Добірка наукової літератури з теми "Gliobastoma (GBM)"
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Статті в журналах з теми "Gliobastoma (GBM)"
Nascimento, Sâmia Israele Braz do, Sheezara Teles Lira dos Santos, Bárbara Torquato Alves, Kevellyn Cruz Aguilera, Airton César Pinheiro de Menezes, Ana Maria Correia Alencar, Ana Maria Lima Carneiro de Andrade, Daniel Gonçalves Leite, and Antonio Marlos Duarte de Melo. "POTENTIAL THERAPEUTIC EFFECT OF ZIKA VIRUS IN GLIOBLASTOMA TREATMENT." Amadeus International Multidisciplinary Journal 2, no. 4 (July 6, 2018): 106–11. http://dx.doi.org/10.14295/aimj.v2i4.36.
Повний текст джерелаRyskalin, Larisa, Anderson Gaglione, Fiona Limanaqi, Francesca Biagioni, Pietro Familiari, Alessandro Frati, Vincenzo Esposito, and Francesco Fornai. "The Autophagy Status of Cancer Stem Cells in Gliobastoma Multiforme: From Cancer Promotion to Therapeutic Strategies." International Journal of Molecular Sciences 20, no. 15 (August 5, 2019): 3824. http://dx.doi.org/10.3390/ijms20153824.
Повний текст джерелаVijayan, Poornima, Zuhara Shemin, Sharmina Saleem, and Anupama Ponniah. "Pediatric giant cell glioblastoma: a rare entity." International Journal of Contemporary Pediatrics 4, no. 3 (April 25, 2017): 1098. http://dx.doi.org/10.18203/2349-3291.ijcp20171735.
Повний текст джерелаJ Umlauf, Benjamin, Paul A Clark, Jason M Lajoie, Julia V Georgieva, Samantha Bremner, Brantley R Herrin, Eric V Shusta, and John S Kuo. "SCIDOT-05. DEVELOPING VARIABLE LYMPHOCYTE RECEPTORS THAT TARGET PATHOLOGICALLY EXPOSED NEURAL ECM TO TREAT GLIOBLASTOMA." Neuro-Oncology 21, Supplement_6 (November 2019): vi273. http://dx.doi.org/10.1093/neuonc/noz175.1146.
Повний текст джерелаBruno, Francesco, Alessia Pellerino, Edoardo Pronello, Rosa Palmiero, Luca Bertero, Cristina Mantovani, Andrea Bianconi, Antonio Melcarne, Diego Garbossa, and Roberta Rudà. "Elderly Gliobastoma Patients: The Impact of Surgery and Adjuvant Treatments on Survival. A Single Institution Experience." Brain Sciences 12, no. 5 (May 11, 2022): 632. http://dx.doi.org/10.3390/brainsci12050632.
Повний текст джерелаMuniraj, Nethaji, Kajal Chaudhry, Joshua Terao, Luke Barron, Vipin Suri, Catherine Bollard та Conrad Russell Cruz. "IMMU-22. ALLOGENEIC NK CELLS SECRETING IL15 AND RESISTANT TO TGF-β Show Antitumor Activity Against Gliobastoma". Neuro-Oncology 25, Supplement_1 (1 червня 2023): i54. http://dx.doi.org/10.1093/neuonc/noad073.209.
Повний текст джерелаHurwitz, V., J. La, J. Lavrador, L. Brazil, K. Chia, A. Swampillai, O. Al-Salihi, et al. "P16.03.A Epithelioid gliobastoma requires rapid treatment and BRAF inhibitors should be made readily available for their treatment." Neuro-Oncology 24, Supplement_2 (September 1, 2022): ii88. http://dx.doi.org/10.1093/neuonc/noac174.307.
Повний текст джерелаGallego, O., A. Estival, M. Martinez-Garcia, E. Pineda, M. Gil, S. Del Barco, J. Marruecos, et al. "Characteristics of gliobastomas (GBM) not resected (only biopsied) homogeneosuly treated with Stupp regimen. Results from the GLIOCAT study." Annals of Oncology 27 (October 2016): vi111. http://dx.doi.org/10.1093/annonc/mdw367.26.
Повний текст джерелаДисертації з теми "Gliobastoma (GBM)"
Dillenburg, Fabiane Cristine. "An approach for analyzing and classifying microarray data using gene co-expression networks cycles." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/171353.
Повний текст джерелаOne of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.
Desoubzdanne-Dumont, Denis. "Radiorésistance de lignées cellulaires humaines de gliobastomes : recherche de bloqueurs par métabolomique, lipidomique et transcriptomiques." Toulouse 3, 2010. http://thesesups.ups-tlse.fr/877/.
Повний текст джерелаGlioblastomas (GBM) are the most aggressive human brain tumors. Indeed, patients most often die within the year after the diagnostic. Radiotherapy generally associated to radiosensitizers is currently systematically used to reduce tumor progression. Nevertheless, a radioresistance phenomenon still occurs. An individual treatment is hoped for each patient. For this purpose, a molecular classification of GBM has been created, taking into account biomarkers such as a predictive chimioresistance factor, but not radioresistance one. In this context, we have searched for in vitro radioresistance biomarkers in four human GBM cell lines with different radiosensitivity profiles. This corresponds to the first part of the PhD manuscript. Comprehensive and robust analytical methods such as 1H NMR metabolomics, lipidomics and transcriptomics have been used. An accumulation of choline compounds has been observed in the two more radioresistant cell lines. An analytical method using deuterated labelling and HILIC-ESI-MS/MS has been developed to study the metabolism of phosphatidylcholines in the four cell lines. In the second part of the PhD project, we have focused on potential in vitro biomarkers of radio-induced cell death in radiosensitized human GBM cell lines. For this, NMR 1H metabolomics has been chosen. Taurine has been found as a good candidate in a cell line. Lipidomics and FACS analyses have then been used to confirm this result