MRSA 82,5% (n=47). Conclusion : Of the total number of neonates admitted on suspicion of neonatal infection, healing was result in 75% (n=120), neurological deficit in 18,1% (n=29), and the death in 6,9% (n=11) neonates. Keywords : neonates, Infectio perinatalis , Staphylococcus aureus , neurological deficit