97 research outputs found
ΠΠ΅Π΄ΠΈΠ°ΠΊΠΎΠ½Π²Π΅ΡΠ³Π΅Π½ΡΠΈΡ ΠΊΠ°ΠΊ ΡΠ°ΠΊΡΠΎΡ ΡΠΎΡΡΠ° ΡΠ»ΡΡΠ°Π΅Π² Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠΉ Π½ΠΎΡΠΌΡ Π² Π‘ΠΠ
In 2009-2017 years E.A. Baranova talked with journalists, editors and top managers of Russian media companies on topics related to the development of media convergence in the newsrooms of Russia mass media. During these years, 86 respondents from 30 media companies were interviewed. Analysis of empirical data and conceptual comprehension of a wide range of works, as well as personal experience in the media allowed the author to conclude that the media convergence process associated with the constant increase in information flows; development of user generated content and the blogosphere; journalism transformation as a types system of professional activity; economic difficulties faced by many media companies leads toreduction of literacy rate for journalists, an incensement number of spelling and stylistic errors in the mass media. The article also presents an analysis of errors in the texts of print and broadcast media. The purpose of the article is to present the problem situation and actualize the need for the subsequent comprehension of the problem of the fall of language media culture in the aspect of axiology.Π 2009-2018 Π³Π³. ΠΎΠ΄ΠΈΠ½ ΠΈΠ· Π°Π²ΡΠΎΡΠΎΠ² ΡΡΠ°ΡΡΠΈ, Π.Π. ΠΠ°ΡΠ°Π½ΠΎΠ²Π°, ΠΏΡΠΎΠ²Π΅Π»Π° 86 Π³Π»ΡΠ±ΠΈΠ½Π½ΡΡ
ΠΈΠ½ΡΠ΅ΡΠ²ΡΡ Ρ ΠΆΡΡΠ½Π°Π»ΠΈΡΡΠ°ΠΌΠΈ, ΡΠ΅Π΄Π°ΠΊΡΠΎΡΠ°ΠΌΠΈ ΠΈ ΡΠΎΠΏ-ΠΌΠ΅Π½Π΅Π΄ΠΆΠ΅ΡΠ°ΠΌΠΈ ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΠΌΠ΅Π΄ΠΈΠ°ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΉ (Π²ΡΠ΅Π³ΠΎ 30 ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΉ) Π½Π° ΡΠ΅ΠΌΡ, ΡΠ²ΡΠ·Π°Π½Π½ΡΠ΅ Ρ ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ΠΌ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΌΠ΅Π΄ΠΈΠ°ΠΊΠΎΠ½Π²Π΅ΡΠ³Π΅Π½ΡΠΈΠΈ Π² ΡΠ΅Π΄Π°ΠΊΡΠΈΡΡ
ΡΠΎΡΡΠΈΠΉΡΠΊΠΈΡ
ΡΡΠ΅Π΄ΡΡΠ² ΠΌΠ°ΡΡΠΎΠ²ΠΎΠΉ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΈ (Π‘ΠΠ). ΠΠ½Π°Π»ΠΈΠ· ΡΠΌΠΏΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΡΠ°Π»ΡΠ½ΠΎΠ΅ ΠΎΡΠΌΡΡΠ»Π΅Π½ΠΈΠ΅ Π½Π°ΡΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ Π»ΠΈΡΠ½ΡΠΉ ΠΎΠΏΡΡ ΡΠ°Π±ΠΎΡΡ Π°Π²ΡΠΎΡΠΎΠ² Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΆΡΡΠ½Π°Π»ΠΈΡΡΠΎΠ²-ΠΏΡΠ°ΠΊΡΠΈΠΊΠΎΠ² (Π.Π. ΠΠ°ΡΠ°Π½ΠΎΠ²Π° - Β«ΠΠΎΠΌΡΠΎΠΌΠΎΠ»ΡΡΠΊΠ°Ρ ΠΡΠ°Π²Π΄Π°Β», Π.Π. ΠΠ°ΡΠΈΡ
- ΡΠ΅Π»Π΅ΠΊΠ°Π½Π°Π» Π’ΠΠ¦) ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ ΡΠ΄Π΅Π»Π°ΡΡ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΡΡΠ΄ ΠΎΠ±ΡΡΠΎΡΡΠ΅Π»ΡΡΡΠ², ΡΠ°ΠΊ ΠΈΠ»ΠΈ ΠΈΠ½Π°ΡΠ΅ ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΏΡΠΎΡΠ΅ΡΡΠΎΠΌ ΠΌΠ΅Π΄ΠΈΠ°ΠΊΠΎΠ½Π²Π΅ΡΠ³Π΅Π½ΡΠΈΠΈ, Π·Π°ΠΏΡΡΡΠΈΠ»ΠΈ ΠΏΡΠΎΡΠ΅ΡΡ ΠΎΠ±ΡΠ΅Π³ΠΎ ΠΏΠ°Π΄Π΅Π½ΠΈΡ ΠΌΠ΅Π΄ΠΈΠΉΠ½ΠΎΠΉ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠΉ ΠΊΡΠ»ΡΡΡΡΡ. Π Π΅ΡΡ ΠΎ ΠΏΠΎΡΡΠΎΡΠ½Π½ΠΎΠΌ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΠΏΠΎΡΠΎΠΊΠΎΠ², ΡΠ°Π·Π²ΠΈΡΠΈΠΈ ΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΠ΅Π»ΡΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅Π½ΡΠ° ΠΈ Π±Π»ΠΎΠ³ΠΎΡΡΠ΅ΡΡ, ΡΡΠ°Π½ΡΡΠΎΡΠΌΠ°ΡΠΈΠΈ ΠΆΡΡΠ½Π°Π»ΠΈΡΡΠΈΠΊΠΈ ΠΊΠ°ΠΊ ΡΠΈΡΡΠ΅ΠΌΡ Π²ΠΈΠ΄ΠΎΠ² ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ, ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΡΡΠ΄Π½ΠΎΡΡΡΡ
, Ρ ΠΊΠΎΡΠΎΡΡΠΌΠΈ ΡΡΠ°Π»ΠΊΠΈΠ²Π°ΡΡΡΡ ΠΌΠ½ΠΎΠ³ΠΈΠ΅ ΠΌΠ΅Π΄ΠΈΠ°ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΠΈ. Π ΡΡΠ°ΡΡΠ΅ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΡΠ°Π·Π±ΠΎΡ ΠΎΡΠΈΠ±ΠΎΠΊ Π² ΡΠ΅ΠΊΡΡΠ°Ρ
ΠΏΠ΅ΡΠ°ΡΠ½ΡΡ
ΠΈ ΡΡΠΈΡΠ½ΡΡ
ΠΌΠ΅Π΄ΠΈΠ°. Π¦Π΅Π»Ρ ΡΡΠ°ΡΡΠΈ - ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΡ ΡΠΈΡΡΠ°ΡΠΈΡ ΠΈ Π°ΠΊΡΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅Π³ΠΎ ΠΎΡΠΌΡΡΠ»Π΅Π½ΠΈΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΠ°Π΄Π΅Π½ΠΈΡ ΡΠ·ΡΠΊΠΎΠ²ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΠ°ΠΊΡΠ»ΡΡΡΡΡ Π² Π°ΡΠΏΠ΅ΠΊΡΠ΅ Π°ΠΊΡΠΈΠΎΠ»ΠΎΠ³ΠΈΠΈ
BRIDGE ACROSS THE KERCH STRAIT - HISTORY AND MODERNITY
