6 research outputs found

    Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π·Π°ΠΏΡ€Π΅Ρ‚Π° Π½Π° ΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ Ρ‚Π°Π±Π°ΠΊΠ° Π² мСдицинских учрСТдСниях: ΠΎΡ†Π΅Π½ΠΊΠ° готовности

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    Summary. Within the framework of a programme on imposition of ban on tobacco consumption in 4 healthcare facilities at Moscow, healthcare workers were questioned in order to evaluate their adherence to this intervention. Totally, 715 healthcare workers were questioned. The questionnaire included different items regarding tobacco consumption, medical aid for smoking cessation, and a ban on tobacco consumption. Due to a high prevalence of smoking among healthcare workers, insufficient knowledge on harm of tobacco smoking and inadequate promptness for imposition of a ban on tobacco consumption, principal ways to increase the readiness for imposition of a ban on tobacco consumption have been outlined.РСзюмС. Π’ Ρ€Π°ΠΌΠΊΠ°Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ ΠΏΠΎ ввСдСнию Π·Π°ΠΏΡ€Π΅Ρ‚Π° Π½Π° ΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ Ρ‚Π°Π±Π°ΠΊΠ° Π² мСдицинских учрСТдСниях срСди пСрсонала 4 мСдицинских ΡƒΡ‡Ρ€Π΅ΠΆΠ΄Π΅Π½ΠΈΠΉ ΠœΠΎΡΠΊΠ²Ρ‹ Π±Ρ‹Π» ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ опрос с Ρ†Π΅Π»ΡŒΡŽ ΠΎΡ†Π΅Π½ΠΊΠΈ готовности ΠΊ ввСдСнию Π·Π°ΠΏΡ€Π΅Ρ‚Π°. ВсСго Π² опросС приняли участиС 715 мСдицинских Ρ€Π°Π±ΠΎΡ‚Π½ΠΈΠΊΠΎΠ². Π‘Ρ‹Π»ΠΈ Π·Π°Ρ‚Ρ€ΠΎΠ½ΡƒΡ‚Ρ‹ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ вопросы, связанныС с ΡƒΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ΠΌ Ρ‚Π°Π±Π°ΠΊΠ°, ΠΎΠΊΠ°Π·Π°Π½ΠΈΠ΅ΠΌ ΠΏΠΎΠΌΠΎΡ‰ΠΈ Π² ΠΎΡ‚ΠΊΠ°Π·Π΅ ΠΎΡ‚ курСния ΠΈ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ΠΌ Π·Π°ΠΏΡ€Π΅Ρ‚Π° Π½Π° ΡƒΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ Ρ‚Π°Π±Π°ΠΊΠ°. Π’ связи c выявлСнной высокой Ρ€Π°ΡΠΏΡ€ΠΎΡΡ‚Ρ€Π°Π½Π΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ курСния срСди мСдицинских Ρ€Π°Π±ΠΎΡ‚Π½ΠΈΠΊΠΎΠ², нСдостаточным ΡƒΡ€ΠΎΠ²Π½Π΅ΠΌ Π·Π½Π°Π½ΠΈΠΉ ΠΎ Π²Ρ€Π΅Π΄Π΅ Ρ‚Π°Π±Π°ΠΊΠ° ΠΈ нСдостаточной Π³ΠΎΡ‚ΠΎΠ²Π½ΠΎΡΡ‚ΡŒΡŽ ΠΊ ввСдСнию Π·Π°ΠΏΡ€Π΅Ρ‚Π° Π½Π° ΠΏΠΎΡ‚Ρ€Π΅Π±Π»Π΅Π½ΠΈΠ΅ Ρ‚Π°Π±Π°ΠΊΠ° Π² мСдицинских учрСТдСниях Π±Ρ‹Π»ΠΈ Π²Ρ‹Π΄Π΅Π»Π΅Π½Ρ‹ основныС направлСния для Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΏΠΎ ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΡŽ уровня готовности ΠΊ ввСдСнию Π·Π°ΠΏΡ€Π΅Ρ‚Π°

    Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ МБКВ-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ клиничСских Π΄Π°Π½Π½Ρ‹Ρ… ΠΏΡ€ΠΈ острых Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡΡ… ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ кровообращСния

