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Ge M, Wang Y, Mu N, Yang C, Li H, Chen T, Xu D, Yao J. Study of the relationship among biomarkers, cell and tissue of glioma through Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 325:125063. [PMID: 39232314 DOI: 10.1016/j.saa.2024.125063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 07/21/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
Glioma is the most common brain tumors with high mortality and recurrence rates. Currently, the diagnosis methods for glioma are mainly based on tissue level, cellular level and biomarker level. In this paper, the characteristics of biomarkers (γ-aminobutyric acid and matrix mtalloproteinses-2), U87MG glioma cell and tissue were studied based on Raman spectroscopy, respectively. The results showed that the γ-aminobutyric acid concentration exhibited a linear relation with the intensity of characteristic peaks in 800-1600 cm-1 region, whereas the spectral baseline increased with the increasing of sample concentration in 200-700 cm-1 region. The Raman characteristics of matrix mtalloproteinses-2 in 20-1800 cm-1 region was investigated. Especially, it is demonstrated that the matrix mtalloproteinses-2 showed sixteen low-wavenumber Raman peaks in the range of 20-300 cm-1. Moreover, the U87MG glioma cell showed seven different Raman characteristic peaks in 600-1800 cm-1 region. Compared with the normal tissue, the Raman intensity of tumor tissue showed apparent intensity differences in 300-1800 cm-1, where the intensity changes of these Raman peaks were related to the reducing of the lipid metabolic pathways, and increase of the RNA in tumor tissue region. Furthermore, it is found that the Raman spectra of U87MG glioma cell and tumor tissue had corresponding peaks in the Raman spectra of the liquid γ-aminobutyric acid and matrix mtalloproteinses-2. It is suggested that the γ-aminobutyric acid and matrix mtalloproteinses-2 contributed to the formation and growth of glioma cell and tissue. Thus, Raman spectroscopy not only can diagnose glioma at the biomarkers, cellular and tissue level, but also analyze the relationship among the three. Furthermore, the results provided a physical marker for the detection of glioma in clinically.
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Affiliation(s)
- Meilan Ge
- School of Precision Instruments and Optoelectronics Engineering Tianjin University, Tianjin 300073, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300073, China
| | - Yuye Wang
- School of Precision Instruments and Optoelectronics Engineering Tianjin University, Tianjin 300073, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300073, China.
| | - Ning Mu
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Chuanyan Yang
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Haibin Li
- School of Precision Instruments and Optoelectronics Engineering Tianjin University, Tianjin 300073, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300073, China
| | - Tunan Chen
- Department of Neurosurgery and Key Laboratory of Neurotrauma, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China
| | - Degang Xu
- School of Precision Instruments and Optoelectronics Engineering Tianjin University, Tianjin 300073, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300073, China
| | - Jianquan Yao
- School of Precision Instruments and Optoelectronics Engineering Tianjin University, Tianjin 300073, China; Key Laboratory of Optoelectronics Information Technology (Ministry of Education), Tianjin University, Tianjin 300073, China
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Becker N, Camelo-Piragua S, Conway KS. A Contemporary Approach to Intraoperative Evaluation in Neuropathology. Arch Pathol Lab Med 2024; 148:649-658. [PMID: 37694565 DOI: 10.5858/arpa.2023-0097-ra] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/27/2023] [Indexed: 09/12/2023]
Abstract
CONTEXT.— Although the basic principles of intraoperative diagnosis in surgical neuropathology have not changed in the last century, the last several decades have seen dramatic changes in tumor classification, terminology, molecular classification, and modalities used for intraoperative diagnosis. As many neuropathologic intraoperative diagnoses are performed by general surgical pathologists, awareness of these recent changes is important for the most accurate intraoperative diagnosis. OBJECTIVE.— To describe recent changes in the practice of intraoperative surgical neuropathology, with an emphasis on new entities, tumor classification, and anticipated ancillary tests, including molecular testing. DATA SOURCES.— The sources for this review include the fifth edition of the World Health Organization Classification of Tumours of the Central Nervous System, primary literature on intraoperative diagnosis and newly described tumor entities, and the authors' clinical experience. CONCLUSIONS.— A significant majority of neuropathologic diagnoses require ancillary testing, including molecular analysis, for appropriate classification. Therefore, the primary goal for any neurosurgical intraoperative diagnosis is the identification of diagnostic tissue and the preservation of the appropriate tissue for molecular testing. The intraoperative pathologist should seek to place a tumor in the most accurate diagnostic category possible, but specific diagnosis at the time of an intraoperative diagnosis is often not possible. Many entities have seen adjustments to grading criteria, including the incorporation of molecular features into grading. Awareness of these changes can help to avoid overgrading or undergrading at the time of intraoperative evaluation.
