78 research outputs found
Cluster virial expansion for the equation of state of partially ionized hydrogen plasma
We study the contribution of electron-atom interaction to the equation of
state for partially ionized hydrogen plasma using the cluster-virial expansion.
For the first time, we use the Beth-Uhlenbeck approach to calculate the second
virial coefficient for the electron-atom (bound cluster) pair from the
corresponding scattering phase-shifts and binding energies. Experimental
scattering cross-sections as well as phase-shifts calculated on the basis of
different pseudopotential models are used as an input for the Beth-Uhlenbeck
formula. By including Pauli blocking and screening in the phase-shift
calculation, we generalize the cluster-virial expansion in order to cover also
near solid density plasmas. We present results for the electron-atom
contribution to the virial expansion and the corresponding equation of state,
i.e. pressure, composition, and chemical potential as a function of density and
temperature. These results are compared with semi-empirical approaches to the
thermodynamics of partially ionized plasmas. Avoiding any ill-founded input
quantities, the Beth-Uhlenbeck second virial coefficient for the electron-atom
interaction represents a benchmark for other, semi-empirical approaches.Comment: 16 pages, 10 figures, and 5 tables, resubmitted to PR
Π‘ΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΠΠ‘ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ Π΄Π°Π½Π½ΡΡ ΠΏΡΠΈ ΠΎΡΡΡΡΡ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΡ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ
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.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ ΠΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π½Π΅ΠΉΡΠΎΠ²ΠΈΠ·ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΠΎΡΡΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ ΡΠ°ΡΡΡΡ ΠΏΡΠΎΡΠ΅ΡΡΠ° ΠΎΠΊΠ°Π·Π°Π½ΠΈΡ ΠΏΠΎΠΌΠΎΡΠΈ Π±ΠΎΠ»ΡΠ½ΡΠΌ Ρ ΠΎΡΡΡΡΠΌΠΈ Π½Π°ΡΡΡΠ΅Π½ΠΈΡΠΌΠΈ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ (ΠΠΠΠ), ΠΏΡΠΈ ΡΡΠΎΠΌ Π·ΠΎΠ»ΠΎΡΡΠΌ ΡΡΠ°Π½Π΄Π°ΡΡΠΎΠΌ ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½Π°Ρ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΡ (ΠΠ’). ΠΠ½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎ ΡΠ°ΡΡΠΈΡΠΈΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΏΡΠΈΠ½ΡΠΈΠΏΠΎΠ² ΡΠ°Π΄ΠΈΠΎΠΌΠΈΠΊΠΈ. ΠΠ΄Π½Π°ΠΊΠΎ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΡΡΠ΅Π±ΡΠ΅Ρ Π½Π°Π»ΠΈΡΠΈΡ Π±ΠΎΠ»ΡΡΠΈΡ
ΠΌΠ°ΡΡΠΈΠ²ΠΎΠ² DICOM (Digital Imaging and Communications in Medicine)-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΈΡ
Π²Π½Π΅Π΄ΡΠ΅Π½ΠΈΠ΅ Π² ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΡΡ ΠΏΡΠ°ΠΊΡΠΈΠΊΡ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΎ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ Π½Π°Π±ΠΎΡΠ° ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΡ
Π²ΡΠ±ΠΎΡΠΎΠΊ. ΠΡΠΎΠΌΠ΅ ΡΠΎΠ³ΠΎ, Π² Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ, ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΠΈΠ΅ ΠΠ’-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π±ΠΎΠ»ΡΠ½ΡΡ
c ΠΠΠΠ, ΠΊΠΎΡΠΎΡΡΠ΅ Π±ΡΠ»ΠΈ Π±Ρ ΠΏΡΠΈΠ³ΠΎΠ΄Π½Ρ Π΄Π»Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ.Π¦Π΅Π»Ρ Π ΡΠ²ΡΠ·ΠΈ Ρ Π²ΡΡΠ΅ΡΠΊΠ°Π·Π°Π½Π½ΡΠΌ, ΡΠ΅Π»ΡΡ Π΄Π°Π½Π½ΠΎΠΉ ΡΠ°Π±ΠΎΡΡ ΡΠ²Π»ΡΠ»ΠΎΡΡ ΡΠΎΠ·Π΄Π°Π½ΠΈΠ΅ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ DICOM-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π½Π°ΡΠΈΠ²Π½ΠΎΠΉ ΠΠ’ ΠΈ ΠΠ’-Π°Π½Π³ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠΌΠΈ ΡΠΈΠΏΠ°ΠΌΠΈ ΠΠΠΠ.ΠΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΡΠ½ΠΎΠ²ΠΎΠΉ Π΄Π»Ρ ΡΠΎΠ·Π΄Π°Π½ΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΡΡΠ°Π»ΠΈ ΠΈΡΡΠΎΡΠΈΠΈ Π±ΠΎΠ»Π΅Π·Π½ΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ², Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
Π² ΡΠ΅Π³ΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ ΡΠΎΡΡΠ΄ΠΈΡΡΡΠΉ ΡΠ΅Π½ΡΡ ΠΠΠ Π‘Π ΠΈΠΌ. Π.Π. Π‘ΠΊΠ»ΠΈΡΠΎΡΠΎΠ²ΡΠΊΠΎΠ³ΠΎ. ΠΠ»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΠΈ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π»Π°ΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½Π½Π°Ρ Π½Π°ΠΌΠΈ ΡΠ°Π½Π΅Π΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ°Ρ Π²Π²ΠΎΠ΄ΠΈΡΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΎ ΡΠ»ΡΡΠ°ΡΡ
ΠΠΠΠ, ΠΏΡΠΈΠΊΡΠ΅ΠΏΠ»ΡΡΡ ΠΊ ΠΊΠ°ΠΆΠ΄ΠΎΠΌΡ ΡΠ»ΡΡΠ°Ρ DICOM-ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π½ΡΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΎΠΊΠΎΠ½ΡΡΡΠΈΠ²Π°ΡΡ ΠΈ ΡΠ΅Π³ΠΈΡΠΎΠ²Π°ΡΡ (ΡΠ°Π·ΠΌΠ΅ΡΠ°ΡΡ) 3D-ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°. ΠΠ»Ρ ΡΠ΅Π³ΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π±ΡΠ» ΡΠ°Π·ΡΠ°Π±ΠΎΡΠ°Π½ ΡΠ»ΠΎΠ²Π°ΡΡ, ΡΠ»Π΅ΠΌΠ΅Π½ΡΡ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΠΎΠΏΠΈΡΡΠ²Π°ΡΡ ΡΠΈΠΏ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ, Π»ΠΎΠΊΠ°Π»ΠΈΠ·Π°ΡΠΈΡ ΠΈ Π±Π°ΡΡΠ΅ΠΉΠ½ ΠΊΡΠΎΠ²ΠΎΡΠ½Π°Π±ΠΆΠ΅Π½ΠΈΡ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ Π Ρ
ΠΎΠ΄Π΅ ΡΠ°Π±ΠΎΡΡ Π±ΡΠ»Π° ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΡ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ»ΡΡΠ°Π΅Π² ΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π²ΠΊΠ»ΡΡΠ°ΡΡΠ°Ρ Π°Π½ΠΎΠ½ΠΈΠΌΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Π½ΡΡ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΡ ΠΎ 220 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°Ρ
, ΠΈΠ· Π½ΠΈΡ
130 - Ρ ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ, 40 - Ρ Π³Π΅ΠΌΠΎΡΡΠ°Π³ΠΈΡΠ΅ΡΠΊΠΈΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ 50 ΡΠ΅Π»ΠΎΠ²Π΅ΠΊ Π±Π΅Π· ΡΠ΅ΡΠ΅Π±ΡΠΎΠ²Π°ΡΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ. ΠΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅ Π²ΠΊΠ»ΡΡΠ°Π»ΠΈ ΡΠ²Π΅Π΄Π΅Π½ΠΈΡ ΠΎ ΡΠΈΠΏΠ΅ ΠΠΠΠ, Π½Π°Π»ΠΈΡΠΈΠΈ ΡΠΎΠΏΡΡΡΡΠ²ΡΡΡΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈ ΠΎΡΠ»ΠΎΠΆΠ½Π΅Π½ΠΈΠΉ, Π΄Π»ΠΈΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ Π³ΠΎΡΠΏΠΈΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠΈ, ΡΠΏΠΎΡΠΎΠ±Π΅ Π»Π΅ΡΠ΅Π½ΠΈΡ ΠΈ ΠΈΡΡ
