It is known that molecular hydrogen is a relatively stable, ubiquitous gas that is a minor component of the atmosphere. At the same time, in recent decades molecular hydrogen has been shown to have diverse biological effects. By the end of 2022, more than 2000 articles have been published in the field of hydrogen medicine, many of which are original studies. Despite the existence of several review articles on the biology of molecular hydrogen, many aspects of the research direction remain unsystematic. Therefore, the purpose of this review was to systematize ideas about the nature, characteristics, and mechanisms of the influence of molecular hydrogen on various types of cells, including stem cells. The historical aspects of the discovery of the biological activity of molecular hydrogen are presented. The ways of administering molecular hydrogen into the body are described. The molecular, cellular, tissue, and systemic effects of hydrogen are also reviewed. Specifically, the effect of hydrogen on various types of cells, including stem cells, is addressed. The existing literature indicates that the molecular and cellular effects of hydrogen qualify it to be a potentially effective agent in regenerative medicine.

The aim of the study is to develop recommendations for adapting educational content based on the use of a neural network method for recognizing the human autonomic nervous system degree of activity. The initial data for decision making are the vectors of cardiointervals obtained with the help of the pulse sensor. The state of the autonomic nervous system is monitored by a two-layer artificial neural network of direct propagation. The artificial neural network was trained by combining gradient and stochastic training methods. A trial sample was used as a training set, consisting of 168 records of people's cardiointervals belonging to different social and age groups. The presented method for recognizing the autonomic nervous system activity degree was tested via the shared programming support environment Lazarus. As a result of network training, a set of weighting coefficient values was found, for which 100% correct recognition is performed over the entire trial sample. A personalized training system that monitors the functional state of the student varies the presentation of educational content. When the functional state changes during the training load, the virtual agent organizes the training process so as to avoid stress load.

The Purpose – its were developed multiparametric models and algorithms of the optimization playing training biooperated. In work are used methods of the system analysis, modeling, system technical analysis and constructing, mathematical statistics, methods of the computer analysis heart rate variability by means of sensor of the pulse and breathings.The Conclusion: its were designed two models of strategy playing, one – with installation on avoid of the failures and the second – on achievement of the success. The algorithms of the success and efficiency were formed for period of the termination of the biooperated play.