The growth of obesity and type 2 diabetes incidence has made bariatric surgery a widespread method of treatment. The effectiveness of bariatricoperations in the treatment of obesity and related metabolic diseases is thoroughly highlighted in medical literature. However, the resultsof surgery do not always correlate with type of operation. As before, the mechanisms have not been fully studied of how the bariatric surgeryinfluence on insulinresistance, entero-insulin axes, adipokines. Understanding such mechanisms will allow us to determine more precisely theindications relating to surgical treatment, and enhance the effectiveness of surgery in specific patient. The review is focusing on the influence ofvarious types of bariatric surgery on the level of adipokines and incretines that participate in regulation of appetite and of fat and carbohydratemetabolism. The article elaborates modern concepts related to the impact of bariatric operations on metabolic disorders in obesity.

Background Thyrotoxic atrial fibrillation (TAF) is a recognized significant complication of hyperthyroidism. Early identification of the individuals predisposed to TAF would improve thyrotoxic patients’ management. However, to our knowledge, an instrument that establishes an individual risk of the condition is unavailable. Therefore, the aim of this study is to build a TAF prediction model and rank TAF predictors in order of importance using machine learning techniques. Methods In this retrospective study, we have investigated 36 demographic and clinical features for 420 patients with overt hyperthyroidism, 30% of which had TAF. At first, the association of these features with TAF was evaluated by classical statistical methods. Then, we developed several TAF prediction models with eight different machine learning classifiers and compared them by performance metrics. The models included ten features that were selected based on their clinical effectuality and importance for model output. Finally, we ranked TAF predictors, elicited from the optimal final model, by the machine learning tehniques. Results The best performance metrics prediction model was built with the extreme gradient boosting classifier. It had the reasonable accuracy of 84% and AUROC of 0.89 on the test set. The model confirmed such well-known TAF risk factors as age, sex, hyperthyroidism duration, heart rate and some concomitant cardiovascular diseases (arterial hypertension and conjestive heart rate). We also identified premature atrial contraction and premature ventricular contraction as new TAF predictors. The top five TAF predictors, elicited from the model, included (in order of importance) PAC, PVC, hyperthyroidism duration, heart rate during hyperthyroidism and age. Conclusions We developed a machine learning model for TAF prediction. It seems to be the first available analytical tool for TAF risk assessment. In addition, we defined five most important TAF predictors, including premature atrial contraction and premature ventricular contraction as the new ones. These results have contributed to TAF prediction investigation and may serve as a basis for further research focused on TAF prediction improvement and facilitation of thyrotoxic patients’ management.

No abstract

Objective. The role of the GLCCI1 gene polymorphism (rs37973) analysis in children with bronchial asthma (BA). Methods. The study involved 60 children 3-17 years old with allergic (n=37) and mixed forms of BA (n=23). The genetic study included performing genotyping of the studied locus of the GLCCI1 gene (rs37973) by PCR-RFLP (polymerase chain reaction – restriction fragment length polymorphism). Results. Analysis of the frequency of genotypes and alleles occurrence established the predominance of heterozygotes (AG) and allele G in the study group. There were statistically significantly higher indices of VC, FVC, FEV1 and MEF75 (according to spirometry data) with a heterozygous genotype (AG) in comparison with a homozygous GG (p<0,05). At the same time, there was a statistically significantly greater number of children with a FEV1 level ≥80% (p=0,009) when comparing the same subgroups of genotypes. Conclusion. The study revealed the association of GLCCI1 gene polymorphism (rs37973) with the respiratory function according to spirometry data. The presence of a heterozygous gene variant (AG) determined higher indicators of the function of respiratory function (VC, FVC, FEV1, MEF75). The study with the establishment of the GLCCI1 gene (rs37973) genotype can be used to diagnose a prognostically favorable and unfavorable course of asthma in children, which should be taken into account when choosing various basic methods of therapy and rehabilitation.

No abstract