ΠΎΠ΄Π΅. ΠΡΠ΅Π³ΠΎ Π΄Π»Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π±ΡΠ»ΠΈ Π²Π²Π΅Π΄Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ 370 ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ Π½Π°ΡΠΈΠ²Π½ΠΎΠΉ ΠΠ’ ΠΈ 102 ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΠ’-Π°Π½Π³ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ. ΠΠ° ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠ΅ΡΠΈΠΈ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π²ΡΠ°ΡΠΎΠΌ-ΡΠΊΡΠΏΠ΅ΡΡΠΎΠΌ Π±ΡΠ»ΠΈ ΠΎΠΊΠΎΠ½ΡΡΡΠ΅Π½Ρ ΠΈ ΠΏΡΠΎΡΠ΅Π³ΠΈΡΠΎΠ²Π°Π½Ρ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ°, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΡΡΡΠΈΠ΅ ΠΏΡΡΠΌΡΠΌ ΠΈ ΠΊΠΎΡΠ²Π΅Π½Π½ΡΠΌ ΠΏΡΠΈΠ·Π½Π°ΠΊΠ°ΠΌ ΠΠΠΠ.ΠΡΠ²ΠΎΠ΄ Π‘ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½Π°Ρ ΠΊΠΎΠ»Π»Π΅ΠΊΡΠΈΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΡ Π² ΠΏΠΎΡΠ»Π΅Π΄ΡΡΡΠ΅ΠΌ ΠΏΡΠΈΠΌΠ΅Π½ΠΈΡΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΈ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Π² ΡΠ΅ΡΠ΅Π½ΠΈΠΈ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΡ
ΠΏΡΠ°ΠΊΡΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π΄Π°Ρ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΡΠΈΠΏΠ° ΠΠΠΠ, ΠΎΡΠ΅Π½ΠΊΠΈ ΠΎΠ±ΡΠ΅ΠΌΠ° ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π΄Π΅ΡΠΈΡΠΈΡΠ°
ΠΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΠ°
Ischemic stroke is a heterogeneous syndrome with a plurality of potential etiological factors. The routine diagnosis does not always allow the cause of acute cerebrovascular accident to be found, in such cases we talk about cryptogenic ischemic stroke, which incidence is 20-40%. The category of patients with cryptogenic stroke was first characterized and assigned to a separate group in the database of the National Institute of Neurological Diseases and Stroke in the USA, and later in the TOAST study. The diagnosis of cryptogenic stroke is usually based on the exclusion of well-known causes of acute cerebrovascular accidents, such as atherosclerosis, cardiac arrhythmias, arterial hypertension. Due to the considerable variability of concepts for cryptogenic stroke, the term ESUS (Embolic Stroke of Undetermined Source) appeared in 2014 and formulated criteria which accurately characterized these patients: non-lacunar cerebral infarction by CT and/or MRI, no atherosclerotic lesion stenosing a stroke-associated artery of more than 50%, no sources of high-risk cardioembolism, no other causes of stroke such as dissection of the artery supplying the area of infarction in the brain, migraine, arteritis. Among the potential causes and sources of cerebral embolism in patients with cryptogenic stroke are heart, veins of lower extremities and pelvis, nonstenosing atherosclerosis of brachiocephalic artery, atheroma of aortic arch, paradoxical embolism