Relevant bibliographies by topics / Brain tumour (Brat)

Academic literature on the topic 'Brain tumour (Brat)'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Brain tumour (Brat)"

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Macošek, Jakub, Bernd Simon, Johanna-Barbara Linse, Pravin Kumar Ankush Jagtap, Sophie L. Winter, Jaelle Foot, Karine Lapouge, et al. "Structure and dynamics of the quaternary hunchback mRNA translation repression complex." Nucleic Acids Research 49, no. 15 (July 30, 2021): 8866–85. http://dx.doi.org/10.1093/nar/gkab635.

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Abstract A key regulatory process during Drosophila development is the localized suppression of the hunchback mRNA translation at the posterior, which gives rise to a hunchback gradient governing the formation of the anterior-posterior body axis. This suppression is achieved by a concerted action of Brain Tumour (Brat), Pumilio (Pum) and Nanos. Each protein is necessary for proper Drosophila development. The RNA contacts have been elucidated for the proteins individually in several atomic-resolution structures. However, the interplay of all three proteins during RNA suppression remains a long-standing open question. Here, we characterize the quaternary complex of the RNA-binding domains of Brat, Pum and Nanos with hunchback mRNA by combining NMR spectroscopy, SANS/SAXS, XL/MS with MD simulations and ITC assays. The quaternary hunchback mRNA suppression complex comprising the RNA binding domains is flexible with unoccupied nucleotides functioning as a flexible linker between the Brat and Pum-Nanos moieties of the complex. Moreover, the presence of the Pum-HD/Nanos-ZnF complex has no effect on the equilibrium RNA binding affinity of the Brat RNA binding domain. This is in accordance with previous studies, which showed that Brat can suppress mRNA independently and is distributed uniformly throughout the embryo.
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S, Manimurugan. "Classification of Alzheimer's disease from MRI Images using CNN based Pre-trained VGG-19 Model." Journal of Computational Science and Intelligent Technologies 1, no. 2 (2020): 34–41. http://dx.doi.org/10.53409/mnaa.jcsit20201205.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vectormachine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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T, Anitha, Charlyn Pushpa Latha G, and Surendra Prasad M. "A Proficient Adaptive K-means based Brain Tumor Segmentation and Detection Using Deep Learning Scheme with PSO." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 9–14. http://dx.doi.org/10.53409/mnaa.jcsit20201302.

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Determining the size of the tumor is a significant obstacle in brain tumour preparation and objective assessment. Magnetic Resonance Imaging (MRI) is one of the non-invasive methods that has emanated without ionizing radiation as a front-line diagnostic method for brain tumour. Several approaches have been applied in modern years to segment MRI brain tumours automatically. These methods can be divided into two groups based on conventional learning, such as support vector machine (SVM) and random forest, respectively hand-crafted features and classifier method. However, after deciding hand-crafted features, it uses manually separated features and is given to classifiers as input. These are the time consuming activity, and their output is heavily dependent upon the experience of the operator. This research proposes fully automated detection of brain tumor using Convolutional Neural Network (CNN) to avoid this problem. It also uses brain image of high grade gilomas from the BRATS 2015 database. The suggested research performs brain tumor segmentation using clustering of k-means and patient survival rates are increased with this proposed early diagnosis of brain tumour using CNN.
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Almajmaie, Layth Kamil Adday, Ahmed Raad Raheem, Wisam Ali Mahmood, and Saad Albawi. "MRI image segmentation using machine learning networks and level set approaches." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (February 1, 2022): 793. http://dx.doi.org/10.11591/ijece.v12i1.pp793-801.