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Affiliation(s)
- Nicole Becker
- From the Department of Pathology, University of Iowa, Iowa City (Becker)
| | - Sandra Camelo-Piragua
- the Department of Pathology, University of Michigan, Ann Arbor (Camelo-Piragua, Conway)
| | - Kyle S Conway
- the Department of Pathology, University of Michigan, Ann Arbor (Camelo-Piragua, Conway)
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Kino S, Kanamori M, Shimoda Y, Niizuma K, Endo H, Matsuura Y. Distinguishing IDH mutation status in gliomas using FTIR-ATR spectra of peripheral blood plasma indicating clear traces of protein amyloid aggregation. BMC Cancer 2024; 24:222. [PMID: 38365669 PMCID: PMC10870484 DOI: 10.1186/s12885-024-11970-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 02/06/2024] [Indexed: 02/18/2024] Open
Abstract
BACKGROUND Glioma is a primary brain tumor and the assessment of its molecular profile in a minimally invasive manner is important in determining treatment strategies. Among the molecular abnormalities of gliomas, mutations in the isocitrate dehydrogenase (IDH) gene are strong predictors of treatment sensitivity and prognosis. In this study, we attempted to non-invasively diagnose glioma development and the presence of IDH mutations using multivariate analysis of the plasma mid-infrared absorption spectra for a comprehensive and sensitive view of changes in blood components associated with the disease and genetic mutations. These component changes are discussed in terms of absorption wavenumbers that contribute to differentiation. METHODS Plasma samples were collected at our institutes from 84 patients with glioma (13 oligodendrogliomas, 17 IDH-mutant astrocytoma, 7 IDH wild-type diffuse glioma, and 47 glioblastomas) before treatment initiation and 72 healthy participants. FTIR-ATR spectra were obtained for each plasma sample, and PLS discriminant analysis was performed using the absorbance of each wavenumber in the fingerprint region of biomolecules as the explanatory variable. This data was used to distinguish patients with glioma from healthy participants and diagnose the presence of IDH mutations. RESULTS The derived classification algorithm distinguished the patients with glioma from healthy participants with 83% accuracy (area under the curve (AUC) in receiver operating characteristic (ROC) = 0.908) and diagnosed the presence of IDH mutation with 75% accuracy (AUC = 0.752 in ROC) in cross-validation using 30% of the total test data. The characteristic changes in the absorption spectra suggest an increase in the ratio of β-sheet structures in the conformational composition of blood proteins of patients with glioma. Furthermore, these changes were more pronounced in patients with IDH-mutant gliomas. CONCLUSIONS The plasma infrared absorption spectra could be used to diagnose gliomas and the presence of IDH mutations in gliomas with a high degree of accuracy. The spectral shape of the protein absorption band showed that the ratio of β-sheet structures in blood proteins was significantly higher in patients with glioma than in healthy participants, and protein aggregation was a distinct feature in patients with glioma with IDH mutations.
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Affiliation(s)
- Saiko Kino
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan
| | - Masayuki Kanamori
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yoshiteru Shimoda
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Kuniyasu Niizuma
- Department of Neurosurgical Engineering and Translational Neuroscience, Graduate School of Biomedical Engineering, Tohoku University, Seiryo 2-1, Aoba, Sendai City, 980-8575, Miyagi Prefecture, Japan
- Department of Neurosurgical Engineering and Translational Neuroscience, Tohoku University Graduate School of Medicine, 980-8575 Seiryo 2-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Hidenori Endo
- Department of Neurosurgery, Tohoku University Graduate School of Medicine, 980-8574 Seiryo 1-1, Aoba, Sendai City, Miyagi Prefecture, Japan
| | - Yuji Matsuura
- Graduate School of Biomedical Engineering, Tohoku University, 6-6-05, Aza-Aoba, Aramaki, Aoba, Sendai City, 980-8579, Miyagi Prefecture, Japan.
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Wang Y, Yuan H, Zhao X, Zhang P, Wang G, Gao F. Compressive Raman imaging by combining scattering-projection interleaving with context-aware excitation. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2024; 16:583-588. [PMID: 38189485 DOI: 10.1039/d3ay02231e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Exciting an object with a laser-focus array and randomly interleaving its scattering projection has been proved to be an effective strategy for speeding up Raman imaging. The so-called scattering interleaved Raman imaging (SIRI) method allows Raman hyperspectral imaging with a single snapshot and exhibits excellent reconstruction fidelity and signal-to-noise ratios (SNRs). Here, we show that the performance of SIRI is significantly improved when combined with context-aware excitation. The experiments on micro-plastics demonstrate that the restriction of Raman excitation within a smaller region of interest as guided by bright-field microscopy improves the signal intensity and the SNR, and it is surprising that the spectral resolution is also significantly improved. The context-aware SIRI method is successfully used for imaging of lipid-producing yeast cells, suggesting that it is a promising analytical tool for studying live cells or tissues.
