41 research outputs found

    ΠšΠ»Π°ΡΡΠΈΡ„ΠΈΠΊΠ°Ρ†ΠΈΡ Π²Π½ΡƒΡ‚Ρ€ΠΈΡ‡Π΅Ρ€Π΅ΠΏΠ½Ρ‹Ρ… ΠΎΠΏΡƒΡ…ΠΎΠ»Π΅ΠΉ Π½Π° основС ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°

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    The motivation for the present study was the need to develop methods of urgent intraoperative biopsy during surgery for removal of intracranial tumors. Based on the experience of previous joint work of GPI RAS and N.N. Burdenko National Medical Research Center of Neurosurgery to introduce fluorescence spectroscopy methods into clinical practice, an approach combiningΒ  various optical-spectral techniques, such as autofluorescence spectroscopy, fluorescence of 5-ALA induced protoporphyrin IX, diffuse reflection of broadband light, which can be used to determine hemoglobin concentration in tissues and their optical density, Raman spectroscopy, which is a spectroscopic method that allows detection of various molecules in tissues by vibrations of individual characteristic molecular bonds. Such a variety of optical and spectral characteristics makes it difficult for the surgeon to analyze them directly during surgery, as it is usually realized in the case of fluorescence methods – tumor tissue can be distinguished from normal with a certain degree of certainty by fluorescence intensity exceedingΒ  a threshold value. In case the number of parameters exceeds a couple of dozens, it is necessary to use machine learning algorithmsΒ  to build a intraoperative decision support system for the surgeon. This paper presents research in this direction. Our earlier statistical analysis of the optical-spectral features allowed identifyingΒ  statistically significant spectral ranges for analysis of diagnosticallyΒ  important tissue components. Studies of dimensionality reduction techniques of the optical-spectral feature vector and methods of clustering of the studied samples also allowed us to approach the implementationΒ  of the automatic classification method. Importantly, the classification task can be used in two applicationsΒ  – to differentiate between different tumors and to differentiate between different parts of the same (center, perifocal zone, normal) tumor. This paper presents the results of our research in the first direction. We investigated the combination of several methods and showed the possibility of differentiating glial and meningeal tumors based on the proposed optical-spectral analysis method.ΠœΠΎΡ‚ΠΈΠ²Π°Ρ†ΠΈΠ΅ΠΉ провСдСния настоящСго исслСдования послуТила Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒ развития ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² срочной ΠΈΠ½Ρ‚Ρ€Π°ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠΉ биопсии ΠΏΡ€ΠΈ ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΉ ΠΏΠΎ ΠΏΠΎΠ²ΠΎΠ΄Ρƒ удалСния Π²Π½ΡƒΡ‚Ρ€ΠΈΡ‡Π΅Ρ€Π΅ΠΏΠ½Ρ‹Ρ… ΠΎΠΏΡƒΡ…ΠΎΠ»Π΅ΠΉ. На основании ΠΎΠΏΡ‹Ρ‚Π° ΠΏΡ€Π΅Π΄Ρ‹Π΄ΡƒΡ‰Π΅ΠΉ совмСстной Ρ€Π°Π±ΠΎΡ‚Ρ‹ ИОЀ РАН ΠΈ НМИЦ Π½Π΅ΠΉΡ€ΠΎΡ…ΠΈΡ€ΡƒΡ€Π³ΠΈΠΈΒ  ΠΈΠΌ. Н.