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<span>The segmented brain tissues from magnetic resonance images (MRI) always pose substantive challenges to the clinical researcher community, especially while making precise estimation of such tissues. In the recent years, advancements in deep learning techniques, more specifically in fully convolution neural networks (FCN) have yielded path breaking results in segmenting brain tumour tissues with pin-point accuracy and precision, much to the relief of clinical physicians and researchers alike. A new hybrid deep learning architecture combining SegNet and U-Net techniques to segment brain tissue is proposed here. Here, a skip connection of the concerned U-Net network was suitably explored. The results indicated optimal multi-scale information generated from the SegNet, which was further exploited to obtain precise tissue boundaries from the brain images. Further, in order to ensure that the segmentation method performed better in conjunction with precisely delineated contours, the output is incorporated as the level set layer in the deep learning network. The proposed method primarily focused on analysing brain tumor segmentation (BraTS) 2017 and BraTS 2018, dedicated datasets dealing with MRI brain tumour. The results clearly indicate better performance in segmenting brain tumours than existing ones.</span>
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Ravikumar, M., and B. J. Shivaprasad. "Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images." Tehnički glasnik 15, no. 1 (March 4, 2021): 37–42. http://dx.doi.org/10.31803/tg-20210204162414.

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In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor &amp; non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 &amp; F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 &amp; F1-Score: 0.88.
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Lokody, Isabel. "BRAF mutation drives rare brain tumour." Nature Reviews Cancer 14, no. 3 (February 24, 2014): 157. http://dx.doi.org/10.1038/nrc3693.

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Berghoff, Anna Sophie, and Matthias Preusser. "BRAF alterations in brain tumours." Current Opinion in Neurology 27, no. 6 (December 2014): 689–96. http://dx.doi.org/10.1097/wco.0000000000000146.

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Lima, Jorge, Jorge Pinheiro, Susana Nunes, Ana Paula Fernandes, Paula Soares, Jose Carlos Machado, Josue Pereira, and Maria Joao Gil da Costa. "TBIO-10. NGS molecular profile of paediatric brain tumours: results from 92 consecutive patients treated at Centro Hospitalar Universitário de São João." Neuro-Oncology 24, Supplement_1 (June 1, 2022): i185. http://dx.doi.org/10.1093/neuonc/noac079.692.

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Abstract AIM: Our aim was to progress in bringing molecular medicine to routine clinical practice in the setting of paediatric neuro-oncology. We have implemented a protocol between Ipatimup and Centro Hospitalar Universitário de São João for the rapid and efficient delivery of the molecular portrait of paediatric brain tumours. MATERIAL AND METHODS: We have enrolled 92 patients with the following inclusion criteria: Age 0-18 years; newly diagnosed brain tumour; previously diagnosed brain tumour, whenever it presented as rare, aggressive or refractory disease; availability of tumour material; signed informed consent. Tumour samples were centrally reviewed by expert pathologists and profiled using the Oncomine Childhood Cancer Research Assay. RESULTS: In the 92 tumours that were molecularly profiled, BRAF was the most frequently altered gene, especially in pilocytic astrocytomas, being also detected in other LGG and HGG. Other commonly mutated genes were PIK3CA and FGFR, the former in HGG and the latter in LGG. MYB and RAF1 rearrangements were also found in low grade glial/glioneuronal tumours, while HGG showed a more complex profile, with many cases harbouring multiple alterations in EGFR, PDGFRA, ATRX, H3F3A, HIST1H3B, TP53, among others. A 16-year old patient with CMMR (homozygous mutation in PMS2) developed a glioblastoma that carried nearly 5x the average number of mutations. Among the 8 medulloblastomas, 2 showed mutations in the SHH pathway (1 in PTCH1 and one in SUFU) and 2 in the WNT pathway (1 in CTNNB1 and one in APC). In the remaining cases, one ependymoma presented MYCN amplification, while no alterations were detected in 3 patients. CONCLUSIONS: This study enabled the detailed molecular study of 92 paediatric brain patients, allowing a more robust tumour classification and the identification of actionable alterations. A subset of the patients are already undergoing targeted therapy, mainly using BRAF or MEK inhibitors with generally good improvement.
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Frank, Deborah J., Bruce A. Edgar, and Mark B. Roth. "TheDrosophila melanogastergenebrain tumornegatively regulates cell growth and ribosomal RNA synthesis." Development 129, no. 2 (January 15, 2002): 399–407. http://dx.doi.org/10.1242/dev.129.2.399.