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Affiliation(s)
- Yakun Wang
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Hang Yuan
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Xuan Zhao
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Pengfei Zhang
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
| | - Guiwen Wang
- Guangxi Academy of Sciences, 98 Daling Road, Nanning, Guangxi 530007, China.
| | - Feng Gao
- School of Precision Instrument and Opto-Electronics Engineering, Tianjin University, Tianjin, 300072, China.
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Ranc V, Pavlacka O, Kalita O, Vaverka M. Discrimination of resected glioma tissues using surface enhanced Raman spectroscopy and Au@ZrO 2 plasmonic nanosensor. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 305:123521. [PMID: 37862838 DOI: 10.1016/j.saa.2023.123521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 10/22/2023]
Abstract
Gliomas present one of the most prevalent malignant tumors related to the central nervous system. Surgical extraction is still a preferred route for glioma treatment. Nonetheless, neurosurgeons still have a considerable challenge to detect actual margins of the targeted glioma intraoperatively and correctly because of its great natural infiltration. Here we evaluated the possibility of using surface-enhanced Raman spectroscopy to analyze freshly resected brain tissues. The developed method is based on the application of Au@ZrO2 nanosensor. The plasmonic properties of the sensor were first tested on the analysis of Rhodamine 6G, where concentrations down to 10-7 mol/L can be successfully detected. We also compared the performance of the nanosensor with silver plasmonic nanoparticles, where similar results were obtained regarding the reduction of the fluorescence background and enhancement of the intensity of the measured analytical signal. However, application of silver nanospheres led to increased variations in spectral data due to its probable aggregation. Applied ZrO2@Au nanosensor thus dramatically lowers the fluorescence present in the Raman data, and considerably improves the quality of the measured signal. The developed method allows for rapid discrimination between the glioma's periphery and central parts, which could serve as a steppingstone toward highly precise neurosurgery.
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Affiliation(s)
- Vaclav Ranc
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, Palacký University and Faculty Hospital Olomouc, Hněvotínská 5, 775 15, Olomouc, Czech Republic.
| | - Ondrej Pavlacka
- Department of Mathematical Analysis and Applications of Mathematics, Faculty of Science, Palacký, University Olomouc, 17. Listopadu 12, Olomouc, Czech Republic
| | - Ondrej Kalita
- Department of Neurosurgery, Faculty Hospital Olomouc, I.P. Pavlova 6, 775 20, Olomouc, Czechia; Department of Health Care Science, Faculty of Humanities, T. Bata University in Zlín, Štefanikova 5670, 760 01 Zlín, Czechia
| | - Miroslav Vaverka
- Department of Neurosurgery, Faculty Hospital Olomouc, I.P. Pavlova 6, 775 20, Olomouc, Czechia
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Wurm LM, Fischer B, Neuschmelting V, Reinecke D, Fischer I, Croner RS, Goldbrunner R, Hacker MC, Dybaś J, Kahlert UD. Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning. Analyst 2023; 148:6109-6119. [PMID: 37927114 DOI: 10.1039/d3an01303k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2023]
Abstract
Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.
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Affiliation(s)
- Lennard M Wurm
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Björn Fischer
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
- FISCHER GmbH, Raman Spectroscopic Services, 40667 Meerbusch, Germany
| | | | - David Reinecke
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Igor Fischer
- Department of Neurosurgery, University Hospital Düsseldorf and Medical Faculty Heinrich-Heine University, Düsseldorf, Germany
| | - Roland S Croner
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
| | - Roland Goldbrunner
- Department of Neurosurgery, University Hospital Cologne, Cologne, Germany
| | - Michael C Hacker
- Institute of Pharmaceutics and Biopharmaceutics, University of Düsseldorf, Düsseldorf, Germany
| | - Jakub Dybaś
- Jagiellonian Center for Experimental Therapeutics, Jagiellonian University, Krakow, Poland
| | - Ulf D Kahlert
- Clinic of General- Visceral-, Vascular and Transplantation Surgery, Department of Molecular and Experimental Surgery, University Hospital Magdeburg and Medical Faculty Otto-von-Guericke University, Magdeburg, Germany.