Н. Π‘ΡƒΡ€Π΄Π΅Π½ΠΊΠΎ ΠΏΠΎ Π²Π½Π΅Π΄Ρ€Π΅Π½ΠΈΡŽ Π² ΠΊΠ»ΠΈΠ½ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΡƒ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² флуорСсцСнтной спСктроскопии Π±Ρ‹Π» Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½ ΠΏΠΎΠ΄Ρ…ΠΎΠ΄, ΠΊΠΎΠΌΠ±ΠΈΠ½ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠΉ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Π΅Β  ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ, Ρ‚Π°ΠΊΠΈΠ΅ ΠΊΠ°ΠΊ спСктроскопия  аутофлуорСсцСнции, флуорСсцСнции  5-ΠΠ›Πš ΠΈΠ½Π΄ΡƒΡ†ΠΈΡ€ΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎΒ  ΠΏΡ€ΠΎΡ‚ΠΎΠΏΠΎΡ€Ρ„ΠΈΡ€ΠΈΠ½Π°Β  IX, Π΄ΠΈΡ„Ρ„ΡƒΠ·Π½ΠΎΠ³ΠΎΒ  отраТСния  ΡˆΠΈΡ€ΠΎΠΊΠΎΠΏΠΎΠ»ΠΎΡΠ½ΠΎΠ³ΠΎ излучСния,Β  ΠΏΠΎ ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΌΡƒ ΠΌΠΎΠΆΠ½ΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΡΡ‚ΡŒ ΠΊΠΎΠ½Ρ†Π΅Π½Ρ‚Ρ€Π°Ρ†ΠΈΡŽ Π³Π΅ΠΌΠΎΠ³Π»ΠΎΠ±ΠΈΠ½Π° Π² тканях ΠΈ ΠΈΡ… ΠΎΠΏΡ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽΒ  ΠΏΠ»ΠΎΡ‚Π½ΠΎΡΡ‚ΡŒ,Β  спСктроскопия  ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎΒ  рассСяния, ΡΠ²Π»ΡΡŽΡ‰Π°ΡΡΡ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ молСкулярной спСктроскопии, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‰ΠΈΠΌ Π΄Π΅Ρ‚Π΅ΠΊΡ‚ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Π΅ ΠΌΠΎΠ»Π΅ΠΊΡƒΠ»Ρ‹ Π² тканях Π·Π° счСта ΠΊΠΎΠ»Π΅Π±Π°Π½ΠΈΠΉ ΠΎΡ‚Π΄Π΅Π»ΡŒΠ½Ρ‹Ρ… Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π½Ρ‹Ρ… связСй Π² ΠΌΠΎΠ»Π΅ΠΊΡƒΠ»Π°Ρ…. Π’Π°ΠΊΠΎΠ΅ Ρ€Π°Π·Π½ΠΎΠΎΠ±Ρ€Π°Π·ΠΈΠ΅ ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик  затрудняСт ΠΈΡ… нСпосрСдствСнный Π°Π½Π°Π»ΠΈΠ· Ρ…ΠΈΡ€ΡƒΡ€Π³ΠΎΠΌ Π²ΠΎ врСмя ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ, ΠΊΠ°ΠΊ это ΠΎΠ±Ρ‹Ρ‡Π½ΠΎ рСализуСтся Π² случаС флуорСсцСнтных ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² – ΠΏΠΎ ΠΏΡ€Π΅Π²Ρ‹ΡˆΠ΅Π½ΠΈΡŽ Π½Π΅ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠ³ΠΎ ΠΏΠΎΡ€ΠΎΠ³Π° интСнсивности флуорСсцСнции с ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠΉ ΡΡ‚Π΅ΠΏΠ΅Π½ΡŒΡŽ достовСрности ΠΌΠΎΠΆΠ½ΠΎ ΡΡƒΠ΄ΠΈΡ‚ΡŒ ΠΎ Ρ‚ΠΎΠΌ, находится Π»ΠΈ Π² Π·ΠΎΠ½Π΅ исслСдования Π½ΠΎΡ€ΠΌΠ°Π»ΡŒΠ½Π°Ρ ΠΈΠ»ΠΈ опухолСвая Ρ‚ΠΊΠ°Π½ΡŒ. Π’ случаС, Ссли число ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΠΏΡ€Π΅Π²Ρ‹ΡˆΠ°Π΅Ρ‚ ΠΏΠ°Ρ€Ρƒ дСсятков, Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎ использованиС Π°Π»Π³ΠΎΡ€ΠΈΡ‚ΠΌΠΎΠ² машинного обучСния для построСния  систСмы ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠΈ принятия  Ρ€Π΅ΡˆΠ΅Π½ΠΈΠΉ Ρ…ΠΈΡ€ΡƒΡ€Π³Π°Β  Π²ΠΎ врСмя ΠΎΠΏΠ΅Ρ€Π°Ρ†ΠΈΠΈ. Настоящая Ρ€Π°Π±ΠΎΡ‚Π° прСдставляСт исслСдования Π² этом Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠΈ. ΠŸΡ€ΠΎΠ²Π΅Π΄Π΅Π½Π½Ρ‹ΠΉ Π½Π°ΠΌΠΈ Ρ€Π°Π½Π΅Π΅ статистичСский  Π°Π½Π°Π»ΠΈΠ· Π΄Π°Π½Π½Ρ‹Ρ… ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… характСристик  ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ» Π²Ρ‹Π΄Π΅Π»ΠΈΡ‚ΡŒ статистичСски  Π·Π½Π°Ρ‡ΠΈΠΌΡ‹Π΅ ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Π΅ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Ρ‹ для Π°Π½Π°Π»ΠΈΠ·Π°, Ρ€Π΅ΠΏΡ€Π΅Π·Π΅Π½Ρ‚ΠΈΡ€ΡƒΡŽΡ‰ΠΈΠ΅Β  диагностичСски  Π²Π°ΠΆΠ½Ρ‹Π΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Ρ‹ Ρ‚ΠΊΠ°Π½Π΅ΠΉ. ИсслСдования ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² пониТСния размСрности Π²Π΅ΠΊΡ‚ΠΎΡ€Π° ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΈΠ·Π½Π°ΠΊΠΎΠ²Β  ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² кластСризации исслСдуСмых ΠΎΠ±Ρ€Π°Π·Ρ†ΠΎΠ² Ρ‚Π°ΠΊΠΆΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΈ ΠΏΡ€ΠΈΠ±Π»ΠΈΠ·ΠΈΡ‚ΡŒΡΡ ΠΊ Ρ€Π΅Π°Π»ΠΈΠ·Π°Ρ†ΠΈΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° автоматичСской классификации. Π’Π°ΠΆΠ½ΠΎ ΠΎΡ‚ΠΌΠ΅Ρ‚ΠΈΡ‚ΡŒ, Ρ‡Ρ‚ΠΎ Π·Π°Π΄Π°Ρ‡Π° классификации ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ использована Π² Π΄Π²ΡƒΡ… прилоТСниях – для Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠ°Ρ†ΠΈΠΈ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… ΠΎΠΏΡƒΡ…ΠΎΠ»Π΅ΠΉ ΠΈ для Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠ°Ρ†ΠΈΠΈ Ρ€Π°Π·Π»ΠΈΡ‡Π½Ρ‹Ρ… частСй ΠΎΠ΄Π½ΠΎΠΉ (Ρ†Π΅Π½Ρ‚Ρ€, ΠΏΠ΅Ρ€ΠΈΡ„ΠΎΠΊΠ°Π»ΡŒΠ½Π°Ρ Π·ΠΎΠ½Π°, Π½ΠΎΡ€ΠΌΠ°) ΠΎΠΏΡƒΡ…ΠΎΠ»ΠΈ. Π’ настоящСй Ρ€Π°Π±ΠΎΡ‚Π΅ прСдставлСны Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ Π½Π°ΡˆΠΈΡ… исслСдований Π² ΠΏΠ΅Ρ€Π²ΠΎΠΌ Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΠΈ. ΠœΡ‹ исслСдовали сочСтаниС Π½Π΅ΡΠΊΠΎΠ»ΡŒΠΊΠΈΡ… ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠ² ΠΈ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡ‚ΡŒ Π΄ΠΈΡ„Ρ„Π΅Ρ€Π΅Π½Ρ†ΠΈΠ°Ρ†ΠΈΠΈ Π³Π»ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΈ ΠΌΠ΅Π½ΠΈΠ½Π³Π΅Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΎΠΏΡƒΡ…ΠΎΠ»Π΅ΠΉ Π½Π° основании ΠΏΡ€Π΅Π΄Π»ΠΎΠΆΠ΅Π½Π½ΠΎΠ³ΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° ΠΎΠΏΡ‚ΠΈΠΊΠΎ-ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°

    The dipolar endofullerene HF@C60

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    The cavity inside fullerenes provides a unique environment for the study of isolated atoms and molecules. We report encapsulation of hydrogen fluoride inside C60 using molecular surgery to give the endohedral fullerene HF@C60. The key synthetic step is the closure of the open fullerene cage while minimizing escape of HF. The encapsulated HF molecule moves freely inside the cage and exhibits quantization of its translational and rotational degrees of freedom, as revealed by inelastic neutron scattering and infrared spectroscopy. The rotational and vibrational constants of the encapsulated HF molecules were found to be redshifted relative to free HF. The NMR spectra display a large 1H-19F J coupling typical of an isolated species. The dipole moment of HF@C60 was estimated from the temperature-dependence of the dielectric constant at cryogenic temperatures and showed that the cage shields around 75% of the HF dipole
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