The present study provides a brief description of the geological conditions in the Kerch Strait, as well as a historical aspects on the complexity of building a bridge across it. The bridge consists of a four-lane road and of another double-track railaway spanning the extensive Staight. The paper provides estimates of expected maximum heights of tsunami waves for the pillars of the Crimean bridge if a significant catastrophic earthquakes occurs in the northwest of the Crimean Peninsula and in the localization of the earthquake source in the basin of the Black and Azov Seas in front of the entrance to the Kerch Strait. The main purpose of this work is to provide estimates of the tsunami mhazard for the area of the Crimean bridge in the Kerch Strait during earthquakes with sources in the nearest basin areas of the Black and Azov Seas, with magnitudes M = 7, 7.5 and 8. Comparative histograms of possible maximum wave heights near the bridge pillars are given. It is shown that in the area of the western pillars of the Crimean Bridge, the tsunami wave heights for all scenarios do not exceed 0.3β0.5 m, and in the area of the eastern pillars, the range of possible wave heights lies in the range of 0.6β1.95 m
TSUNAMI DANGER IN THE KERCH STRAIT
The numerical simulation of the tsunami wave propagation along the Kerch Strait is carried out with localization of possible sources at the entrances to the strait, both from the Black Sea and from the Sea of Azov. Under computation of both generation and tsunami propagation, a system of nonlinear shallow water equations was used. The potential strong earthquakes (with earthquake magnitude M ~ 7) with seismic sources of elliptical shape were considered. Detailed assessments of the wave characteristics in the Kerch Strait, in particular for the Crimean bridge area, were carried out. The obtained wave characteristics are compared with the available data of the work of other authors
ΠΠΠ’Π£ΠΠΠ¬ΠΠ«Π ΠΠΠΠ ΠΠ‘Π« ΠΠΠΠΠΠΠ‘Π’ΠΠΠ ΠΠΠ©ΠΠΠΠ ΠΠΠΠΠ ΠΠΠ Π ΠΠΠΠΠΠ’Π ΠΠ§ΠΠ‘ΠΠΠ ΠΠ ΠΠΠ’ΠΠΠ
Food allergy (FA) in children, especially in infancy, is still a significant public health problem. The severity and prognosis of disease progression associated with FA considerably depends on the correct and early diagnostics of this pathology, as well as on the following management of a child. At the same time delayed elimination diet administration, unreasonable or overlong dietary intervention might have become abuse management of a patient and have a negative impact on the development of a child and reduce the quality of life. The article summarizes the current practical approaches to the diagnosis of FA based on evidence-based medicine and adopted European and Russian national consensus documents, as well as on our own experience of management of patients with this pathology. FA diagnosis in a child usually includes clinical laboratory tests and clarification of clinical and anamnestic data. Unfortunately, it is a fact that preference is given to laboratory methods for diagnosis based on specific IgE determination or skin samples. However, the basis for cause-significant allergen identifying is detecting detailed medical history and clinical picture of a disease which still appears to be the most reliable tool for FA diagnosis.Β ΠΠΈΡΠ΅Π²Π°Ρ Π°Π»Π»Π΅ΡΠ³ΠΈΡ (ΠΠ) Ρ Π΄Π΅ΡΠ΅ΠΉ, ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ Π² ΡΠ°Π½Π½Π΅ΠΌ Π²ΠΎΠ·ΡΠ°ΡΡΠ΅, Π΄ΠΎ ΡΠΈΡ
ΠΏΠΎΡ ΠΎΡΡΠ°Π΅ΡΡΡ ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ Π·Π΄ΡΠ°Π²ΠΎΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΡ. Π’ΡΠΆΠ΅ΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΏΡΠΎΠ³Π½ΠΎΠ· Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, ΡΠ²ΡΠ·Π°Π½Π½ΡΡ