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7
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Hou Z, Hu J, Liu X, Yan Z, Zhang K, Fang S, Jiang T, Wang Y. Decision system for extent of resection in WHO grade 3 gliomas: a Chinese Glioma Genome Atlas database analysis. J Neurooncol 2023; 164:461-471. [PMID: 37668945 DOI: 10.1007/s11060-023-04420-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/09/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Extensive surgical resection has been found to be associated with longer survival in patients with gliomas, but the interactive prognostic value of molecular pathology of the surgical resection is unclear. This study evaluated the impact of molecular pathology and clinical characteristics on the surgical benefit in WHO grade 3 IDH-mutant gliomas. METHODS Clinical and pathological information of 246 patients with WHO grade 3 IDH-mutant gliomas were collected from the Chinese Glioma Genome Atlas database (2006-2020). The role of the extent of resection on overall survival, stratified by molecular pathology and clinical characteristics, was investigated. We then assessed prognostic factors using a univariate log-rank test and multivariate Cox proportional hazards model in the subgroups. RESULTS The extent of resection was an independent prognostic factor in the entire cohort, even when adjusted for molecular pathology. Gross total resection was found to be associated with longer survival in all patients and in the astrocytoma group but not in the oligodendroglioma group. Compared with subtotal resections, gross total resections resulted in a longer survival time for astrocytoma patients aged ≤ 45 years. However, there was no survival benefit from total resection in patients with astrocytoma aged > 45 years. CONCLUSIONS Extensive resection benefits only a proportion of patients with WHO grade 3 IDH-mutant gliomas. Younger patients with astrocytomas had survival benefits from extensive resection. In addition to clinical characteristics (especially age), molecular pathology impacted prognosis in patients with gliomas. Our findings provide guiding information to neurosurgeons while planning surgeries.
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Affiliation(s)
- Ziming Hou
- Department of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China
| | - Jie Hu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Zeya Yan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Kenan Zhang
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Shengyu Fang
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
| | - Tao Jiang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, #119 Area A, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
- Beijing Neurosurgical Institute, Capital Medical University, #119 Area B, Nansihuanxi Road, Fengtai District, Beijing, 100070, China.
- Chinese Institute for Brain Research, Beijing, China.
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Murugappan S, Tofail SAM, Thorat ND. Raman Spectroscopy: A Tool for Molecular Fingerprinting of Brain Cancer. ACS OMEGA 2023; 8:27845-27861. [PMID: 37576695 PMCID: PMC10413827 DOI: 10.1021/acsomega.3c01848] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 07/12/2023] [Indexed: 08/15/2023]
Abstract
Brain cancer is one of those few cancers with very high mortality and low five-year survival rate. First and foremost reason for the woes is the difficulty in diagnosing and monitoring the progression of brain tumors both benign and malignant, noninvasively and in real time. This raises a need in this hour for a tool to diagnose the tumors in the earliest possible time frame. On the other hand, Raman spectroscopy which is well-known for its ability to precisely represent the molecular markers available in any sample given, including biological ones, with great sensitivity and specificity. This has led to a number of studies where Raman spectroscopy has been used in brain tumors in various ways. This review article highlights the fundamentals of Raman spectroscopy and its types including conventional Raman, SERS, SORS, SRS, CARS, etc. are used in brain tumors for diagnostics, monitoring, and even theragnostics, collating all the major works in the area. Also, the review explores how Raman spectroscopy can be even more effectively used in theragnostics and the clinical level which would make them a one-stop solution for all brain cancer needs in the future.
