Publications by authors named "E S Bolgova"

Endometrial cancer (EC) is the most common gynecological tumor in high-income countries, and its incidence has increased over time. The most critical risk factor for EC is the long-term unopposed exposure to increased estrogens both exogenous and endogenous. Machine learning can be used as a promising tool to resolve longstanding challenges and support identification of the risk factors and their correlations before the clinical trials and make them more focused.

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Timely identification of risk factors in the early stages of pregnancy, risk management and mitigation, prevention, adherence management can reduce the number of adverse perinatal outcomes and complications for both mother and a child. We have retrospectively analyzed electronic health records from the perinatal Center of the Almazov specialized medical center in Saint-Petersburg, Russia. Correlation analysis was performed using Pearson correlation coefficient to select the most relevant predictors.

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Prediction of a labor due date is important especially for the pregnancies with high risk of complications where a special treatment is needed. This is especially valid in the countries with multilevel health care institutions like Russia. In Russia medical organizations are distributed into national, regional and municipal levels.

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Article Synopsis
  • The study examined the variability of lumbar spine lordosis in mature individuals, analyzing 224 nuclear magnetic tomograms using advanced morphometric and statistical methods.
  • A new hardware-software complex was developed to create 3D models of the lumbar spine, enhancing understanding of spine biomechanics.
  • Findings indicated that normal lumbar lordosis angles optimally handle compression loads, while extreme hyperlordosis or hypolordosis can impair spinal functionality.
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