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    Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the Β«gold standardΒ» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit.ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ ΠŸΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Π½Π΅ΠΉΡ€ΠΎΠ²ΠΈΠ·ΡƒΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ являСтся Π½Π΅ΠΎΡ‚ΡŠΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ Ρ‡Π°ΡΡ‚ΡŒΡŽ процСсса оказания ΠΏΠΎΠΌΠΎΡ‰ΠΈ Π±ΠΎΠ»ΡŒΠ½Ρ‹ΠΌ с острыми Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ кровообращСния (ОНМК), ΠΏΡ€ΠΈ этом Π·ΠΎΠ»ΠΎΡ‚Ρ‹ΠΌ стандартом обслСдования Π΄Π°Π½Π½ΠΎΠΉ ΠΊΠ°Ρ‚Π΅Π³ΠΎΡ€ΠΈΠΈ Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ… являСтся ΠΊΠΎΠΌΠΏΡŒΡŽΡ‚Π΅Ρ€Π½Π°Ρ томография (КВ). Π—Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Ρ€Π°ΡΡˆΠΈΡ€ΠΈΡ‚ΡŒ возмоТности Π°Π½Π°Π»ΠΈΠ·Π° КВ-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ соврСмСнных ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² машинного обучСния, Π² Ρ‚ΠΎΠΌ числС Π½Π° основС примСнСния ΠΏΡ€ΠΈΠ½Ρ†ΠΈΠΏΠΎΠ² Ρ€Π°Π΄ΠΈΠΎΠΌΠΈΠΊΠΈ. Однако, Ρ‚Π°ΠΊ ΠΊΠ°ΠΊ использованиС этих ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² Ρ‚Ρ€Π΅Π±ΡƒΠ΅Ρ‚ наличия Π±ΠΎΠ»ΡŒΡˆΠΈΡ… массивов DICOM (Digital Imaging and Communications in Medicine)-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈΡ… Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΠ΅ Π² ΠΊΠ»ΠΈΠ½ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΡƒ ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½ΠΎ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΠΎΠΉ Π½Π°Π±ΠΎΡ€Π° Ρ€Π΅ΠΏΡ€Π΅Π·Π΅Π½Ρ‚Π°Ρ‚ΠΈΠ²Π½Ρ‹Ρ… Π²Ρ‹Π±ΠΎΡ€ΠΎΠΊ. ΠšΡ€ΠΎΠΌΠ΅ Ρ‚ΠΎΠ³ΠΎ, Π² настоящСС врСмя Π² ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚ΠΎΠΌ доступС практичСски Π½Π΅ прСдставлСны ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ, содСрТащиС КВ-изобраТСния Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ… c ОНМК, ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Π΅ Π±Ρ‹Π»ΠΈ Π±Ρ‹ ΠΏΡ€ΠΈΠ³ΠΎΠ΄Π½Ρ‹ для машинного обучСния.ЦСль Π’ связи с Π²Ρ‹ΡˆΠ΅ΡΠΊΠ°Π·Π°Π½Π½Ρ‹ΠΌ, Ρ†Π΅Π»ΡŒΡŽ Π΄Π°Π½Π½ΠΎΠΉ Ρ€Π°Π±ΠΎΡ‚Ρ‹ являлось созданиС ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ DICOM-ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ Π½Π°Ρ‚ΠΈΠ²Π½ΠΎΠΉ КВ ΠΈ КВ-Π°Π½Π³ΠΈΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹ΠΌΠΈ Ρ‚ΠΈΠΏΠ°ΠΌΠΈ ОНМК.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π» ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Основой для создания ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ стали истории Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ², госпитализированных Π² Ρ€Π΅Π³ΠΈΠΎΠ½Π°Π»ΡŒΠ½Ρ‹ΠΉ сосудистый Ρ†Π΅Π½Ρ‚Ρ€ НИИ БП ΠΈΠΌ. Н.