Ekologiya cheloveka (Human Ecology)

In this review article we provide an overview of epigenetic markers associated with the effects of particulate matter with different aerodynamic diameters, namely, PM0,1, PM, PM10 and PM2,5-10. The developed list of more than 150 epigenetic signatures of environmental pollutants on different human health conditions may contribute to the development of novel methodological approaches to early detection of diseases in individuals exposed to particulate matter. In addition, this work can become a prerequisite for the development of fundamentally new air purification systems.

Aim: To assess temporal trends in morbidity and mortality among preterm and term newborns in the Russian Federation. Methods: Morbidity and mortality of newborns with birthweight of 1 000 grams or more were calculated using data from all state statistical observation forms in 2016-2018 in the Russian Federation. Data were analyzed in total and separately for term and preterm babies. Ninety-five confidence intervals (CI) for neonatal morbidity and mortality were calculated. Temporal trends were assessed. Comparisons between groups were performed using Pearson's chi-square test. Results: A significant decrease in both morbidity and mortality of newborns was observed for the most of causes. However, we found an increase in the incidence of respiratory disorders (45,5 %; 95 % CI: 45,2 % - 45,8 % in 2016 vs. 52,2 %; 95 % CI: 51,9 % - 52,6 % in 2018; p < 0,001); respiratory distress (24,5 %; 95 % CI: 24,3 % - 24,8 % vs. 29,3 %; 95% CI: 29,1 % - 29,6 %; p < 0,001); hemolytic disease of the fetus and newborn (10,1 %; 95 % CI: 10,0 % - 10,3 % vs. 11,1 %; 95 % CI: 10,9 % - 11,3 %; p < 0,001) and congenital anomalies (29,6 %; 95 % CI: 29,4 % - 29,9 % vs. 32,9 %; 95 % CI: 32,6 % - 33,2 %; p < 0,001) over the study period. Mortality from infectious diseases specific to the perinatal period increased from 0,073 %; 95 % CI: 0,061 % - 0,087 % in 2016 to 0,082 %; 95 % CI: 0,068 % - 0,098 % in 2018 including bacterial sepsis (from 0,029 %; 95 % CI: 0,022 % - 0,037 % to 0,039 %; 95 % CI: 0,030 % - 0,051 %). Mortality from intraventricular hemorrhage increased from 0,008 %; 95 % CI: 0,0046 % - 0,0131 % to 0,01 %; 95 % CI: 0,006 % - 0,016 % in term neonates. Mortality due to neonatal aspiration pneumonia increased among preterm infants (from 0,02 %; 95 % CI: 0,02 % - 0,07 % to 0,03 %; 95 % CI: 0,007 % - 0,096 %) as well as other diseases (with 0,19 %; 95 % CI: 0,11 % - 0,29 % to 0,21 %; 95 % CI: 0,13 % - 0,33 %). Conclusion: Despite significant favorable trends in morbidity and mortality of both term and preterm newborns in the Russian Federation we found an increase in incidence of preventable diseases and causes of death warranting improvements in medical care in this age-group.

Aim: To assess the effectiveness of alginate-containing biogel as a health prevention agent for employees of test laboratories with chemical and biological occupational hazards. Methods: An experimental non-controlled study. The sample consisted of 43 healthy non-smoking volunteers aged 24-75 years who were examined before and after taking 30 gram of the food supplement «Laminal (biogel from algae)» during 4 weeks. Cells in smears of buccal epithelium stained with alizarin red C, used fluorescence microscopy were studied. We assessed cells differentiation degrees, cytochemical coefficients and quantified structural features of the nuclei and cytoplasm. Results: We observed stabilization of the cellular elements of buccal epithelium after the use of Laminal with no cytotoxic effects. The number of pathological epithelial cells significantly decreased (p < 0.001). The number of cells with signs of keratinization also decreased over the study period (p < 0.001). Conclusions: The studied food supplement "Laminal (seaweed biogel)" has a potential to mitigate occupational hazards on the oral epithelium among laboratory workers.

Convolutional neural networks are one of the modern tools that allow medical research to analyze and recognize not just a set of data about the objects under study or patients, but to use an image as the object under study. Due to the significant role of visual diagnostic methods in the provision of medical care, the use of intelligent recognition of the results of these methods becomes essential. At the moment, convolutional neural networks become widespread in research on the quality of diagnostics in various fields of medicine. However, complex mathematical apparatus behind convolutional neural networks function, and the tools for their construction limit implementation of these models into medical research and practice. This paper provides a gentle introduction to the methodology and application possibilities of convolutional neural networks in medical research. In this paper the reader will find methodological foundations behind convolutional neural networks, a description of a data set for building such models, an example of construction of a convolutional neural network model for classification of dermatoscopic images using TensorFlow and Keras libraries in Python as well as recommendations on how to present the results of building convolutional neural networks.