non-atherosclerotic vasculopathy, monogenic diseases, hypercoagulable states, and others. We should note that there is a lot of studies on the possible causes of cryptogenic stroke in the available literature, but no common approach to classification of etiologic factors and examination algorythms were developed. The high incidence of cryptogenic stroke, the significant heterogeneity of its etiopathogenetic mechanisms and the need for differentiated approaches to the secondary prevention of this type of acute cerebrovascular accident determine the relevance of further studies in this field.ΠΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΈΠ½ΡΡΠ»ΡΡ ΡΠ²Π»ΡΠ΅ΡΡΡ Π³Π΅ΡΠ΅ΡΠΎΠ³Π΅Π½Π½ΡΠΌ ΡΠΈΠ½Π΄ΡΠΎΠΌΠΎΠΌ Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²ΠΎΠΌ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΠΎ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ². Π ΡΡΠΈΠ½Π½Π°Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° Π½Π΅ Π²ΡΠ΅Π³Π΄Π° ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ ΠΏΡΠΈΡΠΈΠ½Ρ ΠΎΡΡΡΠΎΠ³ΠΎ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΠΌΠΎΠ·Π³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΡΠΎΠ²ΠΎΠΎΠ±ΡΠ°ΡΠ΅Π½ΠΈΡ (ΠΠΠΠ), Π² ΡΠ°ΠΊΠΈΡ
ΡΠ»ΡΡΠ°ΡΡ
ΠΏΡΠΈΠ½ΡΡΠΎ Π³ΠΎΠ²ΠΎΡΠΈΡΡ ΠΎ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠΌ ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠ΅, ΡΠ°ΡΡΠΎΡΠ° ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ 20β40%. ΠΠ°ΡΠ΅Π³ΠΎΡΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΡΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ Π±ΡΠ»Π° ΠΎΡ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π° ΠΈ Π²ΡΠ΄Π΅Π»Π΅Π½Π° Π² ΠΎΡΠ΄Π΅Π»ΡΠ½ΡΡ Π³ΡΡΠΏΠΏΡ Π² Π±Π°Π·Π΅ Π΄Π°Π½Π½ΡΡ
ΠΠ°ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠΈΡΡΡΠ° Π½Π΅Π²ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΠΉ ΠΈ ΠΈΠ½ΡΡΠ»ΡΡΠ° Π‘Π¨Π, Π° Π²ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΈ Π² ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ TOAST. ΠΠΈΠ°Π³Π½ΠΎΠ· ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΠ°, ΠΊΠ°ΠΊ ΠΏΡΠ°Π²ΠΈΠ»ΠΎ, Π±Π°Π·ΠΈΡΡΠ΅ΡΡΡ Π½Π° ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΠΈ Ρ
ΠΎΡΠΎΡΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
ΠΏΡΠΈΡΠΈΠ½ ΠΠΠΠ, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ Π°ΡΠ΅ΡΠΎΡΠΊΠ»Π΅ΡΠΎΠ·, Π½Π°ΡΡΡΠ΅Π½ΠΈΡ ΡΠΈΡΠΌΠ° ΡΠ΅ΡΠ΄ΡΠ°, Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½Π°Ρ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΡ. Π ΡΠ²ΡΠ·ΠΈ ΡΠΎ Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ Π²Π°ΡΠΈΠ°Π±Π΅Π»ΡΠ½ΠΎΡΡΡΡ ΠΏΠΎΠ½ΡΡΠΈΡ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΠ° Π² 2014 Π³. Π±ΡΠ» Π²Π²Π΅Π΄Π΅Π½ ΡΠ΅ΡΠΌΠΈΠ½ ESUS (Embolic Stroke of Undetermined Source β ΡΠΌΠ±ΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΠΈΠ½ΡΡΠ»ΡΡ Ρ Π½Π΅ΡΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½Π½ΡΠΌ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠΌ) ΠΈ ΡΡΠΎΡΠΌΡΠ»ΠΈΡΠΎΠ²Π°Π½Ρ ΠΊΡΠΈΡΠ΅ΡΠΈΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ΅ΡΠΊΠΎ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΡΡ ΡΠ°ΠΊΠΈΡ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ²: Π½Π΅Π»Π°ΠΊΡΠ½Π°ΡΠ½ΡΠΉ ΠΈΠ½ΡΠ°ΡΠΊΡ ΠΌΠΎΠ·Π³Π° ΠΏΠΎ Π΄Π°Π½Π½ΡΠΌ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠΉ ΠΈ/ΠΈΠ»ΠΈ ΠΌΠ°Π³Π½ΠΈΡΠ½ΠΎ-ΡΠ΅Π·ΠΎΠ½Π°Π½ΡΠ½ΠΎΠΉ ΡΠΎΠΌΠΎΠ³ΡΠ°ΡΠΈΠΈ, ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ Π°ΡΠ΅ΡΠΎΡΠΊΠ»Π΅ΡΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΠΎΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΡΡΠ΅Π½ΠΎΠ·ΠΈΡΡΡΡΠ΅Π³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΡΠ²ΡΠ·Π°Π½Π½ΡΡ Π°ΡΡΠ΅ΡΠΈΡ Π±ΠΎΠ»Π΅Π΅ ΡΠ΅ΠΌ Π½Π° 50%, ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΠΊΠ°ΡΠ΄ΠΈΠΎΡΠΌΠ±ΠΎΠ»ΠΈΠΈ Π²ΡΡΠΎΠΊΠΎΠ³ΠΎ ΡΠΈΡΠΊΠ°, ΠΎΡΡΡΡΡΡΠ²ΠΈΠ΅ Π΄ΡΡΠ³ΠΈΡ
ΠΏΡΠΈΡΠΈΠ½ ΠΈΠ½ΡΡΠ»ΡΡΠ°, ΡΠ°ΠΊΠΈΡ
ΠΊΠ°ΠΊ Π΄ΠΈΡΡΠ΅ΠΊΡΠΈΡ Π°ΡΡΠ΅ΡΠΈΠΈ, ΠΏΠΈΡΠ°ΡΡΠ΅ΠΉ ΠΎΠ±Π»Π°ΡΡΡ ΠΈΠ½ΡΠ°ΡΠΊΡΠ° ΠΌΠΎΠ·Π³Π°, ΠΌΠΈΠ³ΡΠ΅Π½Ρ, Π°ΡΡΠ΅ΡΠΈΠΈΡ. Π‘ΡΠ΅Π΄ΠΈ ΠΏΠΎΡΠ΅Π½ΡΠΈΠ°Π»ΡΠ½ΡΡ
ΠΏΡΠΈΡΠΈΠ½ ΠΈ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ² ΡΠ΅ΡΠ΅Π±ΡΠ°Π»ΡΠ½ΠΎΠΉ ΡΠΌΠ±ΠΎΠ»ΠΈΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΡΠΌ ΠΈΠ½ΡΡΠ»ΡΡΠΎΠΌ Π΄ΠΎΠ»ΠΆΠ½Ρ Π±ΡΡΡ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ ΡΠ΅ΡΠ΄ΡΠ΅, Π²Π΅Π½Ρ Π½ΠΈΠΆΠ½ΠΈΡ
ΠΊΠΎΠ½Π΅ΡΠ½ΠΎΡΡΠ΅ΠΉ ΠΈ ΡΠ°Π·Π°, Π½Π΅ΡΡΠ΅Π½ΠΎΠ·ΠΈΡΡΡΡΠΈΠΉ Π°ΡΠ΅ΡΠΎΡΠΊΠ»Π΅ΡΠΎΠ· Π±ΡΠ°Ρ
ΠΈΠΎΡΠ΅ΡΠ°Π»ΡΠ½ΡΡ
Π°ΡΡΠ΅ΡΠΈΠΉ, Π°ΡΠ΅ΡΠΎΠΌΡ Π΄ΡΠ³ΠΈ Π°ΠΎΡΡΡ, ΠΏΠ°ΡΠ°Π΄ΠΎΠΊΡΠ°Π»ΡΠ½Π°Ρ ΡΠΌΠ±ΠΎΠ»ΠΈΡ, Π½Π΅Π°ΡΠ΅ΡΠΎΡΠΊΠ»Π΅ΡΠΎΡΠΈΡΠ΅ΡΠΊΠ°Ρ Π²Π°ΡΠΊΡΠ»ΠΎΠΏΠ°ΡΠΈΡ, ΠΌΠΎΠ½ΠΎΠ³Π΅Π½Π½ΡΠ΅ Π·Π°Π±ΠΎΠ»Π΅Π²Π°Π½ΠΈΡ, Π³ΠΈΠΏΠ΅ΡΠΊΠΎΠ°Π³ΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΈ Π΄Ρ. Π‘Π»Π΅Π΄ΡΠ΅Ρ ΠΎΡΠΌΠ΅ΡΠΈΡΡ, ΡΡΠΎ Π² Π΄ΠΎΡΡΡΠΏΠ½ΠΎΠΉ Π½Π°ΠΌ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ΅ ΠΈΠΌΠ΅Π΅ΡΡΡ Π±ΠΎΠ»ΡΡΠΎΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ, ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΡΡ
ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΡ
ΠΏΡΠΈΡΠΈΠ½ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΠ°, ΠΎΠ΄Π½Π°ΠΊΠΎ Π΅Π΄ΠΈΠ½ΡΠ΅ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄Ρ ΠΊ ΡΠΈΡΡΠ΅ΠΌΠ°ΡΠΈΠ·Π°ΡΠΈΠΈ ΡΡΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² ΠΈ ΠΏΡΠΎΡΠΎΠΊΠΎΠ»Π° ΠΎΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΎΡΡΡΡΡΡΠ²ΡΡΡ. ΠΡΡΠΎΠΊΠ°Ρ ΡΠ°ΡΡΠΎΡΠ° ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΊΡΠΈΠΏΡΠΎΠ³Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΡΠ»ΡΡΠ°, Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π³Π΅ΡΠ΅ΡΠΎΠ³Π΅Π½Π½ΠΎΡΡΡ Π΅Π³ΠΎ ΡΡΠΈΠΎΠΏΠ°ΡΠΎΠ³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ², Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΠΎΡΡΡ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΠΎΠ² ΠΊΠΎ Π²ΡΠΎΡΠΈΡΠ½ΠΎΠΉ ΠΏΡΠΎΡΠΈΠ»Π°ΠΊΡΠΈΠΊΠ΅ Π΄Π°Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΏΠ° ΠΠΠΠ ΠΎΠ±ΡΡΠ»Π°Π²Π»ΠΈΠ²Π°ΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΡ
ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΠΏΡΠΈ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ