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The regulation of ribosome synthesis is likely to play an important role in the regulation of cell growth. Previously, we have shown that the ncl-1 gene in Caenorhabditis elegans functions as an inhibitor of cell growth and ribosome synthesis. We now indicate that the Drosophila melanogaster tumor suppressor brain tumor (brat) is an inhibitor of cell growth and is a functional homolog of the C. elegans gene ncl-1. The brat gene is able to rescue the large nucleolus phenotype of ncl-1 mutants. We also show that brat mutant cells are larger, have larger nucleoli, and have more ribosomal RNA than wild-type cells. Furthermore, brat overexpressing cells contain less ribosomal RNA than control cells. These results suggest that the tumorous phenotype of brat mutants may be due to excess cell growth and ribosome synthesis.
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Sakamoto, Tomohiro, Katsunori Arai, Karen Makishima, and Akira Yamasaki. "BRAF V600E-mutated combined large cell neuroendocrine carcinoma and adenocarcinoma responding to targeted therapy." BMJ Case Reports 14, no. 12 (December 2021): e243295. http://dx.doi.org/10.1136/bcr-2021-243295.

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We present a case of combined large cell neuroendocrine carcinoma (LCNEC), harbouring a BRAF V600E mutation, which significantly benefited from BRAF-targeted therapy. A 57-year-old woman was referred to our hospital for headache and vomiting. A head MRI showed a large tumour in her brain, and a whole-body CT revealed a tumour in the hilum of the right lung and mediastinal lymphadenopathies. Both the resected brain tumour and the mediastinal lymph node tissue contained LCNEC. Next-generation sequencing revealed a BRAF V600E mutation, and a combination therapy with dabrafenib and trametinib was initiated. The patient had a good response to treatment. Like non–small cell lung cancer patients, LCNEC patients should undergo multiplex somatic mutation testing.
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Dissertations / Theses on the topic "Brain tumour (Brat)"

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Mercurio, Sandy. "Mise en évidence de nouvelles cibles thérapeutiques dans les tumeurs gliales et glioneuronales de l'enfant." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM5094/document.

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Les tumeurs gliales et glioneuronales sont les tumeurs cérébrales les plus fréquentes chez l'enfant. Elles sont généralement d'excellent pronostic. En revanche, les astrocytomes pilocytiques (AP) hypothalamo-chiasmatiques, ont un potentiel évolutif plus agressif. Ce travail de thèse propose une nouvelle stratégie thérapeutique pour ce sous-type d'AP selon la méthode du « drug repositioning », en employant la combinaison du celecoxib et de la fluvastatine. Nos travaux ont montré in vitro que cette association de molécules était synergique, capable d'arrêter le cycle cellulaire, de diminuer la prolifération et d'induire l'apoptose des cellules tumorales. Cette combinaison a également été testée avec succès chez une patiente souffrant d'un AP multifocal et réfractaire aux traitements conventionnels dans le cadre d'une thérapie métronomique. Ce manuscrit décrit également l'étude histo-moléculaire de plusieurs séries de tumeurs gliales et glioneuronales pédiatriques menées afin d'améliorer leur caractérisation et leur diagnostic. Nos travaux ont confirmé la présence de la fusion KIAA1549:BRAF dans les AP analysés ainsi que le caractère péjoratif de la topographie hypothalamo-chiasmatique, du variant histologique pilomyxoïde et de l'âge au diagnostic inférieur à 36 mois. Ils ont également montré l'absence de différence moléculaire entre les gliomes corticaux de grade II et des DNT. Enfin, nos travaux ont montré que les DNT, les GG et les PXA partagent la mutation BRAFV600E et l'expression de CD34. Ces travaux confirment l'implication majeure de l'altération de la voie des MAPKinases dans la tumorigenèse de ces tumeurs, constituant ainsi une cible thérapeutique prometteuse
Glial and glioneuronal tumors are the most frequent brain tumors in children. They are characterized by an excellent prognosis. However, hypothalamic-chiasmatic pilocytic astrocytomas (PA) have a more aggressive outcome. In the first part, we propose a new therapeutic strategy for hypothalamic-chiasmatic PA according to drug repositioning method, by using celecoxib, and fluvastatin. We showed that, in vitro, this combination was synergistic, stopped cell cycle, inhibited cell proliferation and increased apoptosis. In addition, this combination was tested with success, under a metronomic chemotherapy, for a girl suffering from a multifocal PA and refractory to conventional treatment. This new strategy of treatment appears promising for this type of tumor because it is less toxic than conventional chemotherapy and not too expensive. In the second part, this manuscript describes the histo-molecular study of several retrospective series of glial and glioneuronal pediatric tumors conducted to improve their characterization and their diagnosis. We confirmed the presence of the fusion gene KIAA1549: BRAF in PA as well as the pejorative nature of the hypothalamic-chiasmatic topography, pilomyxoïde histology and the age at diagnosis less than 36 months. We also showed no molecular difference between cortical grade II gliomas associated with chronic epilepsy and the DNT group. Finally, we showed that DNT, GG and PXA share BRAFV600E mutation and expression of CD34. These studies confirm the major implication of the MAPKinase altered pathway in tumorigenesis of glial and glioneuronal pediatric tumors, constituting a promising therapeutic target
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Padovani, Laëtitia. "Caractérisation moléculaire des tumeurs cérébrales circonscrites de l'enfant." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM5018.