Ρ ΠΠ, Π²ΠΎ ΠΌΠ½ΠΎΠ³ΠΎΠΌ Π·Π°Π²ΠΈΡΡΡ ΠΎΡ ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΠΎΠΉ ΠΈ ΡΠ²ΠΎΠ΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΈ Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ΅Π±Π΅Π½ΠΊΠ°. ΠΡΠΈ ΡΡΠΎΠΌ ΠΊΠ°ΠΊ Π½Π΅ΡΠ²ΠΎΠ΅Π²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΡΠ»ΠΈΠΌΠΈΠ½Π°ΡΠΈΠΎΠ½Π½ΠΎΠΉ Π΄ΠΈΠ΅ΡΡ, ΡΠ°ΠΊ ΠΈ Π½Π΅ΠΎΠΏΡΠ°Π²Π΄Π°Π½Π½Π°Ρ Π΄ΠΈΠ΅ΡΠ° ΠΈΠ»ΠΈ Π΅Π΅ ΡΠ»ΠΈΡΠΊΠΎΠΌ Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΠΎΠ±Π»ΡΠ΄Π΅Π½ΠΈΠ΅ ΠΌΠΎΠ³ΡΡ ΡΠ²Π»ΡΡΡΡΡ ΠΎΡΠΈΠ±ΠΎΡΠ½ΠΎΠΉ ΡΠ°ΠΊΡΠΈΠΊΠΎΠΉ Π²Π΅Π΄Π΅Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°, Π½Π΅Π³Π°ΡΠΈΠ²Π½ΠΎ Π²Π»ΠΈΡΡΡ Π½Π° ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΠΈ ΡΠ½ΠΈΠΆΠ°ΡΡ ΠΊΠ°ΡΠ΅ΡΡΠ²ΠΎ ΠΆΠΈΠ·Π½ΠΈ ΡΠ΅Π±Π΅Π½ΠΊΠ°. Π ΡΡΠ°ΡΡΠ΅ ΠΊΡΠ°ΡΠΊΠΎ ΠΈΠ·Π»ΠΎΠΆΠ΅Π½Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ ΠΠ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΠ΅ Π½Π° Π΄ΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅ ΠΈ ΠΏΡΠΈΠ½ΡΡΡΠ΅ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΠ²ΡΠΎΠΏΠ΅ΠΉΡΠΊΠΈΡ
ΠΈ ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΡΡ
ΡΠΎΠ³Π»Π°ΡΠΈΡΠ΅Π»ΡΠ½ΡΡ
Π΄ΠΎΠΊΡΠΌΠ΅Π½ΡΠΎΠ², Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π° ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΠΎΠΏΡΡΠ΅ Π²Π΅Π΄Π΅Π½ΠΈΡ Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ. ΠΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° ΠΠ Ρ ΡΠ΅Π±Π΅Π½ΠΊΠ°, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, Π²ΠΊΠ»ΡΡΠ°Π΅Ρ Π² ΡΠ΅Π±Ρ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠ΅ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΈ Π²ΡΡΡΠ½Π΅Π½ΠΈΠ΅ ΠΊΠ»ΠΈΠ½ΠΈΠΊΠΎ-Π°Π½Π°ΠΌΠ½Π΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
. Π ΡΠΎΠΆΠ°Π»Π΅Π½ΠΈΡ, ΡΠ°ΡΡΠΎ ΠΏΡΠΈΡ
ΠΎΠ΄ΠΈΡΡΡΡ ΡΡΠ°Π»ΠΊΠΈΠ²Π°ΡΡΡΡ Ρ ΡΠ΅ΠΌ, ΡΡΠΎ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ΅Π½ΠΈΠ΅ ΠΎΡΠ΄Π°Π΅ΡΡΡ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΠΌ ΠΌΠ΅ΡΠΎΠ΄Π°ΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΡ
IgE ΠΈΠ»ΠΈ ΠΊΠΎΠΆΠ½ΡΡ
ΠΏΡΠΎΠ±Π°Ρ
. ΠΠ΄Π½Π°ΠΊΠΎ ΠΎΡΠ½ΠΎΠ²ΠΎΠΉ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΏΡΠΈΡΠΈΠ½Π½ΠΎ-Π·Π½Π°ΡΠΈΠΌΠΎΠ³ΠΎ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π° ΠΎΡΡΠ°Π΅ΡΡΡ Π΄Π΅ΡΠ°Π»ΡΠ½ΡΠΉ ΡΠ±ΠΎΡ Π°Π½Π°ΠΌΠ½Π΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π΄Π°Π½Π½ΡΡ
, ΠΊΠΎΡΠΎΡΡΠΉ Π²ΠΌΠ΅ΡΡΠ΅ Ρ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ°ΡΡΠΈΠ½ΠΎΠΉ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ Π΄ΠΎ ΡΠΈΡ
ΠΏΠΎΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π½Π°Π΄Π΅ΠΆΠ½ΡΠΌ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΠ.
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
Π ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π·Π°ΡΡΠ°Ρ Π½Π° Π²ΡΡΠΎΠΊΠΎΡΡΡΠ΅ΠΊΡΠΈΠ²Π½ΡΡ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ Π°Π»Π»Π΅ΡΠ³ΠΈΠΈ Ρ Π΄Π΅ΡΠ΅ΠΉ: Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ³Π»Π°ΡΠΎΠ²Π°Π½Π½ΠΎΡΡΠΈ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² Π°Π»Π»Π΅ΡΠ³ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ in vitro- ΠΈ in vivo-ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ
High morbidity rate of atopic diseases among children, including high importance of grass pollen as a sensitizing agent, determine the relevance of studies on diagnostic examination systems for appointment of adequate therapy. The research of the most relevant allergens for patients to exclude of duplicating and uninformative tests became urgent after development of a new type of diagnostic tests that does not require expensive equipment.Β The objectiveΒ of this research was to evaluate the results of in vitro- and in vivo-diagnostic examinations of children with various forms of atopic disease caused by pollen of meadow grasses, and to choose the most significant prognostic parameters for the diagnosis.Β Methods: 277 children aged 4β16 years with various forms of atopic disease were included in the study. There were performed skin prick tests and determination of IgE-antibodies levels to allergen extracts of cocksfoot (g3), meadow fescue (g4), timothy grass (g6).