Humoral immunity indicators as prediction factors for dysbiosis development risk
Keywordsnormobiota, bacterial vaginosis, linear neural network model for determination of the dysbiosis severity, humoral factor of immune defence
Diseases associated with bacterial vaginosis lead to chronic inflammatory processes of the internal genitals, the development of adhesions of the pelvic organs, infertility, spontaneous abortion at different times, as well as the development of malignant neoplasms. Vaginal microflora is an indicator of a woman's health which can form changing in hormonal and immunological status during various pathological conditions. The aim of the study was to create a system for prediction of the dysbiosis development according to the levels of nonspecific humoral factor of immune defence.
The study was performed in 298 women aged 16 to 64 years, 53 of whom were diagnosed with normocenosis, and 245 have dysbiosis. Women were divided into 3 groups according to age. Regression analysis was used.
Our previous researches have shown a correlation between increased levels of anti-inflammatory cytokines in the blood and vaginal secretions with the stage of dysbiosis. A logistic regression model was constructed during the study, which showed that the risk of developing dysbiosis in terms of normobiota increases with increasing levels of interleukin 2 in the blood, tumor necrosis factor α. Significant features of the three-factor model for predicting the risk of developing dysbiosis (IL2, IL4 and TNFα) were selected by the method of genetic algorithm. The levels of these indicators in the blood were related to the severity of dysbiosis according to the results of discriminant analysis. Thus, a linear neural network model was developed for determination of dysbiosis severity according to the levels of nonspecific humoral factors of immune defence such as the C4 component of the complement system and γ-interferon in vaginal secretions, as well as the amount of circulating immune complexes and tumor necrosis factor α in the blood. Kappa Cohen's agreement for this model on the training set was 0.87 (95% CI 0.82-0.91), and on the confirmatory set was 0.89 (95% CI 0.77-1.00). These indicators show the adequacy of the constructed model. The interface of the expert system for the dysbiosis severity prediction has been created.
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