Π’. Бклифосовского. Для формирования ΠΊΠΎΠ»Π»Π΅ΠΊΡ†ΠΈΠΈ использовалась разработанная Π½Π°ΠΌΠΈ Ρ€Π°Π½Π΅Π΅ спСциализированная ΠΏΠ»Π°Ρ‚Ρ„ΠΎΡ€ΠΌΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰Π°Ρ Π²Π²ΠΎΠ΄ΠΈΡ‚ΡŒ клиничСскиС Π΄Π°Π½Π½Ρ‹Π΅ ΠΎ случаях ОНМК, ΠΏΡ€ΠΈΠΊΡ€Π΅ΠΏΠ»ΡΡ‚ΡŒ ΠΊ ΠΊΠ°ΠΆΠ΄ΠΎΠΌΡƒ ΡΠ»ΡƒΡ‡Π°ΡŽ DICOM-изобраТСния ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹Ρ… исслСдований, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΎΠΊΠΎΠ½Ρ‚ΡƒΡ€ΠΈΠ²Π°Ρ‚ΡŒ ΠΈ Ρ‚Π΅Π³ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ (Ρ€Π°Π·ΠΌΠ΅Ρ‡Π°Ρ‚ΡŒ) 3D-области интСрСса. Для тСгирования Π±Ρ‹Π» Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½ ΡΠ»ΠΎΠ²Π°Ρ€ΡŒ, элСмСнты ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠ³ΠΎ ΠΎΠΏΠΈΡΡ‹Π²Π°ΡŽΡ‚ Ρ‚ΠΈΠΏ патологичСского образования, Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°Ρ†ΠΈΡŽ ΠΈ бассСйн кровоснабТСния.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Π’ Ρ…ΠΎΠ΄Π΅ Ρ€Π°Π±ΠΎΡ‚Ρ‹ Π±Ρ‹Π»Π° сформирована коллСкция клиничСских случаСв ΠΈ ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ, Π²ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‰Π°Ρ Π°Π½ΠΎΠ½ΠΈΠΌΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½ΡƒΡŽ ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ†ΠΈΡŽ ΠΎ 220 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°Ρ…, ΠΈΠ· Π½ΠΈΡ… 130 - с ΠΈΡˆΠ΅ΠΌΠΈΡ‡Π΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡƒΠ»ΡŒΡ‚ΠΎΠΌ, 40 - с гСморрагичСским ΠΈΠ½ΡΡƒΠ»ΡŒΡ‚ΠΎΠΌ, Π° Ρ‚Π°ΠΊΠΆΠ΅ 50 Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊ Π±Π΅Π· цСрСброваскулярной ΠΏΠ°Ρ‚ΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠšΠ»ΠΈΠ½ΠΈΡ‡Π΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½Ρ‹Π΅ Π²ΠΊΠ»ΡŽΡ‡Π°Π»ΠΈ свСдСния ΠΎ Ρ‚ΠΈΠΏΠ΅ ОНМК, Π½Π°Π»ΠΈΡ‡ΠΈΠΈ ΡΠΎΠΏΡƒΡ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈ ослоТнСний, Π΄Π»ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ госпитализации, способС лСчСния ΠΈ исходС. ВсСго для ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² Π±Ρ‹Π»ΠΈ Π²Π²Π΅Π΄Π΅Π½Ρ‹ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ 370 исслСдований Π½Π°Ρ‚ΠΈΠ²Π½ΠΎΠΉ КВ ΠΈ 102 исслСдования КВ-Π°Π½Π³ΠΈΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ. На ΠΊΠ°ΠΆΠ΄ΠΎΠΉ сСрии ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ Π²Ρ€Π°Ρ‡ΠΎΠΌ-экспСртом Π±Ρ‹Π»ΠΈ ΠΎΠΊΠΎΠ½Ρ‚ΡƒΡ€Π΅Π½Ρ‹ ΠΈ ΠΏΡ€ΠΎΡ‚Π΅Π³ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ области интСрСса, ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΠ΅ прямым ΠΈ косвСнным ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠ°ΠΌ ОНМК.Π’Ρ‹Π²ΠΎΠ΄ Бформированная коллСкция ΠΈΠ·ΠΎΠ±Ρ€Π°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ‚ Π² ΠΏΠΎΡΠ»Π΅Π΄ΡƒΡŽΡ‰Π΅ΠΌ ΠΏΡ€ΠΈΠΌΠ΅Π½ΠΈΡ‚ΡŒ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½Ρ‹Ρ… ΠΈ машинного обучСния Π² Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΈ Π²Π°ΠΆΠ½Π΅ΠΉΡˆΠΈΡ… практичСских Π·Π°Π΄Π°Ρ‡, Π² Ρ‚ΠΎΠΌ числС диагностики Ρ‚ΠΈΠΏΠ° ОНМК, ΠΎΡ†Π΅Π½ΠΊΠΈ объСма пораТСния, ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° стСпСни нСврологичСского Π΄Π΅Ρ„ΠΈΡ†ΠΈΡ‚Π°