Β Results: In the studied group of patients 32β50% of children have antibodies to grass allergens. There was a close correlation of antibody response on the investigated allergens, quantitative coincidence of IgE-antibodies to g3 and g4 allergens levels. IgE (g6) concentration was close to the IgE(g3) and IgE(g4) levels (85,0Β±21,6%). Analysis of the skin tests results showed that 44% of patients have a positive response to grass allergens, and in vivo-tests results coincide with serological tests results, mostly in a qualitative sense. The most significant relationship was noted between in vivo and in vitro-tests in the results of testing the response to meadow fescue pollen.Conclusion: Based on these data IgE concentration index to meadow fescue allergens can be used as a prognostic marker to determine the sensitization of patients with different nosology forms of allergy and can help to improve allergic diagnostics.Π Π°ΡΠΏΡΠΎΡΡΡΠ°Π½Π΅Π½Π½ΠΎΡΡΡ Π°ΡΠΎΠΏΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π²ΡΡΠΎΠΊΠ°Ρ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ ΠΏΡΠ»ΡΡΡ Π·Π»Π°ΠΊΠΎΠ²ΡΡ
ΡΡΠ°Π² ΠΊΠ°ΠΊ ΡΠ΅Π½ΡΠΈΠ±ΠΈΠ»ΠΈΠ·ΠΈΡΡΡΡΠ΅Π³ΠΎ Π°Π³Π΅Π½ΡΠ°, ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΏΠΎΠΈΡΠΊΠ° ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°Π»Π»Π΅ΡΠ³ΠΎΠ΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Ρ ΡΠ΅Π»ΡΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅Π³ΠΎ Π½Π°Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠ°ΠΏΠΈΠΈ. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΎΠΉ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΡ-ΡΠΈΡΡΠ΅ΠΌΡ, Π½Π΅ ΡΡΠ΅Π±ΡΡΡΠ΅ΠΉ Π΄ΠΎΡΠΎΠ³ΠΎΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΎΠ±ΠΎΡΡΠ΄ΠΎΠ²Π°Π½ΠΈΡ, Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌΠΈ ΡΡΠ°Π»ΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΡ
Π΄Π»Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π°Π»Π»Π΅ΡΠ³Π΅Π½ΠΎΠ², ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅ Π΄ΡΠ±Π»ΠΈΡΡΡΡΠΈΡ
ΠΈ ΠΌΠ°Π»ΠΎΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΡ
ΡΠ΅ΡΡΠΎΠ².Β Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΎΡΠ΅Π½ΠΈΡΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡΒ inΒ vitro- ΠΈΒ inΒ vivo-ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ Π΄Π΅ΡΠ΅ΠΉ Ρ ΡΠ°Π·Π½ΠΎΠΉ Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠΎΠΉ Π°Π»Π»Π΅ΡΠ³ΠΈΠΈ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΠΎΠΉ ΠΏΡΠ»ΡΡΠΎΠΉ Π·Π»Π°ΠΊΠΎΠ²ΡΡ
ΡΡΠ°Π², ΠΈ Π²ΡΠ±ΡΠ°ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΠ΅ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ Π΄Π»Ρ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ.Β ΠΠ΅ΡΠΎΠ΄Ρ:Β Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π±ΡΠ»ΠΈ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ 277 Π΄Π΅ΡΠ΅ΠΉ Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ 4β16 Π»Π΅Ρ Ρ ΡΠ°Π·Π½ΡΠΌΠΈ Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠΎΡΠΌΠ°ΠΌΠΈ Π°ΡΠΎΠΏΠΈΡΠ΅ΡΠΊΠΎΠΉ Π±ΠΎΠ»Π΅Π·Π½ΠΈ. ΠΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌ Π±ΡΠ»ΠΈ Π²ΡΠΏΠΎΠ»Π½Π΅Π½Ρ ΠΊΠΎΠΆΠ½ΡΠ΅ ΡΠ΅ΡΡΡ ΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡΒ IgEΒ ΠΊ ΡΠ΅Π»ΡΠ½ΡΠΌ ΡΠΊΡΡΡΠ°ΠΊΡΠ°ΠΌ Π°Π»Π»Π΅ΡΠ³Π΅Π½ΠΎΠ² Π΅ΠΆΠΈ ΡΠ±ΠΎΡΠ½ΠΎΠΉ (g3), ΠΎΠ²ΡΡΠ½ΠΈΡΡ Π»ΡΠ³ΠΎΠ²ΠΎΠΉ (g4), ΡΠΈΠΌΠΎΡΠ΅Π΅Π²ΠΊΠΈ Π»ΡΠ³ΠΎΠ²ΠΎΠΉ (g6).Β Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ:Β ΠΎΡ 32 Π΄ΠΎ 50% Π΄Π΅ΡΠ΅ΠΉ Π² ΠΈΡΡΠ»Π΅Π΄ΡΠ΅ΠΌΡΡ
Π³ΡΡΠΏΠΏΠ°Ρ
ΠΈΠΌΠ΅ΡΡ Π°Π½ΡΠΈΡΠ΅Π»Π° ΠΊ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌ Π·Π»Π°ΠΊΠΎΠ²ΡΡ
ΡΡΠ°Π². ΠΡΠΌΠ΅ΡΠ΅Π½Ρ ΡΠ΅ΡΠ½Π°Ρ ΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ Π°Π½ΡΠΈΡΠ΅Π»ΡΠ½ΡΠΌ ΠΎΡΠ²Π΅ΡΠΎΠΌ Π½Π° ΠΈΠ·ΡΡΠ°Π΅ΠΌΡΠ΅ Π°Π»Π»Π΅ΡΠ³Π΅Π½Ρ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΡΠΎΠ²ΠΏΠ°Π΄Π΅Π½ΠΈΠ΅ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΡΒ IgE-Π°Π½ΡΠΈΡΠ΅Π» ΠΊ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌΒ g3 ΠΈΒ g4, Π±Π»ΠΈΠ·ΠΊΠΎΠ΅ ΠΊΒ IgE(g3) ΠΈΒ IgE(g4) Π·Π½Π°ΡΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈΒ IgEΒ ΠΊΒ g6 (85,0 Β± 21,6%). ΠΠ½Π°Π»ΠΈΠ· ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΊΠΎΠΆΠ½ΡΡ
ΡΠ΅ΡΡΠΎΠ² ΠΏΠΎΠΊΠ°Π·Π°Π», ΡΡΠΎ 44% ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΈΠΌΠ΅ΡΡ ΠΏΠΎΠ·ΠΈΡΠΈΠ²Π½ΡΠΉ ΠΎΡΠ²Π΅Ρ Π½Π° Π°Π»Π»Π΅ΡΠ³Π΅Π½Ρ Π·Π»Π°ΠΊΠΎΠ²ΡΡ
ΡΡΠ°Π², ΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΌ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠΈ ΠΎΡΠ²Π΅ΡΡΒ in-vivo-ΡΠ΅ΡΡΠΎΠ² ΡΠΎΠ²ΠΏΠ°Π΄Π°ΡΡ Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΡΠ΅ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ². ΠΠ°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΠ°Ρ ΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄ΡΒ inΒ vivo- ΠΈΒ inΒ vitro-ΡΠ΅ΡΡΠ°ΠΌΠΈ ΠΎΡΠΌΠ΅ΡΠ΅Π½Π° ΠΌΠ΅ΠΆΠ΄Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Ρ ΡΠ΅Π»ΡΠ½ΡΠΌ ΡΠΊΡΡΡΠ°ΠΊΡΠΎΠΌ Π°Π»Π»Π΅ΡΠ³Π΅Π½ΠΎΠ² ΠΎΠ²ΡΡΠ½ΠΈΡΡ.Β ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅:Β ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈΒ IgEΒ ΠΊ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌ ΠΎΠ²ΡΡΠ½ΠΈΡΡ Π»ΡΠ³ΠΎΠ²ΠΎΠΉ ΠΌΠΎΠΆΠ½ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°ΡΡ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΌΠ°ΡΠΊΠ΅ΡΠ° Π΄Π»Ρ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΠ΅Π½ΡΠΈΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ°Π·Π½ΠΎΠΉ Π½ΠΎΠ·ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠΎΠΉ Π°Π»Π»Π΅ΡΠ³ΠΈΠΈ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½Π½ΠΎΠΉ ΡΠ΅Π½ΡΠΈΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ ΠΊ ΠΏΡΠ»ΡΡΠ΅ Π·Π»Π°ΠΊΠΎΠ²ΡΡ
ΡΡΠ°Π², ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ ΡΡΠΎΠ³ΠΎ ΠΎΠΏΡΠΈΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°ΡΡ Π»Π°Π±ΠΎΡΠ°ΡΠΎΡΠ½ΡΡ Π°Π»Π»Π΅ΡΠ³ΠΎΠ΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ
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
Π‘ΠΠΠΠ ΠΠ ΠΠΠ‘Π’Π Π£ΠΠ’ΠΠΠΠΠΠ ΠΠΠΠΠ Π‘ΠΠ Π£ ΠΠΠ’ΠΠ ΠΠΠ Π ΠΠ‘Π Π ΠΠΠΠΠ’ΠΠ― Π‘ΠΠ ΠΠΠ§ΠΠ-Π‘ΠΠ‘Π£ΠΠΠ‘Π’ΠΠ ΠΠΠ’ΠΠΠΠΠΠ
Objective: Our aim was to examine the predictors of cardiovascular disorders in children affected by obstructive sleep apnea syndrome (OSAS) based on the results of polysomnography and continuous monitoring of blood glycose. Πethods: Before the examination, parents filled in questionnaires concerning their children sleep quality. The procedure was followed by the study of the sleep by means of polysomnography (Embla s 7000, USA). A system of continuous monitoring of blood glucose was applied (Guardianreal-time, Medtronicminimed, USA) by means of which a glycemic profile tissue fluid was studied. Results: A night sleep research of 120 children aged 3β16 y.o. is presented. There were 4 groups depending on the pathology: diseases of the nervous system (nΒ =31), ENT-pathology (nΒ =18), bronchial asthma (nΒ =24) and overweight and obesity (nΒ =34). The comparison group consisted of 13 apparently healthy children. The study has shown that the parents of every second child with sleep disorders did not know about the fact. The 60 % of the patients with high body mass index (BMI) had a snore, which was significantly higher the in children with normal body mass index β 35% (ΡΒ =0.012). The index of apnea-hypopnea (AHI) was higher in the patients with ENT-pathology 17 times (pΒ 0.001) and the patients with obesity 7 times (pΒ 0.001) in comparison to the comparison group. In the analysis of the overall sample (nΒ =120) was obtained significant negative correlation with heart rate variability and heart rate (rΒ =Β 0.405; pΒ 0.001). It is also shown that among 14 investigated children with OSAS only 8 had episodes of hypoglycemia (less than 3.3Β mmol/l) during night sleep. All of them were with a high body mass index and with above average stature (1sd). Conclusion: Children with ENT-pathology and with high high body mass index have high risk of cardio-vascular diseases. Children with above average stature and with increased body mass index affected by OSAS have additional backgrounds for cardiovascular diseases development as a result of the latent periods of hypoglycemia at night.Β Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: Π²ΡΡΠ²ΠΈΡΡ ΠΏΡΠ΅Π΄ΠΈΠΊΡΠΎΡΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎ-ΡΠΎΡΡΠ΄ΠΈΡΡΡΡ
