Abstract:
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 antiinflammatory 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.
