I n t r o d u c t i o n: Interactions between oral microbiota and systemic diseases have been suggested. We aimed to examine the composition of oral microbiota with reference to antioxidative defense and its correlation with clinical state in Crohn’s disease (CD) in comparison to ulcerative colitis (UC). Ma t e r i a l s a n d Me t h o d s: Smears were taken from the buccal and tongue mucosa of patients with CD, UC and controls, and cultured with classical microbiology methods. Bacterial colonies were identified using matrix-assisted laser desorption/ionization (MALDI) with a time-of-flight analyzer (TOF). Blood morphology and C-reactive protein (CRP) were analyzed in the hospital laboratory. Antioxidative defense potential (FRAP) was determined using spectrophotometry in saliva and serum. R e s u l t s: Oral microbiota in CD patients were characterized by lower diversity in terms of the isolated bacteria species compared to UC and this correlated with reduced FRAP in the oral cavity and intensified systemic infl ammation. Oral microbiota composition in CD did not depend on the applied treatment. In CD patients, a negative correlation was observed between the FRAP value in saliva and serum and the CRP value in serum. Individual differences in the composition of oral microbiota suggest that different bacteria species may be involved in the induction of oxidative stress associated with a weakening of antioxidative defense in the oral cavity, manifested by ongoing systemic inflammation. C o n c l u s i o n s: Analysis of both the state of the microbiota and antioxidative defense of the oral cavity, as well as their referencing to systemic inflammation may potentially prove helpful in routine diagnostic applications and in aiding a better understanding of CD and UC pathogenesis associated with oral microbiota.
The aim of the study was to choose and validate the tool(s) to predict the number of hospitalized patients by testing three predictive algorithms: a linear regression model, Auto-Regressive Moving Average (ARMA) model, and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model. The study used data from the collection of data on infl ammatory bowel diseases (IBD) from the public database of the National Health Fund for the years 2009–2017, data recalculation taking into account the population of provinces and the country in particular years, and prediction making for the number of patients who would require hospitalization in 2017. Th e anticipated numbers were compared with real data and percentage prediction errors were calculated. Results of prediction for 2017 indicated the number of hospitalizations for Crohn’s disease (CD) and ulcerative colitis (UC) at 17 and 16 respectively per 100,000 persons and 72 per 100,000 persons for all IBD cases. Th e actual outcomes were 21 for both CD and UC (81% and 75% accuracy of prediction, respectively), and 99 for all IBD cases (73% accuracy). The prediction results do not diff er signifi cantly from the actual outcome, this means that the prediction tool (in the form of a linear regression) actually gives good results. Our study showed that the newly developed tool may be used to predict with good enough accuracy the number of patients hospitalized due to IBD in order to organize appropriate therapeutic resources.