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La classification OMS des tumeurs cérébrales de l'enfant distingue les tumeurs gliales des tumeurs glioneuronales, les gliomes circonscrits des infiltrants. Elle représente le meilleur indicateur pronostic mais se heurte pourtant à des limites de reproductibilité. Pour mieux préciser le diagnostic, mieux définir des sous-groupes de pronostic différent, et mieux orienter le thérapeutique, nous avons recherché les profils moléculaires de 108 tumeurs cérébrales circonscrites de l'enfant : astrocytome pilocytique (PA), tumeurs neuroépithéliales dysembryoplasiques (DNT), xanthoastrocytomes pléïomorphes (PXA) et gangliogliomes (GG). Aucune différence n'est retrouvée entre les gliomes corticaux de grade II (GC) et les DNT concernant IDH1 et 2, TP53 et la délétion1p19q. Les DNT non spécifiques et les GC partagent le même profil incluant CD34 et la mutation V600E de BRAF dans 50% des cas. Le PXA exprime la mutation V600E de BRAF dans plus de 50 % des cas et se rapproche du groupe des tumeurs glioneuronales. Concernant le PA, nous confirmons le caractère péjoratif de la topographie hypothalamo-chiasmatique, de l'histologie pilomyxoide, de l'âge inférieur à 36 mois et de l'exérèse partielle. A l'opposé des tumeurs infiltrantes qui appartiendraient au groupe " histones dépendantes", les tumeurs circonscrites pourraient être regroupées sous le terme "MAPKinases dépendantes". On y distinguerait alors les tumeurs avec fusion KIAA1543-BRAF de celles avec mutation V600E de BRAF. Ce travail a permis de mieux caractériser les tumeurs gliales et glioneuronales de l'enfant, reposant sur le transfert en routine de marqueurs moléculaires simples
The OMS classification for pediatric brain tumors includes glial tumors and mixed glial and glioneuronal tumors, diffuse and no diffuse glioma. All strategic decision making are based on this current classification but it drives to some limits of diagnosis reproductibility.The goal of our study was to define molecular profils for low grade no diffuse pediatric brain tumors including pilocytic astrocytoma (PA), dysembryoplasic neuroepithelial tumor (DNT), pleiomorphic xanthoastrocytoma (PXA) and benign gangliogliome (GG), to improve the quality of diagnosis, define different subgroups with different prognosis and then to improve treatment strategy decision making.No molecular difference was found between cortical grade II glioma (GC) and DNT regarding IDH1 and 2 TP53 alterations and 1p19q deletion. Similarly 50 % of no specific form of DNT share the same molecular profil with GC with CD34 expression and V600E mutation of BRAF. PXA demonstrated BRAFV600E mutation in 60 % of cases. PXA could then be very close glioneuronal tumors. Finally in PA we confirmed the negative impact of hypothalochiasmatic location, pilomyxoid diagnosis and age lower than 36 months and partial resection. We could work on the elaboration of a new classification and define the group named “Histone dependant” for tumors with histone aberrations and the group named “MAPKinases dependant” for tumors with either KIAA 1543-BRAF fusion or V600E BRAF mutation.In conclusion, this work has led to improve the molecular profil characteristics of glioneuronal tumors of childhood with different easy diagnostic markers that can be used in routine practice, and could potentially replace DNA sequencing
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Sångberg, Dennis. "Automated Glioma Segmentation in MRI using Deep Convolutional Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-171046.