Dynamical correlations and collective excitations of Yukawa liquids
In dusty (complex) plasmas, containing mesoscopic charged grains, the
grain-grain interaction in many cases can be well described through a Yukawa
potential. In this Review we summarize the basics of the computational and
theoretical approaches capable of describing many-particle Yukawa systems in
the liquid and solid phases and discuss the properties of the dynamical density
and current correlation spectra of three- and two-dimensional strongly coupled
Yukawa systems, generated by molecular dynamics simulations. We show details of
the dispersion relations for the collective excitations in these
systems, as obtained theoretically following the quasilocalized charge
approximation, as well as from the fluctuation spectra created by simulations.
The theoretical and simulation results are also compared with those obtained in
complex plasma experiments.Comment: 54 pages, 31 figure
ΠΠΠ€ΠΠ ΠΠ’ ΠΠΠΠΠΠΠΠΠ ΠΠΠΠΠ ΠΠΠ ΠΠΠ ΠΠΠ ΠΠ ΠΠ―ΠΠΠΠΠΠ ΠΠ ΠΠ’Π ΠΠΠΠ
The article reports a clinical case of the stroke on the background of newly diagnosed polycythemiaΒ vera. Possible mechanisms of the stroke in the course of erythremia as well ascurrent methods oftreatment for the disease are described.Π ΡΡΠ°ΡΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ ΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΠ»ΡΡΠ°ΠΉ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΈΠ½ΡΡΠ»ΡΡΠ° Π½Π° ΡΠΎΠ½Π΅ Π²ΠΏΠ΅ΡΠ²ΡΠ΅ Π²ΡΡΠ²Π»Π΅Π½Π½ΠΎΠΉ ΠΈΡΡΠΈΠ½Π½ΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΡΠ΅ΠΌΠΈΠΈ. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΡΠ΅ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΡ ΡΠ°Π·Π²ΠΈΡΠΈΡ ΠΈΠ½ΡΡΠ»ΡΡΠ° ΠΏΡΠΈ ΡΡΠΈΡΡΠ΅ΠΌΠΈΠΈ.Β ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΡΠ΅ ΠΌΠ΅ΡΠΎΠ΄Ρ Π»Π΅ΡΠ΅Π½ΠΈΡ Π΄Π°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΠΈ
Influence of the quantum interference on the bosonic and fermionic ion-ion collisions
The quantum interference effects on the bosonic-bosonic (He-4)-(He-4), fermionic-fermionic (He-3)-(He-3), and bosonic-fermionic (He-4)-(He-3) ion-ion collisions are investigated by using the isotope of the He nucleus in dense semiclassical Coulomb systems with the Faxen-Holtzmark method. It is found that the scattering cross section for the fermionic-fermionic ion-ion collision is greater than the bosonic-bosonic and bosonic-fermionic ion collision cross sections. It is also found that the collisional induced quantum interference effect enhances the ion-ion collision cross section in semiclassical Coulomb systems. The variation of the quantum-mechanical effect on the bosonic and fermionic ion-ion collisions is also discussed.
This paper is dedicated to the late Prof. P. K. Shukla in memory of exciting and stimulating collaborations on physical processes in semiclassical Coulomb systems
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