40 research outputs found
ΠΠ ΠΠΠΠΠΠ« ΠΠΠ’Π‘ΠΠΠ ΠΠΠΠΠΠΠΠΠΠ‘Π’Π Π Π‘ΠΠΠ ΠΠΠΠΠΠΠ Π ΠΠ‘Π‘ΠΠ
Β Creation of system of early prophylaxis of children disability and support of the families bringing up disabled children and children with limited opportunities are among the main priorities of the Russian Federation state social policy. There are a number of problems requiring immediate solutions. Dynamics of childrenβs disability in our country is characterized by process stagnation. The age and gender structure of childrenβs disability practically doesnβt change. The analysis of its nosological structure shows that alienations and disorders of behavior, illness of a nervous system and congenital anomalies of development steadily occupy more than 60% among the illnesses which caused disability of children of all age groups. There was a decrease in the prevalence of total disability in most classes of diseases, such as injuries, diseases of the genitourinary system, respiratory system, musculoskeletal system, digestive system and growth of disability caused by neoplasms and diseases of the endocrine system. The underestimation of childrenβs disability bound to various reasons is supposed: social motivation of a family, complexity of legal veneering, strict requirements of service of medico-social examination, insufficient medical experts awareness on criteria of disability. Among disability formations risk factors the most discussed are the achievements of perinatology leading to improvement of nursing of prematurely born and small newborns, and wide uses of auxiliary genesial technologies. An important part of all preventive measures aimed at reducing the genetic load of population is prenatal and preimplantation diagnosis. It seems appropriate to extend the screening to congenital and hereditary metabolic diseases in neonatal period, including the most common nosological forms of infrequent illnesses. In solving problems of childhood disability prevention a priority should be given to development of services of family planning; improving antenatal and perinatal care; preventive work with healthy but having deviations in development children; development of medical genetic services; implementation of programs of different types of pathology screening.Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΡΠΈΡΡΠ΅ΠΌΡ ΡΠ°Π½Π½Π΅ΠΉ ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΠΊΠΈ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ Ρ Π΄Π΅ΡΠ΅ΠΉ ΠΈ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠ° ΡΠ΅ΠΌΠ΅ΠΉ, Π²ΠΎΡΠΏΠΈΡΡΠ²Π°ΡΡΠΈΡ
Π΄Π΅ΡΠ΅ΠΉ-ΠΈΠ½Π²Π°Π»ΠΈΠ΄ΠΎΠ² ΠΈ Π΄Π΅ΡΠ΅ΠΉ Ρ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΡΠΌΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ, Π²Ρ
ΠΎΠ΄ΡΡ Π² ΡΠΈΡΠ»ΠΎ ΠΎΡΠ½ΠΎΠ²Π½ΡΡ
ΠΏΡΠΈΠΎΡΠΈΡΠ΅ΡΠΎΠ² Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π ΠΎΡΡΠΈΠΉΡΠΊΠΎΠΉ Π€Π΅Π΄Π΅ΡΠ°ΡΠΈΠΈ. Π‘ΡΡΠ΅ΡΡΠ²ΡΠ΅Ρ ΡΡΠ΄ ΠΏΡΠΎΠ±Π»Π΅ΠΌ, ΡΡΠ΅Π±ΡΡΡΠΈΡ
Π½Π΅Π·Π°ΠΌΠ΅Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΡΠ΅ΡΠ΅Π½ΠΈΡ. Π£ΡΠΎΠ²Π΅Π½Ρ Π΄Π΅ΡΡΠΊΠΎΠΉ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ Π² Π½Π°ΡΠ΅ΠΉ ΡΡΡΠ°Π½Π΅ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅ΡΡΡ ΡΡΠ°Π³Π½Π°ΡΠΈΠ΅ΠΉ ΠΏΡΠΎΡΠ΅ΡΡΠ°. ΠΠΎΠ·ΡΠ°ΡΡΠ½Π°Ρ ΠΈ Π³Π΅Π½Π΄Π΅ΡΠ½Π°Ρ ΡΡΡΡΠΊΡΡΡΠ° Π΄Π΅ΡΡΠΊΠΎΠΉ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ ΠΌΠ΅Π½ΡΠ΅ΡΡΡ. ΠΠ½Π°Π»ΠΈΠ· Π΅Π΅ Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΡΡΠΎ ΠΏΡΠΈΡ
ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΠ°ΡΡΡΡΠΎΠΉΡΡΠ²Π° ΠΈ ΡΠ°ΡΡΡΡΠΎΠΉΡΡΠ²Π° ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΠΈΡ, Π±ΠΎΠ»Π΅Π·Π½ΠΈ Π½Π΅ΡΠ²Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈ Π²ΡΠΎΠΆΠ΄Π΅Π½Π½ΡΠ΅ Π°Π½ΠΎΠΌΠ°Π»ΠΈΠΈ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΡΠ°Π±ΠΈΠ»ΡΠ½ΠΎ ΡΠΎΡΡΠ°Π²Π»ΡΡΡ Π±ΠΎΠ»Π΅Π΅ 60% ΡΡΠ΅Π΄ΠΈ Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ, ΠΎΠ±ΡΡΠ»ΠΎΠ²ΠΈΠ²ΡΠΈΡ
ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΡ Π΄Π΅ΡΠ΅ΠΉ Π²ΡΠ΅Ρ
Π²ΠΎΠ·ΡΠ°ΡΡΠ½ΡΡ
Π³ΡΡΠΏΠΏ. ΠΡΠΎΠΈΠ·ΠΎΡΠ»ΠΎ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΎΠ±ΡΠ΅ΠΉ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ ΠΏΠΎ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Ρ ΠΊΠ»Π°ΡΡΠΎΠ² Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ (ΠΏΡΠΈ ΡΡΠ°Π²ΠΌΠ°Ρ
, Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΡ
ΠΌΠΎΡΠ΅ΠΏΠΎΠ»ΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ, ΠΊΠΎΡΡΠ½ΠΎ-ΠΌΡΡΠ΅ΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ, ΠΎΡΠ³Π°Π½ΠΎΠ² ΠΏΠΈΡΠ΅Π²Π°ΡΠ΅Π½ΠΈΡ) ΠΈ ΡΠΎΡΡ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΠΎΠΉ Π½ΠΎΠ²ΠΎΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ ΠΈ Π±ΠΎΠ»Π΅Π·Π½ΡΠΌΠΈ ΡΠ½Π΄ΠΎΠΊΡΠΈΠ½Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΡΠ΅Π΄ΠΏΠΎΠ»Π°Π³Π°Π΅ΡΡΡ Π½Π΅Π΄ΠΎΡΡΠ΅Ρ Π΄Π΅ΡΡΠΊΠΎΠΉ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ, ΡΠ²ΡΠ·Π°Π½Π½ΡΠΉ Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΠΏΡΠΈΡΠΈΠ½Π°ΠΌΠΈ, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ Ρ ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΌΠΎΡΠΈΠ²ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΡΡΡΡ ΡΠ΅ΠΌΡΠΈ, ΡΠ»ΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ ΡΡΠΈΠ΄ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΡΠΎΡΠΌΠ»Π΅Π½ΠΈΡ, ΠΆΠ΅ΡΡΠΊΠΈΠΌΠΈ ΡΡΠ΅Π±ΠΎΠ²Π°Π½ΠΈΡΠΌΠΈ ΡΠ»ΡΠΆΠ±Ρ ΠΌΠ΅Π΄ΠΈΠΊΠΎ-ΡΠΎΡΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΊΡΠΏΠ΅ΡΡΠΈΠ·Ρ, Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΠΉ ΠΎΡΠ²Π΅Π΄ΠΎΠΌΠ»Π΅Π½Π½ΠΎΡΡΡΡ ΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡΡ
ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΡ
ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠΎΠ². Π‘ΡΠ΅Π΄ΠΈ ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠΈΡΠΊΠ° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΎΠ±ΡΡΠΆΠ΄Π°Π΅ΠΌΡΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ Π΄ΠΎΡΡΠΈΠΆΠ΅Π½ΠΈΡ ΠΏΠ΅ΡΠΈΠ½Π°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΠΏΡΠΈΠ²ΠΎΠ΄ΡΡΠΈΠ΅ ΠΊ ΡΠ»ΡΡΡΠ΅Π½ΠΈΡ Π²ΡΡ
Π°ΠΆΠΈΠ²Π°Π½ΠΈΡ Π½Π΅Π΄ΠΎΠ½ΠΎΡΠ΅Π½Π½ΡΡ
ΠΈ ΠΌΠ°Π»ΠΎΠ²Π΅ΡΠ½ΡΡ
Π½ΠΎΠ²ΠΎΡΠΎΠΆΠ΄Π΅Π½Π½ΡΡ
, ΠΈ ΡΠΈΡΠΎΠΊΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ Π²ΡΠΏΠΎΠΌΠΎΠ³Π°ΡΠ΅Π»ΡΠ½ΡΡ
ΡΠ΅ΠΏΡΠΎΠ΄ΡΠΊΡΠΈΠ²Π½ΡΡ
ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ. ΠΠ°ΠΆΠ½ΠΎΠΉ ΡΠ°ΡΡΡΡ Π²ΡΠ΅Ρ
ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅ΡΠΎΠΏΡΠΈΡΡΠΈΠΉ, Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½Π½ΡΡ