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Affiliation(s)
- Sivasubramanian Murugappan
- Department of Physics, Bernal
Institute and Limerick Digital Cancer Research Centre (LDCRC)
University of Limerick, Castletroy, Limerick V94T9PX, Ireland
| | - Syed A. M. Tofail
- Department of Physics, Bernal
Institute and Limerick Digital Cancer Research Centre (LDCRC)
University of Limerick, Castletroy, Limerick V94T9PX, Ireland
| | - Nanasaheb D. Thorat
- Department of Physics, Bernal
Institute and Limerick Digital Cancer Research Centre (LDCRC)
University of Limerick, Castletroy, Limerick V94T9PX, Ireland
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Klint E, Richter J, Wårdell K. Combined Use of Frameless Neuronavigation and In Situ Optical Guidance in Brain Tumor Needle Biopsies. Brain Sci 2023; 13:brainsci13050809. [PMID: 37239281 DOI: 10.3390/brainsci13050809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 05/10/2023] [Accepted: 05/13/2023] [Indexed: 05/28/2023] Open
Abstract
Brain tumor needle biopsies are performed to retrieve tissue samples for neuropathological analysis. Although preoperative images guide the procedure, there are risks of hemorrhage and sampling of non-tumor tissue. This study aimed to develop and evaluate a method for frameless one-insertion needle biopsies with in situ optical guidance and present a processing pipeline for combined postoperative analysis of optical, MRI, and neuropathological data. An optical system for quantified feedback on tissue microcirculation, gray-whiteness, and the presence of a tumor (protoporphyrin IX (PpIX) accumulation) with a one-insertion optical probe was integrated into a needle biopsy kit that was used for frameless neuronavigation. In Python, a pipeline for signal processing, image registration, and coordinate transformation was set up. The Euclidian distances between the pre- and postoperative coordinates were calculated. The proposed workflow was evaluated on static references, a phantom, and three patients with suspected high-grade gliomas. In total, six biopsy samples that overlapped with the region of the highest PpIX peak without increased microcirculation were taken. The samples were confirmed as being tumorous and postoperative imaging was used to define the biopsy locations. A 2.5 ± 1.2 mm difference between the pre- and postoperative coordinates was found. Optical guidance in frameless brain tumor biopsies could offer benefits such as quantified in situ indication of high-grade tumor tissue and indications of increased blood flow along the needle trajectory before the tissue is removed. Additionally, postoperative visualization enables the combined analysis of MRI, optical, and neuropathological data.
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Affiliation(s)
- Elisabeth Klint
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
| | - Johan Richter
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
- Department of Neurosurgery, Linköping University Hospital, 581 85 Linköping, Sweden
| | - Karin Wårdell
- Department of Biomedical Engineering, Linköping University, 581 85 Linköping, Sweden
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Zhang Y, Yu H, Li Y, Xu H, Yang L, Shan P, Du Y, Yan X, Chen X. Raman spectroscopy: A prospective intraoperative visualization technique for gliomas. Front Oncol 2023; 12:1086643. [PMID: 36686726 PMCID: PMC9849680 DOI: 10.3389/fonc.2022.1086643] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 12/05/2022] [Indexed: 01/06/2023] Open
Abstract
The infiltrative growth and malignant biological behavior of glioma make it one of the most challenging malignant tumors in the brain, and how to maximize the extent of resection (EOR) while minimizing the impact on normal brain tissue is the pursuit of neurosurgeons. The current intraoperative visualization assistance techniques applied in clinical practice suffer from low specificity, slow detection speed and low accuracy, while Raman spectroscopy (RS) is a novel spectroscopy technique gradually developed and applied to clinical practice in recent years, which has the advantages of being non-destructive, rapid and accurate at the same time, allowing excellent intraoperative identification of gliomas. In the present work, the latest research on Raman spectroscopy in glioma is summarized to explore the prospect of Raman spectroscopy in glioma surgery.
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Ranasinghe JC, Wang Z, Huang S. Raman Spectroscopy on Brain Disorders: Transition from Fundamental Research to Clinical Applications. BIOSENSORS 2022; 13:27. [PMID: 36671862 PMCID: PMC9855372 DOI: 10.3390/bios13010027] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 06/17/2023]
Abstract
Brain disorders such as brain tumors and neurodegenerative diseases (NDs) are accompanied by chemical alterations in the tissues. Early diagnosis of these diseases will provide key benefits for patients and opportunities for preventive treatments. To detect these sophisticated diseases, various imaging modalities have been developed such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). However, they provide inadequate molecule-specific information. In comparison, Raman spectroscopy (RS) is an analytical tool that provides rich information about molecular fingerprints. It is also inexpensive and rapid compared to CT, MRI, and PET. While intrinsic RS suffers from low yield, in recent years, through the adoption of Raman enhancement technologies and advanced data analysis approaches, RS has undergone significant advancements in its ability to probe biological tissues, including the brain. This review discusses recent clinical and biomedical applications of RS and related techniques applicable to brain tumors and NDs.