    Creation of a Dataset of MSCT-Images and Clinical Data for Acute Cerebrovascular Events

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    Background The use of neuroimaging methods is an integral part of the process of assisting patients with acute cerebrovascular events (ACVE), and computed tomography (CT) is the Β«gold standardΒ» for examining this category of patients. The capabilities of the analysis of CT images may be significantly expanded with modern methods of machine learning including the application of the principles of radiomics. However, since the use of these methods requires large arrays of DICOM (Digital Imaging and Communications in Medicine)-images, their implementation into clinical practice is limited by the lack of representative sample sets. Inaddition, at present, collections (datasets) of CT images of stroke patients, that are suitable for machine learning, are practically not available in the public domain.Aim of study Regarding the aforesaid, the aim of this work was to create a DICOM images dataset of native CT and CT-angiography of patients with different types of stroke. Material and meth ods The collection was based on the medical cases of patients hospitalized in the Regional Vascular Center of the N.V. Sklifosovsky Research Institute for Emergency Medicine. We used a previously developed specialized platform to enter clinical data on the stroke cases, to attach CT DICOMimages to each case, to contour 3D areas of interest, and to tag (label) them. A dictionary was developed for tagging, where elements describe the type of lesion, location, and vascular territory.Results A dataset of clinical cases and images was formed in the course of the work. It included anonymous information about 220 patients, 130 of them with ischemic stroke, 40 with hemorrhagic stroke, and 50 patients without cerebrovascular disorders. Clinical data included information about type of stroke, presence of concomitant diseases and complications, length of hospital stay, methods of treatment, and outcome. The results of 370 studies of native CT and 102 studies of CT-angiography were entered for all patients. The areas of interest corresponding to direct and indirect signs of stroke were contoured and tagged by radiologists on each series of images.Conclusion The resulting collection of images will enable the use of various methods of data analysis and machine learning in solving the most important practical problems including diagnosis of the stroke type, assessment of lesion volume, and prediction of the degree of neurological deficit

