OBJECTIVE: Hepatitis E infection is one of the most frequent acute hepatitis in the world. Currently five human genotypes with different geographical distributions and distinct epidemiologic patterns are identified. In Slovakia, only rare cases of hepatitis E have been reported in recent years. Therefore, the aim of the study was to evaluate the prevalence of anti-HEV total antibodies and the main risk factors for HEV in the general population in Eastern Slovakia. METHODS: Detection of anti-HEV total antibodies samples was done by a commercial enzyme-linked immunosorbent assay (ELISA) kit. RESULTS: Of 175 hospitalized patients included in the study, 76 (43.5%) showed positivity for anti-HEV total antibodies. No statistically significant differences were found in anti-HEV positivity between men and women or in the groups of different living areas (town/village - urban/rural). CONCLUSION: Prevalence of anti-HEV total antibodies of hospitalised patients was high. The risk factor significantly associated with antibody positivity was eating raw meat. Other factors, such as sex, age, living area and contact with animals were not associated with antibody positivity.
- MeSH
- ELISA MeSH
- hepatitida - protilátky izolace a purifikace MeSH
- hepatitida E MeSH
- hospitalizace statistika a číselné údaje MeSH
- lidé MeSH
- rizikové faktory MeSH
- séroepidemiologické studie MeSH
- virus hepatitidy E imunologie MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Slovenská republika MeSH
Data Analytics provides various methods and approaches to extract hidden and poten-tially useful knowledge for different purposes. It means that we can use this knowledge for decision support, e.g. to identify crucial inputs and relations, to predict some future state, to confirm or reject our hypothesis. The medical diagnostics deals with all the mentioned tasks to provide a right diagnosis for the patient and to ensure the effective diagnostic process. In this paper, we briefly describe some of our activi-ties oriented to support medical diagnostics by means of Data Analytics approaches; we selected only key points here, more details can be found in our previously pub-lished articles.
In determining the symptoms and predicting disease in medicine, health outcomes of patients are used, which are obtained from different clinical tests. Among the initial symptoms of people suffering from Parkinson's disease we can include muscle rigidity, problems with speech (dysphonia), movement or writing (dysgraphia). In this article, we focus just on the data obtained from the primary symp- toms of patients and their further use for early diagnosis and detection of Parkinson's disease using machine learning methods. We first describe basic characteristics of this disease and its typical features and then we analyze the current state and methods of collecting data used for creating decision models. Next, we also summarize the results of our previous research in this area and describe the future directions of our work.