Π½Π°ΡΡΡΠ΅Π½ΠΈΠΉ Ρ Π΄Π΅ΡΠ΅ΠΉ Π½Π° ΡΠΎΠ½Π΅ Β ΡΠΈΠ½Π΄ΡΠΎΠΌΠ° ΠΎΠ±ΡΡΡΡΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π°ΠΏΠ½ΠΎΡ ΡΠ½Π° (Π‘ΠΠΠ‘) ΠΏΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ ΠΏΠΎΠ»ΠΈΡΠΎΠΌΠ½ΠΎΠ³ΡΠ°ΡΠΈΠΈ ΠΈ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³Π»ΡΠΊΠΎΠ·Ρ. ΠΠ΅ΡΠΎΠ΄Ρ: ΠΏΠ΅ΡΠ΅Π΄ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠΎΠ΄ΠΈΡΠ΅Π»ΠΈ Π·Π°ΠΏΠΎΠ»Π½ΡΠ»ΠΈ Π°Π½ΠΊΠ΅ΡΡ-Π²ΠΎΠΏΡΠΎΡΠ½ΠΈΠΊ ΠΏΠΎ ΠΊΠ°ΡΠ΅ΡΡΠ²Ρ ΡΠ½Π°, ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠ½Π° ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΠΎΠ»ΠΈΡΠΎΠΌΠ½ΠΎΠ³ΡΠ°ΡΠΈΠΈ (Embla S7000,Π‘Π¨Π). Π‘ΠΈΡΡΠ΅ΠΌΠΎΠΉ Π½Π΅ΠΏΡΠ΅ΡΡΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³Π»ΡΠΊΠΎΠ·Ρ (Guardian Real-Time, Medtronic MiniMed, Π‘Π¨Π) ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈ Π³Π»ΠΈΠΊΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΏΡΠΎΡΠΈΠ»Ρ Π² ΡΠΊΠ°Π½Π΅Π²ΠΎΠΉ ΠΆΠΈΠ΄ΠΊΠΎΡΡΠΈ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ: ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΎ 120 Π΄Π΅ΡΠ΅ΠΉ Π² Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ 3β16Β Π»Π΅Ρ. ΠΠ΅ΡΠ΅ΠΉ ΡΠ°ΡΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ ΠΏΠΎ Π³ΡΡΠΏΠΏΠ°ΠΌ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΉ: 31 β Ρ Π±ΠΎΠ»Π΅Π·Π½ΡΠΌΠΈ Π½Π΅ΡΠ²Π½ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΡ, 18 β Ρ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ ΠΠΠ -ΠΎΡΠ³Π°Π½ΠΎΠ², 24 β Ρ Π±ΡΠΎΠ½Ρ
ΠΈΠ°Π»ΡΠ½ΠΎΠΉ Π°ΡΡΠΌΠΎΠΉ ΠΈ 34 β Ρ ΠΈΠ·Π±ΡΡΠΎΡΠ½ΡΠΌ Π²Π΅ΡΠΎΠΌ ΠΈ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ (ΠΠ²ΠΈΠ); 13 ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄Π΅ΡΠ΅ΠΉ ΡΠΎΡΡΠ°Π²ΠΈΠ»ΠΈ Π³ΡΡΠΏΠΏΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ. Π ΠΎΠ΄ΠΈΡΠ΅Π»ΠΈ ΠΊΠ°ΠΆΠ΄ΠΎΠ³ΠΎ Π²ΡΠΎΡΠΎΠ³ΠΎ ΡΠ΅Π±Π΅Π½ΠΊΠ° Π½Π΅ Π·Π½Π°Π»ΠΈ ΠΎ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΡ
Π΄ΡΡ
Π°Π½ΠΈΡ Π²ΠΎ Π²ΡΠ΅ΠΌΡ ΡΠ½Π° Ρ ΡΠ²ΠΎΠΈΡ
Π΄Π΅ΡΠ΅ΠΉ. Π₯ΡΠ°ΠΏ Π² ΠΎΠ±ΡΠ΅ΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ ΠΏΡΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΠΎΠΌ ΠΠΠ’ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ Ρ 60% Π΄Π΅ΡΠ΅ΠΉ, ΡΡΠΎ Π·Π½Π°ΡΠΈΠΌΠΎ ΡΠ°ΡΠ΅, ΡΠ΅ΠΌ Ρ Π΄Π΅ΡΠ΅ΠΉ Ρ Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΠΌ ΠΠΠ’ (35%; ΡΒ =0,012). ΠΠ½Π΄Π΅ΠΊΡ Π°ΠΏΠ½ΠΎΡΒ /Β Π³ΠΈΠΏΠΎΠΏΠ½ΠΎΡ (ΠΠΠ) Π±ΡΠ» Π²ΡΡΠ΅ ΠΏΡΠΈ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΠΠ -ΠΎΡΠ³Π°Π½ΠΎΠ² Π² 17 ΡΠ°Π· (pΒ 0,001) ΠΈ Π² Π³ΡΡΠΏΠΏΠ΅ Ρ ΠΠΠΈΠ β Π² 7 ΡΠ°Π· (pΒ 0,001) ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ Π³ΡΡΠΏΠΏΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ. Π ΡΡΠΈΡ
ΠΆΠ΅ Π³ΡΡΠΏΠΏΠ°Ρ
ΠΎΠΊΠ°Π·Π°Π»Π°ΡΡ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΠΎΠΉ ΡΠ°ΡΡΠΎΡΠ° ΡΠ΅ΡΠ΄Π΅ΡΠ½ΡΡ
ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΠΉ (Π§Π‘Π‘) ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ (ΡΒ =0,002). ΠΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π΅ Π² ΠΎΠ±ΡΠ΅ΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ (nΒ =120) Π±ΡΠ»Π° ΠΏΠΎΠ»ΡΡΠ΅Π½Π° Π·Π½Π°ΡΠΈΠΌΠ°Ρ ΠΎΡΡΠΈΡΠ°ΡΠ΅Π»ΡΠ½Π°Ρ ΡΠ²ΡΠ·Ρ ΠΌΠ΅ΠΆΠ΄Ρ Π²Π°ΡΠΈΠ°Π±Π΅Π»ΡΠ½ΠΎΡΡΡΡ ΡΠΈΡΠΌΠ° ΡΠ΅ΡΠ΄ΡΠ° ΠΈ ΡΠ°ΡΡΠΎΡΠΎΠΉ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΡΡ
ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΠΉ (r=Β -0,405; pΒ 0,001). ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ, ΡΡΠΎ ΠΈΠ· 14 ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠΎΠ»ΡΠΊΠΎ Ρ Π΄Π΅ΡΠ΅ΠΉ Ρ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΡΠΌ ΠΠΠ’ ΠΈ ΡΠΎΡΡΠΎΠΌ Π²ΡΡΠ΅ ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ (1SD; nΒ =8) Π±ΡΠ»ΠΈ Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½Ρ ΡΠΏΠΈΠ·ΠΎΠ΄Ρ Π½ΠΎΡΠ½ΠΎΠΉ Π³ΠΈΠΏΠΎΠ³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ (3,3Β ΠΌΠΌΠΎΠ»Ρ/Π»). ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅: Π΄Π΅ΡΠΈ Ρ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠ΅ΠΉ ΠΠΠ -ΠΎΡΠ³Π°Π½ΠΎΠ² ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΡΠΌ ΠΠΠ’ Π½Π° ΡΠΎΠ½Π΅ Π‘ΠΠΠ‘ ΠΏΠΎΠ΄Π²Π΅ΡΠΆΠ΅Π½Ρ ΡΠΈΡΠΊΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎ-ΡΠΎΡΡΠ΄ΠΈΡΡΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠ΅ΡΠΈ Ρ ΡΠΎΡΡΠΎΠΌ Π²ΡΡΠ΅ ΡΡΠ΅Π΄Π½Π΅Π³ΠΎ ΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½Π½ΡΠΌ ΠΠΠ’ Π½Π° ΡΠΎΠ½Π΅ Π‘ΠΠΠ‘ ΠΈΠΌΠ΅ΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΠΏΡΠ΅Π΄ΠΏΠΎΡΡΠ»ΠΊΠΈ Π΄Π»Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎ-ΡΠΎΡΡΠ΄ΠΈΡΡΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ Π² Π²ΠΈΠ΄Π΅ ΡΠΊΡΡΡΡΡ
ΠΏΠ΅ΡΠΈΠΎΠ΄ΠΎΠ² Π³ΠΈΠΏΠΎΠ³Π»ΠΈΠΊΠ΅ΠΌΠΈΠΈ Π² Π½ΠΎΡΠ½ΠΎΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄.Β
ΠΠΈΡΠ΅Π²Π°Ρ Π°Π»Π»Π΅ΡΠ³ΠΈΡ Ρ Π΄Π΅ΡΠ΅ΠΉ Ρ Π²ΡΠΎΠΆΠ΄Π΅Π½Π½ΡΠΌ Π±ΡΠ»Π»Π΅Π·Π½ΡΠΌ ΡΠΏΠΈΠ΄Π΅ΡΠΌΠΎΠ»ΠΈΠ·ΠΎΠΌ. Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π½Π°Π±Π»ΡΠ΄Π°ΡΠ΅Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ
Background: Inherited epidermolysis bullosa (EB) refers to a group of rare inherited disorders characterized by severe damage of skin and in most patients β the gastrointestinal mucosa, what leads to a violation of skin and mucosal barrier properties in relation to allergens. However, the issues of food sensitization and food allergy in this category of patients have not been studied, and the study of this problem is important.Aim: To evaluate the clinical manifestations of food allergy (FA) and IgE-response to food proteins in children with EB.Methods: 82 patients with EB aged from 2 months to 16 years were entered this open non-randomized observational prospective study, including 20 patients with simple form of EB and 62 patients with dystrophic form of EB. We analyzed allergic history and clinical manifestations of the FA in all the patients. Every patient in this study underwent of determination of the concentration of total serum IgE and specific serum IgE to the most important food allergens, as well as to mixtures of household allergens in some cases (UniCAP System, Phadia AB).Β Results:Β Skin lesion in patients with EB masks allergic skin manifestations, causing a hypodiagnosis of the FA in this category of patients, which in turn leads to erroneous organization of nutritional support. FA (clinical manifestations) was identified in 20.7% of children with EB (in 10% of cases with simple form of EB and in 24.2% β in patients with dystrophic form of EB). Products containing cowβs milk protein, cereals, and eggs were identified as etiologic factors of FA in most cases. In the group of children with comorbidity FA and EB high and very high levels of total IgE (1000 kUA / l) were detected most frequently. The main cause-significant allergens are cowβs milk proteins, cereals, eggs.Β Conclusions: Comorbidity with FA is high in patients with dystrophic form of EB. The main cause-significant allergens are cowβs milk proteins, cereals, eggs.ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅. ΠΡΠΎΠΆΠ΄Π΅Π½Π½ΡΠΉ Π±ΡΠ»Π»Π΅Π·Π½ΡΠΉ ΡΠΏΠΈΠ΄Π΅ΡΠΌΠΎΠ»ΠΈΠ· (ΠΠΠ) ΠΎΡΠ½ΠΎΡΠΈΡΡΡΒ ΠΊΒ Π³ΡΡΠΏΠΏΠ΅ ΡΠ΅Π΄ΠΊΠΈΡ
Π½Π°ΡΠ»Π΅Π΄ΡΡΠ²Π΅Π½Π½ΡΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡΠΈΡ
ΡΡ ΡΡΠΆΠ΅Π»ΡΠΌ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ΠΌ ΠΊΠΎΠΆΠ½ΡΡ
ΠΏΠΎΠΊΡΠΎΠ²ΠΎΠ²Β ΠΈ ΡΒ Π±ΠΎΠ»ΡΡΠΈΠ½ΡΡΠ²Π° Π±ΠΎΠ»ΡΠ½ΡΡ
Β β ΡΠ»ΠΈΠ·ΠΈΡΡΠΎΠΉ ΠΎΠ±ΠΎΠ»ΠΎΡΠΊΠΈ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠ½ΠΎ-ΠΊΠΈΡΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°ΠΊΡΠ°, ΡΡΠΎ ΠΏΡΠΈΠ²ΠΎΠ΄ΠΈΡΒ ΠΊΒ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΈΡ
Π±Π°ΡΡΠ΅ΡΠ½ΡΡ