Π½Π° ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π³ΡΡΠ·Π° ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ, ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠ΅Π½Π°ΡΠ°Π»ΡΠ½Π°Ρ ΠΈ ΠΏΡΠ΅ΠΈΠΌΠΏΠ»Π°Π½ΡΠ°ΡΠΈΠΎΠ½Π½Π°Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ°. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅ΡΡΡ ΡΠ΅Π»Π΅ΡΠΎΠΎΠ±ΡΠ°Π·Π½ΡΠΌ ΡΠ°ΡΡΠΈΡΠ΅Π½ΠΈΠ΅ ΡΠΊΡΠΈΠ½ΠΈΠ½Π³Π° Π½Π° Π²ΡΠΎΠΆΠ΄Π΅Π½Π½ΡΠ΅ ΠΈ Π½Π°ΡΠ»Π΅Π΄ΡΡΠ²Π΅Π½Π½ΡΠ΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΎΠ±ΠΌΠ΅Π½Π° Π² Π½Π΅ΠΎΠ½Π°ΡΠ°Π»ΡΠ½ΠΎΠΌ ΠΏΠ΅ΡΠΈΠΎΠ΄Π΅, Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ Π² Π½Π΅Π³ΠΎ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΡΡ
Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠΎΡΠΌ ΡΠ΅Π΄ΠΊΠΈΡ
Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ. Π ΡΠ΅ΡΠ΅Π½ΠΈΠΈ ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΠΊΠΈ Π΄Π΅ΡΡΠΊΠΎΠΉ ΠΈΠ½Π²Π°Π»ΠΈΠ΄Π½ΠΎΡΡΠΈ ΡΠ»Π΅Π΄ΡΠ΅Ρ ΠΎΡΠ΄Π°Π²Π°ΡΡ ΠΏΡΠΈΠΎΡΠΈΡΠ΅Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ»ΡΠΆΠ± ΠΏΠ»Π°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π΄Π΅ΡΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΡ, ΡΠΎΠ²Π΅ΡΡΠ΅Π½ΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ Π°Π½ΡΠ΅Π½Π°ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΈ ΠΏΠ΅ΡΠΈΠ½Π°ΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ, ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ°Π±ΠΎΡΠ΅ ΡΠΎ Π·Π΄ΠΎΡΠΎΠ²ΡΠΌΠΈ Π΄Π΅ΡΡΠΌΠΈ, Π½ΠΎ ΠΈΠΌΠ΅ΡΡΠΈΠΌΠΈ ΠΎΡΠΊΠ»ΠΎΠ½Π΅Π½ΠΈΡ Π² ΡΠ°Π·Π²ΠΈΡΠΈΠΈ, Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΡ ΡΠΊΡΠΈΠ½ΠΈΡΡΡΡΠΈΡ
ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Π½Π° ΡΠ°Π·Π½ΡΠ΅ Π²ΠΈΠ΄Ρ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ, ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΌΠ΅Π΄ΠΈΠΊΠΎ-Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ»ΡΠΆΠ±Ρ
Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ Π΄Π°Π½Π½ΡΡ
The paper presents the system for intelligent analysis of clinical information. Authors describe methods implemented in the system for clinical information retrieval, intelligent diagnostics of chronic diseases, patientβs features importance and for detection of hidden dependencies between features. Results of the experimental evaluation of these methods are also presented.Background: Healthcare facilities generate a large flow of both structured and unstructured data which contain important information about patients. Test results are usually retained as structured data but some data is retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations, such as ultrasound, ECG or X-ray studies). Many tasks arising in clinical practice can be automated applying methods for intelligent analysis of accumulated structured array and unstructured data that leads to improvement of the healthcare quality.Aims: the creation of the complex system for intelligent data analysis in the multi-disciplinary pediatric center.Materials and methods: Authors propose methods for information extraction from clinical texts in Russian. The methods are carried out on the basis of deep linguistic analysis. They retrieve terms of diseases, symptoms, areas of the body and drugs. The methods can recognize additional attributes such as Β«negationΒ» (indicates that the disease is absent), Β«no patientΒ» (indicates that the disease refers to the patientβs family member, but not to the patient), Β«severity of illnessΒ», Β«disease courseΒ», Β«body region to which the disease refersΒ». Authors use a set of hand-drawn templates and various techniques based on machine learning to retrieve information using a medical thesaurus. The extracted information is used to solve the problem of automatic diagnosis of chronic diseases. A machine learning method for classification of patients with similar nosology and the method for determining the most informative patientsβ features are also proposed.Results: Authors have processed anonymized health records from the pediatric center to estimate the proposed methods. The results show the applicability of the information extracted from the texts for solving practical problems. The records of patients with allergic, glomerular and rheumatic diseases were used for experimental assessment of the method of automatic diagnostic. Authors have also determined the most appropriate machine learning methods for classification of patients for each group of diseases, as well as the most informative disease signs. It has been found that using additional information extracted from clinical texts, together with structured data helps to improve the quality of diagnosis of chronic diseases. Authors have also obtained pattern combinations of signs of diseases.Conclusions: The proposed methods have been implemented in the intelligent data processing system for a multidisciplinary pediatric center. The experimental results show the availability of the system to improve the quality of pediatric healthcare.Β ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅. ΠΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΠ΅ ΡΡΡΠ΅ΠΆΠ΄Π΅Π½ΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΡΡΡ Π±ΠΎΠ»ΡΡΠΎΠΉ ΠΏΠΎΡΠΎΠΊ ΠΊΠ°ΠΊ ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
, ΡΠ°ΠΊ ΠΈ Π½Π΅ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΡ
Π²Π°ΠΆΠ½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°Ρ
. Π ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΌ Π²ΠΈΠ΄Π΅, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, Ρ
ΡΠ°Π½ΡΡΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΎΠ², ΠΎΠ΄Π½Π°ΠΊΠΎ ΠΏΠΎΠ΄Π°Π²Π»ΡΡΡΠ΅Π΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ Π΄Π°Π½Π½ΡΡ
Ρ
ΡΠ°Π½ΠΈΡΡΡ Π² Π½Π΅ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ ΡΠΎΡΠΌΠ΅ Π² Π²ΠΈΠ΄Π΅ ΡΠ΅ΠΊΡΡΠΎΠ² Π½Π° Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΡΠ·ΡΠΊΠ΅ (Π°Π½Π°ΠΌΠ½Π΅Π·Ρ, ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΎΡΠΌΠΎΡΡΠΎΠ², ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ Π£ΠΠ, ΠΠΠ, ΡΠ΅Π½ΡΠ³Π΅Π½ΠΎΠ²ΡΠΊΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ Π΄Ρ.). ΠΡΠΏΠΎΠ»ΡΠ·ΡΡ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π½Π°ΠΊΠΎΠΏΠ»Π΅Π½Π½ΡΡ
ΠΌΠ°ΡΡΠΈΠ²ΠΎΠ² ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈ Π½Π΅ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΌΠΎΠΆΠ½ΠΎ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΌΠ½ΠΎΠ³ΠΈΡ
Π·Π°Π΄Π°Ρ, Π²ΠΎΠ·Π½ΠΈΠΊΠ°ΡΡΠΈΡ
Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΠΈ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ:Β ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΡΠΎΡΠΈΠ»ΡΠ½ΠΎΠΌ ΠΏΠ΅Π΄ΠΈΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅Π½ΡΡΠ΅.ΠΠ΅ΡΠΎΠ΄Ρ. ΠΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΠ΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠΎΠ² Π½Π° ΡΡΡΡΠΊΠΎΠΌ ΡΠ·ΡΠΊΠ΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΠΎΠ»Π½ΠΎΠ³ΠΎ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. ΠΠ·Π²Π»Π΅ΠΊΠ°ΡΡΡΡ ΡΠΏΠΎΠΌΠΈΠ½Π°Π½ΠΈΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, ΡΠΈΠΌΠΏΡΠΎΠΌΠΎΠ², ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ ΡΠ΅Π»Π°, Π»Π΅ΠΊΠ°ΡΡΡΠ²Π΅Π½Π½ΡΡ
