УДК: 616.89:[616.988:578.834.1]-036

   As the COVID-19 pandemic progresses, the observed increase in mental health issues requires more and more clinical attention. Mental disorders have become a major cause for disturbances in social adjustment, primarily due to disorders that fall into three clusters: prolonged fatigue (asthenia) with cognitive impairment; anxiety disorders with sleep disorders; and depression. The last two are also found in individuals who have not contracted SARSCoV-2; they are seen as a result of their exposure to the stress of the pandemic. Therefore, to successfully manage the consequences of the pandemic, it is necessary to develop a cohesive clinical interpretation of mental disorders related to COVID-19 infection. Our proposed model would encompass all the above manifestations as follows: а) for the general population — by the triad of ‘nosogenic reactions’ with excessive (hyper-), normal (normo-) or ignoring (hyponosognostic) psychological responses to stress related to the semantics and individual signifi cance of the SARS-CoV-2 diagnosis (nosos); b) for long COVID — by the biopsychosocial model as a typical combination of neurotoxic asthenia with cognitive impairment (Bonhoeff er’s neurobiological factor) that exacerbates ‘nosogenic’ anxiety and sleep disorders (psychological factor) and thus provokes a depressive response (as a social maladaptive factor).

   З прогресуванням пандемії COVID-19 спостерігається зростання проблем з психічним здоров’ям, що вимагає дедалі більшої уваги клініцистів. Саме ці проблеми призводять до основних порушень соціальної адаптації, передусім через розлади, розподілені на три «кластери»: тривалу астенію (втому) з когнітивними порушеннями, тривожні розлади з розладами сну та депресії. Останні виявляються також в осіб, які не хворіли на SARS-CoV-2, та оцінюються як результат впливу стресу внаслідок пандемії. Щоб успішно подолати наслідки пандемії необхідно випрацювати цілісну клінічну інтерпретацію психічних розладів, пов’язаних із коронавірусною інфекцією COVID-19. Запропонована у нашому дослідженні модель охопила б усі вищезазначені прояви в такий спосіб: а) для загальної популяції — тріадою «нозогенних реакцій» з надмірним (гіпер-), нормальним (нормо-) або іґноруючим (гіпо-нозогностичним) психологічним реагуванням на стрес, пов’язаний зі смислом та особистою значимістю діагнозу «SARS-CoV-2» (нозосом); б) для лонг COVID: біопсихосоціальною моделлю, як типовою комбінацію нейротоксичної астенії з когнітивними порушеннями за К. Бонгофером (нейробіологічний фактор), яка посилює «нозогенну» тривогу і порушення сну (психологічний фактор), що в низці випадків провокує депресію (як фактор соціальної дезадаптації).

УДК: 616.89-008.441.14: 616.716.8-018.46-002]-036-099

   Appropriate surgical tactics and pathogenetic therapy are of great importance for toxic osteomyelitis in drug addicts. Therefore, in our study, with long-term integrated treatment, systemic and pathogenetically conditioned drug therapy was used, and appropriate surgical treatment, aimed at removal of necrotized areas of the mandibular bone, was performed. We believe that due to the treatment, despite the total destruction of the mandible, it was possible to stop the destructive bone processes and to preserve life for such a patient and to create conditions for subsequent reconstructive operations. The treatment was aimed at reducing the disability of patients with the persistent rejection of substance abuse.

The authors of the article consider decelerating structures made of homogeneous material, which have a periodic structure in space. Such systems are used to concentrate the energy of high-frequency electromagnetic waves in order to increase the sensitivity of devices designed for their registration, increase the efficiency of the interaction of a beam of free electrons with a slowed electromagnetic field, for the manufacture of structural elements in waveguide devices, and for generators of monochromatic radiation in the terahertz and optical ranges (effect Vavilov-Cherenkov radiation). The parameters of the wave process are studied on the basis of an exact analytical solution based on the cylindrical Bessel and Hankel functions for a decelerating system with axial symmetry in the form of a spiral. To obtain numerical solutions, the optimization problem of the system of nonlinear equations of a complex variable is solved. The conducted studies establish the relationship between the transverse and longitudinal wave numbers and the attenuation coefficient of the electromagnetic wave. A detailed analysis of the solutions of the equation showed that, in addition to the classical solution that determines the surface wave, other solutions are possible for which the concentration of the electromagnetic field inside the structure is higher.

The conducted studies related to the substantiation of the knowledge base, which ensures the prediction of the duration of inpatient treatment of diabetes in children based on the use of the developed neural network model of direct communication. The paper proposes an approach and prepares data for predicting the duration of inpatient treatment of diabetes in children. The substantiation of the parameters of the feedforward neural network model for predicting the course of inpatient treatment of diabetes was performed, and the accuracy indicators of the proposed model were also evaluated. The proposed approach to predicting the duration of inpatient treatment of diabetes in children is based on the use of forward propagation neural networks and involves implementing nine stages. The peculiarity of this approach is that the formation of databases and knowledge is carried out based on taking into account the peculiarities of inpatient treatment projects. It is based on a computer analysis of historical data and involves modeling, which provides a systematic consideration of the relationships between factors and the duration of inpatient treatment of diabetes in children.
Based on the use of the developed approach, as well as using the prepared data, the parameters of the neural network model of direct communication for predicting the duration of inpatient treatment of diabetes in children are substantiated. The accuracy indicators of the model were evaluated. The proposed rational feedforward neural network model involves two layers (the first is the Dense type with 64 neurons and the ReLU activation function, and
the second is the Dense type and 1 neuron). The total number of model parameters is 385. In the proposed model, the learning rate is 0.0001.