    Π­Π›Π•ΠšΠ’Π ΠžΠΠΠ«Π• Π‘Π˜Π“ΠΠ Π•Π’Π«: ΠžΠ¦Π•ΠΠšΠ Π‘Π•Π—ΠžΠŸΠΠ‘ΠΠžΠ‘Π’Π˜ И Π Π˜Π‘ΠšΠžΠ’ Π”Π›Π― Π—Π”ΠžΠ ΠžΠ’Π¬Π―

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    A new product which is an electronic cigarette acting as nicotine delivery has been marketed at early 2000s. An electronic cigarette generates nicotine aerosol from a solution comprised of several basic substances, nicotine and flavours. The electronic cigarettes were widely promoted by a manufacturer like a safe product substituting tobacco smoking during quitting period. As a result, their consumption has been increasing progressively worldwide. Investigators of the aerosol content reported that it mainly contains ultrafine particles which easily penetrate into alveoli and blood vessels. The aerosol also contains nitrosamines, toxic substances and heavy metals. Strong evidence has been made about the aerosol cytotoxicity that could lead to serious injury and diseases. Nicotine dependence was demonstrated to develop as a result of an electronic cigarette smoking. While smoking an electronic cigarette, the indoor air concentration of toxic substances could reach hazardous levels. Electronic cigarettes do not have any advantage and cannot be considered as a mean to quit tobacco smoking. Moreover, electronic cigarette consumers were shown to quit smoking significantly harder. A large body of scientific confirmation of hazardous effect of electronic cigarettes on a human either during active or passive smoking have been obtained. It is no doubt that further investigations are needed especially with regards to rapid change in the market of electronic cigarettes. To minimize adverse effect of electronic cigarettes both on an individual and on population at whole certain measures should be undertaken intended to limitation of demand and supply of electronic cigarettes in the country similar to those for typical tobacco products.Π’ Π½Π°Ρ‡Π°Π»Π΅ 2000-Ρ… Π³ΠΎΠ΄ΠΎΠ² Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ появился Π½ΠΎΠ²Ρ‹ΠΉ ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ – элСктронныС сигарСты (Π­Π‘), ΠΏΡ€Π΅Π΄Π½Π°Π·Π½Π°Ρ‡Π΅Π½Π½Ρ‹Π΅ для доставки Π½ΠΈΠΊΠΎΡ‚ΠΈΠ½Π° Π² ΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌ Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊΠ°. Для этого гСнСрируСтся никотинсодСрТащий Π°ΡΡ€ΠΎΠ·ΠΎΠ»ΡŒ ΠΈΠ· раствора, состоящСго ΠΈΠ· Π½Π΅ΡΠΊΠΎΠ»ΡŒΠΊΠΈΡ… Π±Π°Π·ΠΎΠ²Ρ‹Ρ… вСщСств, Π½ΠΈΠΊΠΎΡ‚ΠΈΠ½Π° ΠΈ Π°Ρ€ΠΎΠΌΠ°Ρ‚ΠΈΠ·Π°Ρ‚ΠΎΡ€ΠΎΠ². ΠŸΡ€ΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΡ‚Π΅Π»ΠΈ Π½Π°Ρ‡Π°Π»ΠΈ ΡˆΠΈΡ€ΠΎΠΊΠΎ Ρ€Π΅ΠΊΠ»Π°ΠΌΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Π­Π‘ ΠΊΠ°ΠΊ бСзопасный ΠΏΡ€ΠΎΠ΄ΡƒΠΊΡ‚ ΠΈ ΠΏΡ€ΠΎΠ΄Π²ΠΈΠ³Π°Ρ‚ΡŒ ΠΈΡ… ΠΊΠ°ΠΊ Π·Π°ΠΌΠ΅ΡΡ‚ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠ΅ срСдство для эффСктивного ΠΎΡ‚ΠΊΠ°Π·Π° ΠΎΡ‚ Ρ‚Π°Π±Π°ΠΊΠ°. Π’ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π΅ Ρ€Π°ΡΠΏΡ€ΠΎΡΡ‚Ρ€Π°Π½Π΅Π½Π½ΠΎΡΡ‚ΡŒ Π­Π‘ Π² ΠΌΠΈΡ€Π΅ Π½Π΅ΡƒΠΊΠ»ΠΎΠ½Π½ΠΎ растСт. Π’ аэрозолС содСрТатся Π² основном ΡƒΠ»ΡŒΡ‚Ρ€Π°ΠΌΠ΅Π»ΠΊΠΈΠ΅ частицы, свободно ΠΏΡ€ΠΎΠ½ΠΈΠΊΠ°ΡŽΡ‰ΠΈΠ΅ Π² Π°Π»ΡŒΠ²Π΅ΠΎΠ»Ρ‹ ΠΈ ΠΊΡ€ΠΎΠ²Π΅Π½ΠΎΡΠ½ΡƒΡŽ систСму Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊΠ°, Π½ΠΈΡ‚Ρ€ΠΎΠ·Π°ΠΌΠΈΠ½Ρ‹, ряд токсичСских вСщСств, тяТСлыС ΠΌΠ΅Ρ‚Π°Π»Π»Ρ‹, Ρ‡Ρ‚ΠΎ подтвСрТдаСтся Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌΠΈ исслСдования Π΅Π³ΠΎ состава, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Ρ‹ Ρ‚Π°ΠΊΠΆΠ΅ строгиС Π΄ΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π° цитотоксичности аэрозоля, Ρ‡Ρ‚ΠΎ ΠΌΠΎΠΆΠ΅Ρ‚ ΠΏΡ€ΠΈΠ²ΠΎΠ΄ΠΈΡ‚ΡŒ ΠΊ Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΡŽ ΡΠ΅Ρ€ΡŒΠ΅Π·Π½Ρ‹Ρ… ΠΏΠΎΠ²Ρ€Π΅ΠΆΠ΄Π΅Π½ΠΈΠΉ ΠΈ Π±ΠΎΠ»Π΅Π·Π½Π΅ΠΉ Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊΠ°. ΠŸΠΎΠ΄Ρ‚Π²Π΅Ρ€ΠΆΠ΄Π΅Π½ΠΎ, Ρ‡Ρ‚ΠΎ ΠΏΡ€ΠΈ ΠΊΡƒΡ€Π΅Π½ΠΈΠΈ Π­Π‘ ΠΌΠΎΠΆΠ΅Ρ‚ Ρ€Π°Π·Π²ΠΈΠ²Π°Ρ‚ΡŒΡΡ никотиновая Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡ‚ΡŒ. УстановлСно, Ρ‡Ρ‚ΠΎ ΠΏΡ€ΠΈ ΠΊΡƒΡ€Π΅Π½ΠΈΠΈ Π­Π‘ Π² ΠΏΠΎΠΌΠ΅Ρ‰Π΅Π½ΠΈΠΈ концСнтрация токсичСских вСщСств достигаСт опасного для Π·Π΄ΠΎΡ€ΠΎΠ²ΡŒΡ Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊΠ° уровня. Π­Π‘ Π½Π΅ ΠΎΠ±Π»Π°Π΄Π°ΡŽΡ‚ Π½ΠΈΠΊΠ°ΠΊΠΈΠΌ прСимущСством ΠΈ Π½Π΅ ΡΠ²Π»ΡΡŽΡ‚ΡΡ эффСктивным срСдством для ΠΎΡ‚ΠΊΠ°Π·Π° ΠΎΡ‚ табакокурСния, Π±ΠΎΠ»Π΅Π΅ Ρ‚ΠΎΠ³ΠΎ, Π΄ΠΎΠΊΠ°Π·Π°Π½ΠΎ, Ρ‡Ρ‚ΠΎ потрСбитСлям Π­Π‘ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Ρ‚Ρ€ΡƒΠ΄Π½Π΅Π΅ Π±Ρ€ΠΎΡΠΈΡ‚ΡŒ ΠΊΡƒΡ€ΠΈΡ‚ΡŒ. Π˜ΠΌΠ΅ΡŽΡ‚ΡΡ достаточно вСскиС Π½Π°ΡƒΡ‡Π½Ρ‹Π΅ Π΄ΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π° Π²Ρ€Π΅Π΄Π½ΠΎΠ³ΠΎ воздСйствия Π­Π‘ Π½Π° ΠΎΡ€Π³Π°Π½ΠΈΠ·ΠΌ Ρ‡Π΅Π»ΠΎΠ²Π΅ΠΊΠ° ΠΊΠ°ΠΊ ΠΏΡ€ΠΈ Π°ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΌ, Ρ‚Π°ΠΊ ΠΈ ΠΏΡ€ΠΈ пассивном ΠΊΡƒΡ€Π΅Π½ΠΈΠΈ. БСзусловно, исслСдования Π΄ΠΎΠ»ΠΆΠ½Ρ‹ Π±Ρ‹Ρ‚ΡŒ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΠΆΠ΅Π½Ρ‹, Ρ‚. ΠΊ. Ρ€Ρ‹Π½ΠΎΠΊ Π­Π‘ быстро измСняСтся. Для ΠΌΠΈΠ½ΠΈΠΌΠΈΠ·Π°Ρ†ΠΈΠΈ Π½Π΅Π³Π°Ρ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΈ популяционного влияния Π­Π‘ слСдуСт ΠΏΡ€ΠΈΠ½ΡΡ‚ΡŒ ряд ΠΌΠ΅Ρ€, Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½Π½Ρ‹Ρ… Π½Π° сокращСниС ΠΈΡ… спроса ΠΈ прСдлоТСния, ΠΊΠ°ΠΊ это Π±Ρ‹Π»ΠΎ принято Π² ΠΎΡ‚Π½ΠΎΡˆΠ΅Π½ΠΈΠΈ Ρ‚Ρ€Π°Π΄ΠΈΡ†ΠΈΠΎΠ½Π½Ρ‹Ρ… Ρ‚Π°Π±Π°Ρ‡Π½Ρ‹Ρ… ΠΈΠ·Π΄Π΅Π»ΠΈΠΉ
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