ΠΏΡΠ΅ΠΏΠ°ΡΠ°ΡΠΎΠ². Π ΡΠ΅ΠΊΡΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΡΠ°ΡΠΏΠΎΠ·Π½Π°ΡΡΡΡ Π°ΡΡΠΈΠ±ΡΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ: Β«ΠΎΡΡΠΈΡΠ°Π½ΠΈΠ΅Β» (ΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π½Π° ΡΠΎ, ΡΡΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅ ΠΎΡΡΡΡΡΡΠ²ΡΠ΅Ρ), Β«Π½Π΅ ΠΏΠ°ΡΠΈΠ΅Π½ΡΒ» (ΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ Π½Π° ΡΠΎ, ΡΡΠΎ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅ ΠΎΡΠ½ΠΎΡΠΈΡΡΡ Π½Π΅ ΠΊ ΠΏΠ°ΡΠΈΠ΅Π½ΡΡ, Π° ΠΊ Π΅Π³ΠΎ ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΠΈΠΊΡ), Β«ΡΡΠΆΠ΅ΡΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΒ», Β«ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΒ», Β«ΠΎΠ±Π»Π°ΡΡΡ ΡΠ΅Π»Π°, ΠΊ ΠΊΠΎΡΠΎΡΠΎΠΉ ΠΎΡΠ½ΠΎΡΠΈΡΡΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠ΅Β». ΠΠ»Ρ ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΈΠ΅ ΡΠ΅Π·Π°ΡΡΡΡΡ, Π½Π°Π±ΠΎΡ Π²ΡΡΡΠ½ΡΡ ΡΠΎΡΡΠ°Π²Π»Π΅Π½Π½ΡΡ
ΡΠ°Π±Π»ΠΎΠ½ΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ ΠΈΠ· ΡΠ΅ΠΊΡΡΠΎΠ² Π΄Π°Π½Π½ΡΠ΅ ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΡΡΡΡ Π΄Π»Ρ ΡΠ΅ΡΠ΅Π½ΠΈΡ Π·Π°Π΄Π°ΡΠΈ Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ ΠΌΠ΅ΡΠΎΠ΄ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠΎ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠΌΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΌΠ΅ΡΠΎΠ΄ Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ
ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ².Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ Π½Π° ΠΎΠ±Π΅Π·Π»ΠΈΡΠ΅Π½Π½ΡΡ
ΠΈΡΡΠΎΡΠΈΡΡ
Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΏΠ΅Π΄ΠΈΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ΅Π½ΡΡΠ°. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΈΠ· ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠΎΠ² Π½Π° ΡΡΡΡΠΊΠΎΠΌ ΡΠ·ΡΠΊΠ΅. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΠ°Π»ΡΠ½Π°Ρ ΠΎΡΠ΅Π½ΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄Π° Π°Π²ΡΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π½Π° Π΄Π°Π½Π½ΡΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π°Π»Π»Π΅ΡΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΠΌΠΈ ΠΈ Π±ΠΎΠ»Π΅Π·Π½ΡΠΌΠΈ ΠΎΡΠ³Π°Π½ΠΎΠ² Π΄ΡΡ
Π°Π½ΠΈΡ, Π½Π΅ΡΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΠΈ ΡΠ΅Π²ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΠΌΠΈ. ΠΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ Π³ΡΡΠΏΠΏΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠ΅ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΈ. ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ Π΄Π°Π½Π½ΡΡ
, ΠΈΠ·Π²Π»Π΅ΡΠ΅Π½Π½ΡΡ
ΠΈΠ· ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ΅ΠΊΡΡΠΎΠ² ΡΠΎΠ²ΠΌΠ΅ΡΡΠ½ΠΎ ΡΠΎ ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΏΠΎΠ²ΡΡΠΈΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Π»ΠΈΡΡ Π΄ΠΎΡΡΡΠΏΠ½ΡΡ
ΡΡΡΡΠΊΡΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