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Affiliation(s)
| | | | - Shengxi Huang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
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12
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Wadden J, Newell BS, Bugbee J, John V, Bruzek AK, Dickson RP, Koschmann C, Blaauw D, Narayanasamy S, Das R. Ultra-rapid somatic variant detection via real-time targeted amplicon sequencing. Commun Biol 2022; 5:708. [PMID: 35840782 PMCID: PMC9284968 DOI: 10.1038/s42003-022-03657-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 06/29/2022] [Indexed: 12/03/2022] Open
Abstract
Molecular markers are essential for cancer diagnosis, clinical trial enrollment, and some surgical decision making, motivating ultra-rapid, intraoperative variant detection. Sequencing-based detection is considered the gold standard approach, but typically takes hours to perform due to time-consuming DNA extraction, targeted amplification, and library preparation times. In this work, we present a proof-of-principle approach for sub-1 hour targeted variant detection using real-time DNA sequencers. By modifying existing protocols, optimizing for diagnostic time-to-result, we demonstrate confirmation of a hot-spot mutation from tumor tissue in ~52 minutes. To further reduce time, we explore rapid, targeted Loop-mediated Isothermal Amplification (LAMP) and design a bioinformatics tool-LAMPrey-to process sequenced LAMP product. LAMPrey's concatemer aware alignment algorithm is designed to maximize recovery of diagnostically relevant information leading to a more rapid detection versus standard read alignment approaches. Using LAMPrey, we demonstrate confirmation of a hot-spot mutation (250x support) from tumor tissue in less than 30 minutes.
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Affiliation(s)
- Jack Wadden
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA.
| | - Brandon S Newell
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Joshua Bugbee
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Vishal John
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Amy K Bruzek
- Department of Neurosurgery, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Robert P Dickson
- Division of Pulmonary and Critical Care, Department of Internal Medicine, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - Carl Koschmann
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan School of Medicine, Ann Arbor, MI, 48109, USA
| | - David Blaauw
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Satish Narayanasamy
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Reetuparna Das
- Division of Computer Science and Engineering, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, 48109, USA.
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13
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Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies. Cancers (Basel) 2021; 13:cancers13164196. [PMID: 34439355 PMCID: PMC8392399 DOI: 10.3390/cancers13164196] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/12/2021] [Accepted: 08/16/2021] [Indexed: 11/26/2022] Open
Abstract
Simple Summary Isocitrate dehydrogenase (IDH) mutation is one of the most important prognostic markers in glioma tumors. Raman spectroscopy (RS) is an optical technique with great potential in intraoperative molecular diagnosis and surgical guidance. We analyzed RS’s ability to detect the IDH mutation onto unprocessed glioma biopsies. A total of 2073 Raman spectra were extracted from 38 tumor specimens. From the 103 Raman shifts screened, we identified 52 shifts (related to lipids, collagen, DNA and cholesterol/phospholipids) with the highest performance in the distinction of the two groups. We described 18 shifts never used before for IDH detection with RS in fresh or frozen samples. We were able to distinguish between IDH-mutated and IDH-wild-type tumors with an accuracy and precision of 87%. RS showed optimal accuracy and precision in discriminating IDH-mutated glioma from IDH-wild-type tumors ex-vivo onto fresh surgical specimens. Abstract Isocitrate dehydrogenase (IDH) mutational status is pivotal in the management of gliomas. Patients with IDH-mutated (IDH-MUT) tumors have a better prognosis and benefit more from extended surgical resection than IDH wild-type (IDH-WT). Raman spectroscopy (RS) is a minimally invasive optical technique with great potential for intraoperative diagnosis. We evaluated the RS’s ability to characterize the IDH mutational status onto unprocessed glioma biopsies. We extracted 2073 Raman spectra from thirty-eight unprocessed samples. The classification performance was assessed using the eXtreme Gradient Boosted trees (XGB) and Support Vector Machine with Radial Basis Function kernel (RBF-SVM). Measured Raman spectra displayed differences between IDH-MUT and IDH-WT tumor tissue. From the 103 Raman shifts screened as input features, the cross-validation loop identified 52 shifts with the highest performance in the distinction of the two groups. Raman analysis showed differences in spectral features of lipids, collagen, DNA and cholesterol/phospholipids. We were able to distinguish between IDH-MUT and IDH-WT tumors with an accuracy and precision of 87%. RS is a valuable and accurate tool for characterizing the mutational status of IDH mutation in unprocessed glioma samples. This study improves RS knowledge for future personalized surgical strategy or in situ target therapies for glioma tumors.