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Manual segmentation of brain tumours is a time consuming process, results often show high variability, and there is a call for automation in clinical practice. In this thesis the use of deep convolutional networks for automatic glioma segmentation in MRI is investigated. The implemented networks are evaluated on data used in the brain tumor segmentation challenge (BraTS). It is found that 3D convolutional networks generally outperform 2D convolutional networks, and that the best networks can produce segmentations that closely resemble human segmentations. Convolutional networks are also evaluated as feature extractors with linear SVM classifiers on top, and although the sensitivity is improved considerably, the segmentations are heavily oversegmented. The importance of the amount of data available is investigated as well by comparing results from networks trained on both 2013 and the greatly extended 2014 data set, but it is found that the method of producing ground-truth was also a contributing factor. The networks does not beat the previous high-scores on the BraTS data, but several simple improvement areas are identified to take the networks further.
Manuell segmentering av hjärntumörer är en tidskrävande process, segmenteringarna är ofta varierade mellan experter, och automatisk segmentering skulle vara användbart för kliniskt bruk. Den här rapporten undersöker användningen av deep convolutional networks (ConvNets) för automatisk segmentering av gliom i MR-bilder. De implementerade nätverken utvärderas med hjälp av data från brain tumor segmentation challenge (BraTS). Studien finner att 3D-nätverk har generellt bättre resultat än 2D-nätverk, och att de bästa nätverken har förmågan att ge segmenteringar som liknar mänskliga segmenteringar. ConvNets utvärderas också som feature extractors, med linjära SVM som klassificerare. Den här metoden ger segmenteringar med hög känslighet, men är också till hög grad översegmenterade. Vikten av att ha mer träningsdata undersöks också genom att träna på två olika stora dataset, men metoden för att få fram de riktiga segmenteringarna har troligen också stor påverkan på resultatet. Nätverken slår inte de tidigare rekorden på BraTS, men flera viktiga men enkla förbättringsområden är identifierade som potentiellt skulle förbättra resultaten.
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Hussain, Rashid. "Exploring metabolic interventions for CIN cancer therapy." Thesis, 2017. http://hdl.handle.net/2440/119191.

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Chromosomal instability (CIN) has been established as one of the hallmarks of cancer, which is prevalent in most of the solid and advanced tumours. CIN enhances genetic heterogeneity in cancer cells. This heterogeneity provides selective advantages to cancer cells against the drugs and the therapies, which are linked to poor prognosis and relapse of cancer. Altered metabolism is another hallmark of cancer, which is being targeted for cancer therapy. In this thesis, I have discussed the therapeutic effect of targeting metabolism in CIN cells and CIN tumours. Chapter 1 is my introduction in which I have reviewed cancer, its therapy, CIN, its types, mechanisms, causes, and therapeutic targeting of CIN. I also review cancer metabolism, its targeting for the treatment, and targeting metabolism in CIN cells. Chapter 2 is a published review article about Drosophila being a model for CIN. In this article I have discussed different CIN models and their limitations, then I described Drosophila as a model for CIN studies. I later discussed different Drosophila CIN model systems which have been studied to understand CIN and cancer. As Drosophila has been extensively studied for CIN and cancer therapy, our lab has focused on targeting CIN cells in Drosophila. In an earlier study (Shaukat et al, 2012) it was found metabolic candidates such as Pas kinase and phosphofructokinase could be crucial for CIN cell survival. Chapter 3 is a further screening of metabolic candidates. We found few potential targets from all the major metabolic pathways whose knock down can specifically kill CIN cells. It was found, mitochondrial activity and oxidative stress was high which induced DNA damage and apoptosis in CIN cells targeted by these metabolic alterations. In chapter 4, I discuss the application of the selected candidates on CIN tumours. We further explain how one of my metabolic candidates stopped the tumour growth. This chapter also discusses the mechanism of ROS (reactive oxygen species) production and implications of high NADH levels in CIN cells, which was deficient in our earlier studies. Chapter 5 is my discussion in which I have collectively discussed my results, the significant of my work, my current model, and future directions. In appendix 1 I have presented a published review article on the role of JNK in response to oxidative DNA damage. This chapter encompasses activation of JNK by ROS, outcomes of JNK in response to ROS. Appendix 2 has figures for SOX drug and ovary numbers of the hosts.
Thesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 2017
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"Molecular analysis of BRAF and microsatellite analysis of chromosome 14q in astrocytic tumors." 2004. http://library.cuhk.edu.hk/record=b5892088.

