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Xu J, Xu J, Tong Z, Du B, Liu B, Mu X, Guo T, Yu S, Liu S, Gao C, Wang J, Liu Z, Zhang P. Performance of feature extraction method for classification and identification of proteins based on three-dimensional fluorescence spectrometry. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 285:121841. [PMID: 36179565 DOI: 10.1016/j.saa.2022.121841] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 06/16/2023]
Abstract
Three-dimensional excitation emission matrix (EEM) fluorescence spectroscopy was employed to discriminate protein samples comprising bovine serum albumin, neurotensin, ovalbumin, ricin, trypsin from bovine pancreas and trypsin from porcine pancreas. Two methods of feature extraction with and without parameterization were applied to the spectral data in order to evaluate their performance of discrimination between protein samples. The discrimination of protein samples was conducted by k-means clustering algorithm and eigenvalue extracting procedure based on principal component analysis (PCA). It was found that the method of feature extraction without parameterization performed best, correctly attributing 100% of the spectral data in the condition of two principal components (PCs) captured. Features extracted with spectral parameterization failed to separate ricin and trypsin from bovine pancreas in same condition. Without spectral parameterization, less dimensionality and unique principal components captured by PCA indicates the spectrally-resolved features of corresponding protein samples. By clustering using each spectrum at fixed excitation wavelength, excitation wavelengths matched with common intrinsic fluorophores were found to be more sensitive to the classification accuracy. Contributions of spectral features extracted from EEM to the principal components were discussed and demonstrated their feature differentiation capabilities among six protein samples. These results reveal that appropriate extraction approach of features in combination with PCA analysis could be used in discrimination of protein samples at species level as a spectroscopic diagnostic tool. Our study provides fundamental references about computational strategies when EEM are used to explore proteins in ambient environment.
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Affiliation(s)
- Jiwei Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jianjie Xu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China.
| | - Zhaoyang Tong
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bin Du
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Bing Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Xihui Mu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Tengxiao Guo
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Siqi Yu
- School of Environmental Science and Optoelectronic Technology, University of Science and Technology of China, Hefei, 230026, Anhui, China
| | - Shuai Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Chuan Gao
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Jiang Wang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Zhiwei Liu
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
| | - Pengjie Zhang
- State Key Laboratory of NBC Protection for Civilian, Beijing, 102205, China
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Tikhvinskii D, Kuianova J, Kislitsin D, Orlov K, Gorbatykh A, Parshin D. Numerical Assessment of the Risk of Abnormal Endothelialization for Diverter Devices: Clinical Data Driven Numerical Study. J Pers Med 2022; 12:652. [PMID: 35455768 PMCID: PMC9025183 DOI: 10.3390/jpm12040652] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 04/07/2022] [Accepted: 04/08/2022] [Indexed: 12/07/2022] Open
Abstract
Numerical modeling is an effective tool for preoperative planning. The present work is devoted to a retrospective analysis of neurosurgical treatments for the occlusion of cerebral aneurysms using flow-diverters and hemodynamic factors affecting stent endothelization. Several different geometric approaches have been considered for virtual flow-diverters deployment. A comparative analysis of hemodynamic parameters as a result of computational modeling has been carried out basing on the four clinical cases: one successful treatment, one with no occlusion and two with in stent stenosis. For the first time, a quantitative assessment of both: the limiting magnitude of shear stresses that are necessary for the occurrence of in stent stenosis (MaxWSS > 1.23) and for conditions in which endothelialization is insufficiently active and occlusion of the cervical part of the aneurysm does not occur (MaxWSS < 1.68)—has been statistacally proven (p < 0.01).
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Affiliation(s)
- Denis Tikhvinskii
- Lavrentyev Institute of Hydrodynamics SB RAS, Lavrentiev Avenue 15, 630090 Novosibirsk, Russia; (D.T.); (J.K.)
| | - Julia Kuianova
- Lavrentyev Institute of Hydrodynamics SB RAS, Lavrentiev Avenue 15, 630090 Novosibirsk, Russia; (D.T.); (J.K.)
| | - Dmitrii Kislitsin
- Neurosurgery Department, Meshalkin National Medical Research Center, 630055 Novosibirsk, Russia; (D.K.); (K.O.); (A.G.)
| | - Kirill Orlov
- Neurosurgery Department, Meshalkin National Medical Research Center, 630055 Novosibirsk, Russia; (D.K.); (K.O.); (A.G.)
| | - Anton Gorbatykh
- Neurosurgery Department, Meshalkin National Medical Research Center, 630055 Novosibirsk, Russia; (D.K.); (K.O.); (A.G.)
| | - Daniil Parshin
- Lavrentyev Institute of Hydrodynamics SB RAS, Lavrentiev Avenue 15, 630090 Novosibirsk, Russia; (D.T.); (J.K.)
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