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The article summarizes the materials of modern publications on cognitive development of premature infants in connection with perinatal factors and parenting conditions. Leading risk predictors of cognitive defects in premature infants are severe dysmaturity by the time of birth (gestational age

Background. The abilitation of premature infants acquires importance due to the increased prevalence of delayed health disorders in these children. The article considers the issues of individualization of rehabilitation treatment for premature infants according to prediction of perinatal pathology outcomes, determining the individual levels of «abilitation windows» and developing personalized abilitation programs.Objective. The aim of the study was to determine individual timely sensitive ontogenesis stages («abilitation windows») for premature children with various perinatal pathology, as well as the most effective methods of rehabilitation treatment according to the maturity of the child and the type of neurosomatic deficiency.Methods. Two-stage cohort study of premature children with gestational age of 250–6–300–6 weeks was conducted. Stage 1 — retrospective analysis of «abilitation windows» in 115 premature children, development of prognostic scenarios. Stage 2 — prospective cohort study of 109 premature children, using abilitation methods in this cohort according to predicted events and estimation of abilitation efficacy. Medical interventions: familyoriented educational counseling, kinesiotherapy, physiotherapy, neuroprotectors, music therapy.Results. Information matrixes on filling ontogenetic abilitation periods have been developed, as well as age limits for «abilitation windows» have been defined on the 1st stage. Sensitive interval has been established for the use of stimulating effects in extremely immature children (postconceptional age of 34–37 weeks). The leading role of abilitation protective measures has been identified. Perinatal pathology outcomes have been analyzed in children from retrospective analysis group. Direct abilitation of premature children (from birth to 18 months) was carried out on the 2nd stage of the study according to probability forecasts. The priority of non-drug abilitation methods usage in immature children was confirmed. The need in step-by-step dynamic correction of abilitation programs was established.Conclusion. Prognostic significance of perinatal period events and individual postconceptional age in «abilitation windows» development for premature children was determined. The priority of non-drug abilitation for such patients (including family-oriented pedagogics) is established.

Perinatal pathologies and foremost perinatal central nervous system injury are attended with delayed neurosomatic disorders frequently leading to disability. The success of realisation of individual compensation potential of every child depends on dynamics analysis of patient's condition on every survey stage, personalized choice of necessary and sufficient methods of polysystemic diseases correction. The contemporary view on ethiology and pathogenesis of main perinatal pathologies are presented in this review, main mechanisms of its development are also revealed. Recent non-invasive diagnostic technique allow to perform correction of vital signs and prevent cascade reactions of neuronal apoptosis. The sanogenetic importance of some therapeutic interventions (therapeutic hypothermia, erythropoietin, caffeine) is established in early neonatal period. The role of implementation of external factors causing epigenetic effects is clear: optimal nutrition, correction of intestinal microbiota, sensory and motional stimulation. The abilitation success depends largely on successive team activity of specialists and their collaboration with the family. Methods of multifactorial statistical analysis allow not only to prognosticate the outcomes of perinatal affection, but also to create effective mathematical models for personalized choice of facilitation measures.

This paper presents the results of studying the effectiveness of the developed method for diagnosing the functional state of the dental system. The results indicate that the accuracy of the method is about 80%. The results of previous studies are reviewed and the prospects for diagnosis of the functional state of the dental system as a research and practical trend in dentistry are outlined. The physiological regularities of the dental system functioning and relationship between its functional status and the autonomic nervous and postural systems prompt a new trend of research, functional diagnosis in dentistry and interpretation of the findings of functional studies. The prospects of functional diagnosis in dentistry are discussed.

This paper describes the creation of a knowledge base for an intelligent healthcare management system dealing with cases of stroke, myocardial infarction and depression. The main purpose of the knowledge base is to discover and evaluate risk factors and situations which can lead to such diseases, and to enable the formation and support of a preventative measures plan. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual’s health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user’s condition. All of the data obtained is accumulated in a single database. The knowledge base establishes risk factors, including changes in those factors over the course of time, and circumstances or events which might precipitate the emergence of pathology. Hypotheses are generated about the current state of the user’s health, the active risk factors which created conditions for the onset of disease, and circumstances which might produce an increase or decrease in risk factors. Prophylactic measures to reduce those risks are suggested through analysis of the hypotheses generated. Recommendations regarding prophylactic measures are formed with the aid of the knowledge base, the user case-library, and collaborative filtering methods. Recommendations are based on 4P medicine, which re-quires mandatory participation of system users in maintaining their health.

The paper describes knowledge base principles and method for disease risk evaluation that were used for intelligent healthcare management system creation. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual’s health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user’s condition. All of the data obtained is accumulated in a single database. The method for risk evaluation and preventive measures plan hypotheses generation is based on an argumentation reasoning algorithm that is modified to the task at hand. All prevention recommendations are based on the principles of P4 medicine. The current version of the system is based on expert knowledge obtained by automated monitoring and analysis of a large number of publications and recommendations on this topic.

Cell-based immunotherapy is a promising approach for the treatment of chronic infections, autoimmune disorders, and malignant tumors. There are many strategies of cell-based immunotherapy of cancer; these include injection of various immune effector cells, propagated and «trained» in a cell culture. Alternatively, cells presenting tumor antigens on their surface in a form recognized by the immune system can be used to achieve a therapeutic effect. The research results in this field are presented in thousands of texts, and their manual analysis is very complicated. We have developed an approach for automated text analysis in this area of biomedical science. Here we present the first results of the automated analysis of the data extracted from abstracts of scientific articles available in PubMed. These results demonstrate the associations between types of tumors and the most commonly used methods of their cell-based immunotherapy., Клеточная иммунотерапия это перспективный подход к лечению хронических инфекций, аутоиммунных нарушений и злокачественных опухолей. Существует множество стратегий иммунотерапии рака, включая инъекции различных иммунных эффекторных клеток, размноженных и «обученных» в клеточной культуре. В качестве альтернативы для достижения терапевтического эффекта могут быть использованы клетки, представляющие опухолевый антиген на своей поверхности в «понятном» для иммунной системы виде. Результаты исследований в этой области представлены в тысячах текстов, ручной анализ которых затруднен. Мы разработали подход для автоматического анализа текстов в этой области биомедицинской науки. В данной работе мы представляем первые результаты автоматического анализа данных, извлеченных из абстрактов научных статей, доступных в PubMed. На корпусе извлеченных текстов мы демонстрируем ассоциации между типами опухолей и наиболее часто используемыми способами клеточной терапии.