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Chan Ching Yin.
Thesis submitted in: October 2003.
Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.
Includes bibliographical references (leaves 197-221).
Abstracts in English and Chinese.
Acknowledgement --- p.i
Abstract --- p.iii
Abstract in Chinese --- p.vi
List of abbreviations --- p.ix
List of tables --- p.xv
List of figures --- p.xvi
Contents --- p.xviii
Chapter 1. --- Introduction --- p.1
Chapter 1.1. --- What are astrocytic tumors? --- p.1
Chapter 1.1.1. --- Histological characteristics and classification --- p.2
Chapter 1.1.2. --- Epidemiology --- p.2
Chapter 1.1.3. --- Treatment and patient survival --- p.4
Chapter 1.2. --- "Cytogenetics, molecular genetics and epigenetics of astrocytic tumors" --- p.6
Chapter 1.2.1. --- Cytogenetics --- p.6
Chapter 1.2.2. --- Genetic imbalances --- p.7
Chapter 1.2.3. --- Tumor suppressor genes --- p.13
Chapter 1.2.4. --- Oncogenes --- p.22
Chapter 1.2.5. --- Primary and secondary GBMs --- p.26
Chapter 1.3. --- Major pathways involved in astrocytic tumorigenesis --- p.30
Chapter 1.3.1. --- Cell cycle dysregulation and suppression of apoptosis --- p.30
Chapter 1.3.2. --- Promotion of proliferation and survival --- p.33
Chapter 1.4. --- BRAF mutation in human cancers --- p.38
Chapter 1.5. --- Other CNS tumors included in the current study --- p.52
Chapter 2. --- Aims of study --- p.61
Chapter 3. --- Materials and methods --- p.64
Chapter 3.1. --- Clinical materials --- p.64
Chapter 3.2. --- Cell lines --- p.75
Chapter 3.3. --- Cell culture --- p.77
Chapter 3.4. --- DNA extraction --- p.78
Chapter 3.4.1. --- Pre-treatment of samples --- p.78
Chapter 3.4.2. --- Cell lysis and protein removal --- p.80
Chapter 3.4.3. --- Precipitation of DNA --- p.81
Chapter 3.4.4. --- Determination of DNA concentration --- p.81
Chapter 3.5. --- Mutation analysis of BRAF by cycle sequencing --- p.83
Chapter 3.5.1. --- Amplification of BRAF exons --- p.83
Chapter 3.5.2. --- Cycle sequencing and automated gel electrophoresis --- p.84
Chapter 3.6. --- Immunohistochemistry of B-Raf and GFAP --- p.87
Chapter 3.6.1. --- Pre-treatment of samples --- p.87
Chapter 3.6.2. --- Detection of B-Raf and GFAP antigens by ABC method --- p.88
Chapter 3.6.3. --- Controls --- p.90
Chapter 3.7. --- Quantification of EGFR gene dosage by TaqMan based real-time PCR --- p.91
Chapter 3.7.1. --- Preparation of gene constructs --- p.92
Chapter 3.7.2. --- Primers and TaqMan probes --- p.93
Chapter 3.7.3. --- Experimental condition and PCR program --- p.95
Chapter 3.7.4. --- DNA standards --- p.95
Chapter 3.7.5. --- Controls --- p.96
Chapter 3.7.6. --- Experimental layout --- p.96
Chapter 3.8. --- Microsatellite analysis of chromosome 14q in astrocytic tumors --- p.97
Chapter 4. --- Results --- p.101
Chapter 4.1. --- Mutation analysis of BRAF --- p.101
Chapter 4.2. --- Immunohistochemistry of B-Raf protein --- p.107
Chapter 4.3. --- Quantification of EGFR gene dosage --- p.117
Chapter 4.4. --- Correlation between EGFR dosage and BRAF mutation --- p.128
Chapter 4.5. --- Correlation between EGFR dosage and B-Raf expression --- p.129
Chapter 4.6. --- Microsatellite analysis of chromosome 14q in astrocytic tumors --- p.131
Chapter 5. --- Discussions --- p.149
Chapter 5.1. --- BRAF mutations as common events in human cancers --- p.149
Chapter 5.2. --- BRAF mutation in CNS tumor specimens --- p.150
Chapter 5.2.1. --- Tumorigenic effect of the V599E substitution --- p.153
Chapter 5.2.2. --- V599E B-Raf mutant activation independent of Ras activation --- p.155
Chapter 5.2.3. --- Autocrine stimulation of Ras signaling in V599E B-Raf mutant --- p.156
Chapter 5.3. --- BRAF expression in astrocytic tumors --- p.159
Chapter 5.4. --- Mutually exclusive pattern between EGFR amplification and BRAF expression --- p.161
Chapter 5.4.1. --- Similar effect of EGFR activation and B-Raf activation --- p.163
Chapter 5.4.2. --- Mutual effects between Ras/Raf/Mek/Erk and Akt signaling --- p.164
Chapter 5.5. --- Microsatellite analysis of chromosome 14q in human cancers --- p.167
Chapter 5.6. --- Microsatellite analysis of chromosome 14q in astrocytic tumors --- p.170
Chapter 5.6.1. --- Finer mapping of common regions of deletion --- p.170
Chapter 5.6.2. --- Genes within the common regions of deletion --- p.173
Chapter 5.6.3. --- Overlapping deletion regions in astrocytic and non-CNS tumors --- p.186
Chapter 6. --- Further studies --- p.190
Chapter 6.1. --- Role of BRAF alterations in astrocytic tumors --- p.190
Chapter 6.2. --- B-Raf expression in astrocytic tumors and correlation with EGFR overexpression --- p.193
Chapter 6.3. --- Microsatellite analysis of 14q in astrocytic tumors --- p.194
Chapter 7. --- Conclusions --- p.195
Chapter 8. --- References --- p.198
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Huang, Hsuanfei. "The Investigation on the Interaction among Anti-tumor Compounds derived from Adlay Bran against Human Lung and Colorectal Cancer Cells using Combination Index Methodology." 2007. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-2607200713581900.