. ΠΠΎΠ»ΡΡΠ΅Π½Ρ ΡΠ°ΠΊΠΆΠ΅ ΡΠ°Π±Π»ΠΎΠ½Π½ΡΠ΅ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΈ ΠΏΡΠΈΠ·Π½Π°ΠΊΠΎΠ² Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π Π°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π±ΡΠ»ΠΈ ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½Ρ Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΠΎΠΉ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΡΠΎΡΠΈΠ»ΡΠ½ΠΎΠΌ ΠΏΠ΅Π΄ΠΈΠ°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ΅Π½ΡΡΠ΅. ΠΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ ΠΎ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌΡ Π΄Π»Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΡ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΌΠ΅Π΄ΠΈΡΠΈΠ½ΡΠΊΠΎΠΉ ΠΏΠΎΠΌΠΎΡΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌ Π΄Π΅ΡΡΠΊΠΎΠΉ Π²ΠΎΠ·ΡΠ°ΡΡΠ½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ
ΠΠ΅Π½Π΄Π΅ΡΠ½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΡ ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠΈΡΠΊΠ° Ρ ΠΆΠΈΡΠ΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π°
Background: In developed countries there are significant gender differences in lifetime expectancy that can be explained by behavioral risk factorsΒ (RF).Objective: The aim of our study was to estimate gender features of behavioral RF in general population of Saint-Petersburg, Russia.Methods:Β As a part of all-Russian epidemiology survey ESSE-RF a random sampling of 1600 Saint-Petersburg inhabitants (25-64 y.o.) stratified by age andΒ sex was performed. All participants filled in the questionnaire. Anthropometry (weight, height, body-mass index (BMI), waist circumference (WC))Β and fasting blood-tests (lipids, glucose by Abbott Architect 8000 (USA)) were performed.Results: There were examined 573 (36%) men and 1027Β (64%) women. No gender differences in obesity were found according to BMI criteria β in 178 (31.2%) women and 352 (35.1%) men. ObesityΒ was more often detected in females according to WC criteria: ΠΠ’Π III β 44.1 vs 30.3%; IDF 51.2 vs 66.4% (p 0.001 for both). Linear regressionΒ analysis was performed and age was associated with BMI β 1.6 kg/m2/decade, WC in women β 5,2 cm/decade and WC in men β 2.8 cm/decade,Β Ρ 0.001 for all anthropometric parameters. Optimal level of physical activity was equally documented in both genders β 540 (61.2%) women andΒ 286 (58.9%) men. Daily intake of sweets was lower in men β 228 (39.8%) vs 539 (52.5%) in women (p 0.001). 810 (50,6%) of trial subjects wereΒ non-smokers, 395 (24,7%) were former smokers, and 395 (24,7%) were smokers at the moment of trial. The higher number of female smokersΒ was observed β 194 (19.1%).Conclusion: A high prevalence of obesity is observed in sample of Saint-Petersburg inhabitants β it is higher amongΒ women according to WC criteria regardless of menopause, possibly due to bigger sweets consumption. Males smoke more often and consume lessΒ fresh fruits and vegetables which is accompanied by a higher prevalence of hyperglycemia and hypertriglyceridemia.Π ΡΠ°Π·Π²ΠΈΡΡΡ
ΡΡΡΠ°Π½Π°Ρ
ΠΎΡΠΌΠ΅ΡΠ°ΡΡΡΡ Π³Π΅Π½Π΄Π΅ΡΠ½ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ Π² ΠΎΠΆΠΈΠ΄Π°Π΅ΠΌΠΎΠΉ ΠΏΡΠΎΠ΄ΠΎΠ»ΠΆΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ ΠΆΠΈΠ·Π½ΠΈ, ΡΡΠΎ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΠ±ΡΡΡΠ½Π΅Π½ΠΎ ΠΏΡΠΎΡΠΈΠ»Π΅ΠΌΒ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠΈΡΠΊΠ°.Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΈΠ·ΡΡΠΈΡΡ Π³Π΅Π½Π΄Π΅ΡΠ½ΡΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΏΡΠΎΡΠΈΠ»Ρ ΠΏΠΎΠ²Π΅Π΄Π΅Π½ΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎ-ΡΠΎΡΡΠ΄ΠΈΡΡΠΎΠ³ΠΎ ΡΠΈΡΠΊΠ° Π² ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΆΠΈΡΠ΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π°. ΠΠ΅ΡΠΎΠ΄Ρ: Π² ΡΠ°ΠΌΠΊΠ°Ρ