ΡΠ²ΠΎΠΉΡΡΠ² ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡΒ ΠΊΒ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌ. ΠΠ΄Π½Π°ΠΊΠΎ Π²ΠΎΠΏΡΠΎΡΡ ΠΏΠΈΡΠ΅Π²ΠΎΠΉ ΡΠ΅Π½ΡΠΈΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈΒ ΠΈΒ ΠΏΠΈΡΠ΅Π²ΠΎΠΉ Π°Π»Π»Π΅ΡΠ³ΠΈΠΈΒ ΡΒ Π΄Π°Π½Π½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
Π½Π΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ,Β ΠΈΒ ΠΈΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΠΌ.Β Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΒ β ΠΎΡΠ΅Π½ΠΈΡΡ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΠΉ ΠΏΠΈΡΠ΅Π²ΠΎΠΉ Π°Π»Π»Π΅ΡΠ³ΠΈΠΈ (ΠΠ)Β ΠΈΒ ΡΠ΅Π½ΡΠΈΠ±ΠΈΠ»ΠΈΠ·Π°ΡΠΈΠΈΒ ΠΊΒ ΠΏΠΈΡΠ΅Π²ΡΠΌ Π±Π΅Π»ΠΊΠ°ΠΌΒ ΡΒ Π΄Π΅ΡΠ΅ΠΉΒ ΡΒ ΠΠΠ.Β ΠΠ΅ΡΠΎΠ΄Ρ. ΠΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ ΠΎΡΠΊΡΡΡΠΎΠ΅ Π½Π΅ΡΠ°Π½Π΄ΠΎΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ΅ Π½Π°Π±Π»ΡΠ΄Π°ΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠΎΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅. ΠΠΊΠ»ΡΡΠ΅Π½Ρ 82Β ΡΠ΅Π±Π΅Π½ΠΊΠ°Β ΡΒ ΠΠΠΒ Π²Β Π²ΠΎΠ·ΡΠ°ΡΡΠ΅ ΠΎΡ 2Β ΠΌΠ΅Ρ Π΄ΠΎ 16Β Π»Π΅Ρ,Β Π²Β ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ 20Β Π΄Π΅ΡΠ΅ΠΉΒ ΡΒ ΠΏΡΠΎΡΡΠΎΠΉΒ ΠΈΒ 62Β βΒ ΡΒ Π΄ΠΈΡΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠΎΠΉ ΠΠΠ. ΠΡΠ΅Π½ΠΈΠ²Π°Π»ΠΈΡΡ Π°Π»Π»Π΅ΡΠ³ΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°Π½Π°ΠΌΠ½Π΅Π·Β ΠΈΒ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΡ ΠΠ; Π²ΡΠ΅ΠΌ Π±ΠΎΠ»ΡΠ½ΡΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΎΠ±ΡΠ΅ΠΉ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ IgEΒ Π²Β ΡΡΠ²ΠΎΡΠΎΡΠΊΠ΅ ΠΊΡΠΎΠ²ΠΈ, sIgE ΡΡΠ²ΠΎΡΠΎΡΠΊΠΈ ΠΊΡΠΎΠ²ΠΈΒ ΠΊΒ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°ΡΠΈΠΌΡΠΌ ΠΏΠΈΡΠ΅Π²ΡΠΌ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌ, ΠΏΠΎ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΈΡΠΌΒ βΒ ΠΊΒ ΡΠΌΠ΅ΡΡΠΌ Π±ΡΡΠΎΠ²ΡΡ
Π°Π»Π»Π΅ΡΠ³Π΅Π½ΠΎΠ²Β ΡΒ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΉ UniCAP System, Thermo Fisher Scientific (ΡΠ°Π½Π΅Π΅ Phadia ΠΠ).Β Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. ΠΠΎΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ ΠΊΠΎΠΆΠΈ ΠΏΡΠΈ ΠΠΠ ΠΌΠ°ΡΠΊΠΈΡΡΠ΅Ρ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΡ ΠΠΒ ΡΒ Π΄Π΅ΡΠ΅ΠΉ, ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»ΠΈΠ²Π°Ρ Π³ΠΈΠΏΠΎΠ΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΡ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡΒ ΡΒ ΡΡΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², ΡΡΠΎΒ Π²Β ΡΠ²ΠΎΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ Π²Π΅Π΄Π΅ΡΒ ΠΊΒ Π½Π΅ΠΏΡΠ°Π²ΠΈΠ»ΡΠ½ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π½ΡΡΡΠΈΡΠΈΠ²Π½ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ. ΠΠ Π±ΡΠ»Π° Π²ΡΡΠ²Π»Π΅Π½Π°Β ΡΒ 20,7% Π΄Π΅ΡΠ΅ΠΉΒ ΡΒ ΠΠΠ (Π²Β 10% ΡΠ»ΡΡΠ°Π΅Π² ΠΏΡΠΈ ΠΏΡΠΎΡΡΠΎΠΉΒ ΠΈ Π²Β 24,2% ΠΏΡΠΈ Π΄ΠΈΡΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠ΅ ΠΠΠ).Β ΠΒ ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΡΠ°ΠΊΡΠΎΡΠ° Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΡΠΏΠ°Π»ΠΈ ΠΏΡΠΎΠ΄ΡΠΊΡΡ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠ΅ Π±Π΅Π»ΠΎΠΊ ΠΊΠΎΡΠΎΠ²ΡΠ΅Π³ΠΎ ΠΌΠΎΠ»ΠΎΠΊΠ°, Π·Π»Π°ΠΊΠΈ, ΡΠΉΡΠ°.Β ΠΒ Π³ΡΡΠΏΠΏΠ΅ Π΄Π΅ΡΠ΅ΠΉΒ ΡΒ ΠΊΠΎΠΌΠΎΡΠ±ΠΈΠ΄Π½ΠΎΡΡΡΡ ΠΠΒ ΠΈΒ ΠΠΠ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ ΡΠ°ΡΡΠΎ Π²ΡΡΡΠ΅ΡΠ°Π»ΠΈΡΡ Π²ΡΡΠΎΠΊΠΈΠ΅Β ΠΈΒ ΠΊΡΠ°ΠΉΠ½Π΅ Π²ΡΡΠΎΠΊΠΈΠ΅ ΡΡΠΎΠ²Π½ΠΈ ΠΎΠ±ΡΠ΅Π³ΠΎΒ IgE(1000Β kUA/l).Β ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ΅ΡΠΈΒ ΡΒ Π΄ΠΈΡΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠΎΡΠΌΠΎΠΉ ΠΠΠ ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡΒ ΡΒ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌΠΈΒ ΡΒ ΠΏΡΠΎΡΡΠΎΠΉ ΡΠΎΡΠΌΠΎΠΉ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ ΡΠ°ΡΠ΅ ΠΈΠΌΠ΅ΡΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΏΡΠΎΡΠ²Π»Π΅Π½ΠΈΡ ΠΠ. ΠΡΠ½ΠΎΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΡΠΈΠ½Π½ΠΎ-Π·Π½Π°ΡΠΈΠΌΡΠΌΠΈ Π°Π»Π»Π΅ΡΠ³Π΅Π½Π°ΠΌΠΈ ΡΠ²Π»ΡΡΡΡΡ Π±Π΅Π»ΠΊΠΈ ΠΊΠΎΡΠΎΠ²ΡΠ΅Π³ΠΎ ΠΌΠΎΠ»ΠΎΠΊΠ°, Π·Π»Π°ΠΊΠΈ, ΡΠΉΡΠ°.
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