The world has faced a coronavirus outbreak, which, in addition to lung complications, has caused other serious problems, including cardiovascular. There is still no explanation for the mechanisms of coronavirus that trigger dysfunction of the cardiac autonomic nervous system (ANS). We believe that the complex mechanisms that change the status of ANS could only be solved by advanced multidimensional analysis of many variables, obtained both from the original cardiovascular signals and from laboratory analysis and detailed patient history. The aim of this paper is to analyze different measures of entropy as potential dimensions of the multidimensional space of cardiovascular data. The measures were applied to heart rate and systolic blood pressure signals collected from 116 patients with COVID-19 and 77 healthy controls. Methods that indicate a statistically significant difference between patients with different levels of infection and healthy controls will be used for further multivariate research. As a result, it was shown that a statistically significant difference between healthy controls and patients with COVID-19 was shown by sample entropy applied to integrated transformed probability signals, common symbolic dynamics entropy, and copula parameters. Statistical significance between serious and mild patients with COVID-19 can only be achieved by cross-entropies of heart rate signals and systolic pressure. This result contributes to the hypothesis that the severity of COVID-19 disease is associated with ANS disorder and encourages further research.

Artificial neural networks (ANNs) are machine learning technique, inspired by the principles found in biological neurons. This technique has been used for prediction and classification problems in many areas of medical signal processing. The aim of this paper was to identify individuals with high risk of death after acute myocardial infarction using ANN. A training dataset for ANN was 1705 consecutive patients who underwent 24-hour ECG monitoring, short ECG analysis, noninvasive beat-to-beat heart-rate variability, and baroreflex sensitivity that were followed for 3 years. The proposed neural network classifier showed good performance for survival prediction: 88% accuracy, 81% sensitivity, 93% specificity, 0.85 -measure, and area under the curve value of 0.77. These findings support the theory that patients with high sympathetic activity (reduced baroreflex sensitivity) have an increased risk of mortality independent of other risk factors and that artificial neural networks can indicate the individuals with a higher risk.

Post stroke depression (PSD) is a severe and frequent stroke complication and one of the crucial factors for the outcome of rehabilitation and life quality after stroke. However, mood disorders frequently remain unnoticed and therefore untreated. The aim of the study was to examine all the potential risk factors and determine the independent predictors of early-onset depression after first-ever stroke, which would help identify high-risk patients, establish early diagnosis and timely treatment that would improve the course and prognosis of this disorder. This prospective study included 60 patients treated for their first-ever stroke; there were 30 patients diagnosed with depression and 30 patients without depression. The study included collection and analysis of all socio-demographic and clinical risk factors for PSD. Testing was performed two weeks after stroke. Depression was diagnosed according to the Mini International Neuropsychiatry Interview, DSM-IV diagnostic criteria, and depression severity was quantified by the Hamilton Depression Rating Scale. Cognitive impairment was assessed by the Mini Mental State Examination. Neurological deficit was assessed by the US National Institute of Health Stroke Scale. Our results showed that the independent predictors of early-onset depression after stroke were previous depressive episodes, cognitive dysfunction, and more severe neurological deficit.

Background/Aim: Multiple sclerosis is a disease whose aetiology involves multifactorial interactions among genetic and environmental factors. Obesity is one of the most important environmental factors conducive to the onset and progression of the disease. The aim of the study was to determine the value of body mass index (BMI) in a population of patients with multiple sclerosis compared to the general population, in order to assess the relation between the BMI and physical disability in patients with multiple sclerosis and the influence of the BMI on the course and progression of the disease. Methods: A cross-sectional study was performed in 100 patients suffering from multiple sclerosis (experimental group) and 50 healthy people (control group). In order to determine the degree of physical disability, the Expanded Disability Status Scale (EDSS) was used. Clinical and demographic data and values of the BMI in both studied groups were collected. Statistical analysis included the descriptive statistics, t-test, chi-square test, analysis of variance, correlation and regression analysis. Results: Mean body weight and BMI were significantly higher in the control group (p< 0.05). There was no significant correlation between EDSS and BMI (p = 0.574). There was a correlation between the course of MS and the fact whether BMI was abnormal or normal (p = 0.031). Conclusion: BMI is an environmental factor that significantly correlates with the progression and prediction of multiple sclerosis, but not to the degree of physical disability.

Flutamide is the active substance of the drug and belongs to the group of drugs that have antiandrogenic effect. Flutamide prevents the action of male sex hormones, i.e. suppresses the action of testosterone and dihydrotestosterone. Primarily, the indications for the use of flutamide refer to males and the treatment of advanced prostate cancer. It is also used in the treatment of patients with testicles surgically removed, and in patients who have not responded to another type of therapy or do not tolerate other types of treatment. The efficacy of flutamide has also been proven in the treatment of acne, hirsutism and alopecia in men and women with polycystic ovaries. It is important to emphasize that flutamide can cause severe side effects, above all liver damage, so it is not justified to use it in the treatment of conditions other than prostate cancer. Numerous data on hepatotoxicity (retrospective, prospective studies, case reports, surveillance study) were available in literature, which ranged from asymptomatic to acute, fulminant hepatitis that ended in transplantation, i.e. fatal outcome. In our paper, a review of the literature with case reports of notably hepatotoxicity is presented along with a case from our clinical practice.