ΠΌΠ½ΠΎΠ³ΠΎΡΠ΅Π½ΡΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΏΠΈΠ΄Π΅ΠΌΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π½Π°Π±Π»ΡΠ΄Π°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎΒ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΠ‘Π‘Π-Π Π€ Π±ΡΠ»Π° ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° ΡΠ»ΡΡΠ°ΠΉΠ½Π°Ρ Π²ΡΠ±ΠΎΡΠΊΠ° ΠΈΠ· ΠΆΠΈΡΠ΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π°, ΡΡΡΠ°ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΠΎ ΠΏΠΎΠ»Ρ ΠΈ Π²ΠΎΠ·ΡΠ°ΡΡΡ. Π£ΡΠ°ΡΡΠ½ΠΈΠΊΠΈ Π·Π°ΠΏΠΎΠ»Π½ΠΈΠ»ΠΈ ΡΡΠ°Π½Π΄Π°ΡΡΠ½ΡΠΉ ΠΎΠΏΡΠΎΡΠ½ΠΈΠΊ, Π±ΡΠ»Π° Π²ΡΠΏΠΎΠ»Π½Π΅Π½Π° Π°Π½ΡΡΠΎΠΏΠΎΠΌΠ΅ΡΡΠΈΡ: ΡΠΎΡΡ, Π²Π΅Ρ, ΠΈΠ½Π΄Π΅ΠΊΡ ΠΌΠ°ΡΡΡ ΡΠ΅Π»Π° (ΠΠΠ’), ΠΎΠΊΡΡΠΆΠ½ΠΎΡΡΡ ΡΠ°Π»ΠΈΠΈ (ΠΠ’). ΠΠ°ΡΠΎΡΠ°ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ Π»ΠΈΠΏΠΈΠ΄Π½ΡΠΉ ΡΠΏΠ΅ΠΊΡΡ, ΡΡΠΎΠ²Π΅Π½Ρ Π³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ 1600 ΡΠ΅Π»ΠΎΠ²Π΅ΠΊ, ΠΈΠ· Π½ΠΈΡ
ΠΌΡΠΆΡΠΈΠ½ 573Β (35,9%), ΠΆΠ΅Π½ΡΠΈΠ½ 1027 (64,1%). ΠΠΆΠΈΡΠ΅Π½ΠΈΠ΅ Ρ ΠΌΡΠΆΡΠΈΠ½ ΠΈ ΠΆΠ΅Π½ΡΠΈΠ½ Π²ΡΡΡΠ΅ΡΠ°Π»ΠΎΡΡ Π² 31β66% ΡΠ»ΡΡΠ°Π΅Π² (ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΠΠ’ β Ρ 31,2% ΠΌΡΠΆΡΠΈΠ½ ΠΈΒ 35,1% ΠΆΠ΅Π½ΡΠΈΠ½; ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΌΠ΅ΡΠ°Π±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° (ΠΠ’Π III) β Ρ 30,3 ΠΈ 44,1%; ΠΏΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ IDF β Ρ 51,2 ΠΈ 66,4%, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ; ΠΏΠΎ ΠΎΠ±ΠΎΠΈΠΌ ΠΊΡΠΈΡΠ΅ΡΠΈΡΠΌ ΠΠ’ Π·Π½Π°ΡΠΈΠΌΠΎ ΡΠ°ΡΠ΅ Π²ΡΡΡΠ΅ΡΠ°Π»Π°ΡΡ Ρ ΠΆΠ΅Π½ΡΠΈΠ½, (p 0,001). ΠΠΈΠ½Π΅ΠΉΠ½ΡΠΉ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΠΎΠ½Π½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡΒ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΡ Π²ΠΎΠ·ΡΠ°ΡΡΠ° Ρ ΠΠΠ’ (1,6 ΠΊΠ³/ΠΌ2 Π½Π° 1 Π΄Π΅ΠΊΠ°Π΄Ρ), Ρ ΠΠ’ Ρ ΠΆΠ΅Π½ΡΠΈΠ½ (5,2 ΡΠΌ/Π΄Π΅ΠΊΠ°Π΄Π°) ΠΈ Ρ ΠΌΡΠΆΡΠΈΠ½ (2,8 ΡΠΌ/Π΄Π΅ΠΊΠ°Π΄Π°; Π΄Π»Ρ Π²ΡΠ΅Ρ
ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉΒ Ρ 0,001). ΠΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΡΠΎΠ²Π΅Π½Ρ Π΄Π²ΠΈΠ³Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ Π°ΠΊΡΠΈΠ²Π½ΠΎΡΡΠΈ Π½Π΅ ΡΠ°Π·Π»ΠΈΡΠ°Π»ΡΡ Ρ ΠΌΡΠΆΡΠΈΠ½ (286; 58,9%) ΠΈ ΠΆΠ΅Π½ΡΠΈΠ½ (540; 61,2%). ΠΠΆΠ΅Π΄Π½Π΅Π²Π½ΠΎΠ΅Β ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΠ΅ ΡΠ»Π°Π΄ΠΎΡΡΠ΅ΠΉ Π·Π½Π°ΡΠΈΠΌΠΎ ΡΠ΅ΠΆΠ΅ ΠΎΡΠΌΠ΅ΡΠ΅Π½ΠΎ Ρ ΠΌΡΠΆΡΠΈΠ½ (228; 39,8%) ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ ΠΆΠ΅Π½ΡΠΈΠ½Π°ΠΌΠΈ (539; 52,5%; Ρ 0,001). ΠΠ΅ ΠΊΡΡΠΈΠ»ΠΈΒ 810 (50,6%), 395 (24,7%) ΠΊΡΡΠΈΠ»ΠΈ Π² ΠΏΡΠΎΡΠ»ΠΎΠΌ ΠΈ 395 (24,7%) ΠΊΡΡΠΈΠ»ΠΈ Π² ΠΌΠΎΠΌΠ΅Π½Ρ ΠΎΠΏΡΠΎΡΠ°; Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΎΡΡ Π±ΠΎΠ»ΡΡΠΎΠ΅ ΡΠΈΡΠ»ΠΎ ΠΊΡΡΡΡΠΈΡ
ΠΆΠ΅Π½ΡΠΈΠ½ β 194Β (19,1%).ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅: ΡΡΠ΅Π΄ΠΈ ΠΆΠΈΡΠ΅Π»Π΅ΠΉ Π‘Π°Π½ΠΊΡ-ΠΠ΅ΡΠ΅ΡΠ±ΡΡΠ³Π° ΡΠ΅Π³ΠΈΡΡΡΠΈΡΡΠ΅ΡΡΡ Π²ΡΡΠΎΠΊΠ°Ρ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΡ (Π·Π½Π°ΡΠΈΠΌΠΎ ΡΠ°ΡΠ΅ ΡΡΠ΅Π΄ΠΈΒ ΠΆΠ΅Π½ΡΠΈΠ½, ΡΠΎΠ³Π»Π°ΡΠ½ΠΎ ΠΊΡΠΈΡΠ΅ΡΠΈΡ ΠΠ’, Π²Π½Π΅ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π½Π°Π»ΠΈΡΠΈΡ ΠΌΠ΅Π½ΠΎΠΏΠ°ΡΠ·Ρ, Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ, Π·Π° ΡΡΠ΅Ρ Π±ΠΎΠ»ΡΡΠ΅Π³ΠΎ ΠΏΠΎΡΡΠ΅Π±Π»Π΅Π½ΠΈΡ ΡΠ»Π°Π΄ΠΊΠΈΡ