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Raman microspectroscopy and Raman imaging reveal biomarkers specific for thoracic aortic aneurysms. CELL REPORTS MEDICINE 2021; 2:100261. [PMID: 34095874 PMCID: PMC8149374 DOI: 10.1016/j.xcrm.2021.100261] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 01/29/2021] [Accepted: 04/06/2021] [Indexed: 01/30/2023]
Abstract
Aortic rupture and dissection are life-threatening complications of ascending thoracic aortic aneurysms (aTAAs), and risk assessment has been largely based on the monitoring of lumen size enlargement. Temporal changes in the extracellular matrix (ECM), which has a critical impact on aortic remodeling, are not routinely evaluated, and cardiovascular biomarkers do not exist to predict aTAA formation. Here, Raman microspectroscopy and Raman imaging are used to identify spectral biomarkers specific for aTAAs in mice and humans by multivariate data analysis (MVA). Multivariate curve resolution-alternating least-squares (MCR-ALS) combined with Lasso regression reveals elastic fiber-derived (Ce1) and collagen fiber-derived (Cc6) components that are significantly increased in aTAA lesions of murine and human aortic tissues. In particular, Cc6 detects changes in amino acid residues, including phenylalanine, tyrosine, tryptophan, cysteine, aspartate, and glutamate. Ce1 and Cc6 may serve as diagnostic Raman biomarkers that detect alterations of amino acids derived from aneurysm lesions. Label-free Raman imaging of human/murine ascending thoracic aortic aneurysm (aTAA) Multivariate analysis of Raman spectra allows detection of aTAA molecular features Identification of spectral biomarkers for aTAA in elastic and collagen fibers Alterations in amino acid spectra correlate with aTAA formation
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15
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Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples. Cancers (Basel) 2021; 13:cancers13051073. [PMID: 33802369 PMCID: PMC7959285 DOI: 10.3390/cancers13051073] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 02/17/2021] [Accepted: 02/25/2021] [Indexed: 12/14/2022] Open
Abstract
Identifying tumor cells infiltrating normal-appearing brain tissue is critical to achieve a total glioma resection. Raman spectroscopy (RS) is an optical technique with potential for real-time glioma detection. Most RS reports are based on formalin-fixed or frozen samples, with only a few studies deployed on fresh untreated tissue. We aimed to probe RS on untreated brain biopsies exploring novel Raman bands useful in distinguishing glioma and normal brain tissue. Sixty-three fresh tissue biopsies were analyzed within few minutes after resection. A total of 3450 spectra were collected, with 1377 labelled as Healthy and 2073 as Tumor. Machine learning methods were used to classify spectra compared to the histo-pathological standard. The algorithms extracted information from 60 different Raman peaks identified as the most representative among 135 peaks screened. We were able to distinguish between tumor and healthy brain tissue with accuracy and precision of 83% and 82%, respectively. We identified 19 new Raman shifts with known biological significance. Raman spectroscopy was effective and accurate in discriminating glioma tissue from healthy brain ex-vivo in fresh samples. This study added new spectroscopic data that can contribute to further develop Raman Spectroscopy as an intraoperative tool for in-vivo glioma detection.
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16
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Hollon T, Orringer DA. Label-free brain tumor imaging using Raman-based methods. J Neurooncol 2021; 151:393-402. [PMID: 33611706 DOI: 10.1007/s11060-019-03380-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/20/2019] [Indexed: 11/24/2022]
Abstract
INTRODUCTION Label-free Raman-based imaging techniques create the possibility of bringing chemical and histologic data into the operation room. Relying on the intrinsic biochemical properties of tissues to generate image contrast and optical tissue sectioning, Raman-based imaging methods can be used to detect microscopic tumor infiltration and diagnose brain tumor subtypes. METHODS Here, we review the application of three Raman-based imaging methods to neurosurgical oncology: Raman spectroscopy, coherent anti-Stokes Raman scattering (CARS) microscopy, and stimulated Raman histology (SRH). RESULTS Raman spectroscopy allows for chemical characterization of tissue and can differentiate normal and tumor-infiltrated tissue based on variations in macromolecule content, both ex vivo and in vivo. To improve signal-to-noise ratio compared to conventional Raman spectroscopy, a second pulsed excitation laser can be used to coherently drive the vibrational frequency of specific Raman active chemical bonds (i.e. symmetric stretching of -CH2 bonds). Coherent Raman imaging, including CARS and stimulated Raman scattering microscopy, has been shown to detect microscopic brain tumor infiltration in fresh brain tumor specimens with submicron image resolution. Advances in fiber-laser technology have allowed for the development of intraoperative SRH as well as artificial intelligence algorithms to facilitate interpretation of SRH images. With molecular diagnostics becoming an essential part of brain tumor classification, preliminary studies have demonstrated that Raman-based methods can be used to diagnose glioma molecular classes intraoperatively. CONCLUSIONS These results demonstrate how label-free Raman-based imaging methods can be used to improve the management of brain tumor patients by detecting tumor infiltration, guiding tumor biopsy/resection, and providing images for histopathologic and molecular diagnosis.
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17
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Klamminger GG, Gérardy JJ, Jelke F, Mirizzi G, Slimani R, Klein K, Husch A, Hertel F, Mittelbronn M, Kleine-Borgmann FB. Application of Raman spectroscopy for detection of histologically distinct areas in formalin-fixed paraffin-embedded glioblastoma. Neurooncol Adv 2021; 3:vdab077. [PMID: 34355170 PMCID: PMC8331050 DOI: 10.1093/noajnl/vdab077] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" that could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. METHODS To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up an SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. RESULTS Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on the spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal-appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal-appearing brain tissue can be detected. CONCLUSION These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As conclusion, we propose that RS may serve useful as a future method in the pathological toolbox.
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Affiliation(s)
| | - Jean-Jacques Gérardy
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
| | - Finn Jelke
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Giulia Mirizzi
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Rédouane Slimani
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Doctoral School in Science and Engineering (DSSE), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Karoline Klein
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
| | - Andreas Husch
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- National Center of Neurosurgery, Centre Hospitalier de Luxembourg (CHL), Luxembourg, Luxembourg
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Michel Mittelbronn
- National Center of Pathology (NCP), Laboratoire national de santé (LNS), Dudelange, Luxembourg
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
- Luxembourg Centre of Systems Biomedicine (LCSB), University of Luxembourg (UL), Esch-sur-Alzette, Luxembourg
| | - Felix B Kleine-Borgmann
- Saarland University Medical Center and Faculty of Medicine, Homburg, Germany
- Luxembourg Center of Neuropathology (LCNP), Dudelange, Luxembourg
- Department of Oncology (DONC), Luxembourg Institute of Health (LIH), Luxembourg, Luxembourg
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18
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Power LJ, Fasolato C, Barbero A, Wendt DJ, Wixmerten A, Martin I, Asnaghi MA. Sensing tissue engineered cartilage quality with Raman spectroscopy and statistical learning for the development of advanced characterization assays. Biosens Bioelectron 2020; 166:112467. [DOI: 10.1016/j.bios.2020.112467] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 06/08/2020] [Accepted: 07/20/2020] [Indexed: 01/30/2023]
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19
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Nassiri F, Aldape K, Alhuwalia M, Brastianos P, Ducray F, Galldiks N, Kim A, Lamszus K, Mitchell D, Nabors LB, Nam DH, Natsume A, Ng HK, Niclou S, Sahm F, Short S, Walsh K, Wick W, Zadeh G. Highlights of the inaugural ten - the launch of Neuro-Oncology Advances. Neurooncol Adv 2019; 1:vdz016. [PMID: 32642652 DOI: 10.1093/noajnl/vdz016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Affiliation(s)
- Farshad Nassiri
- Division of Neurosurgery, University Health Network and MacFeeters-Hamilton Neuro-Oncology Program, Princess Margaret Hospital, University of Toronto, Toronto, Canada
| | - Kenneth Aldape
- Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Manmeet Alhuwalia
- Rose Ella Burkhardt Brain Tumor and Neuro-Oncology Center and Department of Hematology/Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, Ohio
| | - Priscilla Brastianos
- Divisions of Hematology/Oncology and Neuro-Oncology, Departments of Medicine and Neurology, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts
| | - Francois Ducray
- Department of Neuro-Oncology, Hospices Civils de Lyon, Lyon, France
| | - Norbert Galldiks
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne and Germany Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich and Germany Center of Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Germany
| | - Albert Kim
- Department of Neurosurgery, School of Medicine, Washington University, St. Louis, Missouri
| | - Katrin Lamszus
- Department of Neurosurgery, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Duane Mitchell
- Lillian S. Wells Department of Neurosurgery, UF Brain Tumor Immunotherapy Program, Preston A. Wells, Jr. Center for Brain Tumor Therapy, University of Florida, Gainesville, Florida
| | - L Burt Nabors
- Department of Neurology, University of Alabama, Birmingham, Birmingham, Alabama
| | - Do-Hyun Nam
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea
| | - Atsushi Natsume
- Department of Neurosurgery, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Japan
| | - Ho-Keung Ng
- Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Hong Kong, China
| | - Simone Niclou
- Department of Neuropathology, Institute of Pathology, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Felix Sahm
- NorLux Neuro-Oncology Laboratory, Department of Oncology, Luxembourg Institute of Health, Luxembourg, Luxembourg
| | - Susan Short
- Leeds Institute of Cancer and Pathology, Faculty of Medicine and Health, University of Leeds, St James's University Hospital, Leeds, West Yorkshire
| | - Kyle Walsh
- Duke Cancer Institute, Duke University, Durham, North Carolina
| | - Wolfgang Wick
- University Medical Center and German Cancer Research Center Heidelberg, Germany
| | - Gelareh Zadeh
- Division of Neurosurgery, University Health Network and MacFeeters-Hamilton Neuro-Oncology Program, Princess Margaret Hospital, University of Toronto, Toronto, Canada
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