ΠΏΡΠΎΠ΄ΡΠΊΡΠΎΠ²).Β ΠΡΠΆΡΠΈΠ½Ρ Π·Π½Π°ΡΠΈΠΌΠΎ Π±ΠΎΠ»ΡΡΠ΅ ΠΊΡΡΡΡ ΠΈ ΡΠ΅ΠΆΠ΅ ΠΏΠΎΡΡΠ΅Π±Π»ΡΡΡ ΡΠ²Π΅ΠΆΠΈΠ΅ ΠΎΠ²ΠΎΡΠΈ ΠΈ ΡΡΡΠΊΡΡ, ΡΡΠΎ ΡΠΎΠΏΡΠΎΠ²ΠΎΠΆΠ΄Π°Π΅ΡΡΡ Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΠ°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡΡΒ Π³ΠΈΠΏΠ΅ΡΠ³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ ΠΈ Π³ΠΈΠΏΠ΅ΡΡΡΠΈΠ³Π»ΠΈΡΠ΅ΡΠΈΠ΄Π΅ΠΌΠΈΠΈ
Precision medicine : steps towards improving treatment with vitamin K antagonists and ACE-inhibitors
A method for research viscoplastic characteristics of materials using a vertical gas-gun stand
A methodology of constructing dynamic strain diagrams based on the method of direct impact on a vertical gas-gun stand has been developed and theoretically verified. The methodology has been verified by a reconstruction of a given static strain diagram when substituting the physical experiment by computer modeling of an axisymmetric problem, accounting for the wave processes in the striker-specimen-measuring rod system. The main inaccuracies in determining (reconstructing) viscoplastic characteristics of materials are shown to be determined by the degree of accuracy of experimental measurements. Based on the method of characteristics, an algorithm has been developed for calculating the displacements of the impacted end of the rod based on the results of the measurements of strains by two gauges in the vicinity of the impacted and supported ends of the rod for a multiply passing strain wave. This makes it possible to analyze the deformation process up to the moment the striker stops and to compare the computational and experimental results based on the residual size of the tested specimen
Experimental and calculated approach to the study of deformation and strength characteristics of elastoviscoplastic materials by direct impact method
It is proposed to develop experimental and calculated approach to the study of the strength characteristics of elastoviscoplastic materials in a non-uniform strain-stress state. Integral characteristics (forces, displacements and displacement speed) of the deformation process of hat-shaped specimens in tension are determined by a direct impact method, and their strain β stress states are determined by numerical solution of the axisymmetric problem. The results of experimental and theoretical study of the deformation and failure of hat-shaped specimens in the presence of stress concentrators are obtained
Experimental and calculated approach to the study of deformation and strength characteristics of elastoviscoplastic materials by direct impact method
It is proposed to develop experimental and calculated approach to the study of the strength characteristics of elastoviscoplastic materials in a non-uniform strain-stress state. Integral characteristics (forces, displacements and displacement speed) of the deformation process of hat-shaped specimens in tension are determined by a direct impact method, and their strain β stress states are determined by numerical solution of the axisymmetric problem. The results of experimental and theoretical study of the deformation and failure of hat-shaped specimens in the presence of stress concentrators are obtained