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Huang, Hsuanfei, and 黃萱斐. "The Investigation on the Interaction among Anti-tumor Compounds derived from Adlay Bran against Human Lung and Colorectal Cancer Cells using Combination Index Methodology." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/44058585477807348319.

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碩士
臺灣大學
食品科技研究所
95
The malignant tumor has continuously occupied the first place of the top ten leading causes of death for more than twenty years in Taiwan. Among those malignant neoplasm cases, the lung cancer and colorectal cancer are the first and third places of the top ten leading cancer, respectively. In this study, the combination index methodology was used to discuss the synergism or antagonism among these anti-tumor components from adlay bran. The result showed that caffeic acid (PA4) and 3-O-coumaroyl-β-sitosterol (PE1) appeared to be most synergistic compounds for combining with other compounds and chlorogenic acid (PA6) was the most antagonistic compound with other compounds on the growth inhibition of A549, a human lung cancer cell line; while any 2-compound combinations of palmitic acid (FA), 5-hydroxy-7-methoxy- 4’-acetylisoflavone (IF) and stigmasterol (S1) appeared to be synergistic on the growth inhibition HT-29, a human colorectal cancer cell line. Moreover, the cytotoxicity of compounds varied depending on the cell line tested and that such variation may in turn influence those effects of compound combinations. For example, in 2-compound combinations, PA4 exerted synergism when combining with other compounds to inhibit the growth of A549; while it became an antagonist toward other compounds, such as IF and FA, to inhibit the growth of HT-29. Moreover, in 3-compound combinations, the most synergistic 3-compound groups were PA4-PE1-IF and PA6-PE1-FA against A549 growth; while the most synergistic group was S1-IF-FA against HT-29 growth. On the other hand, the combination of PA4, PA6, and IF was the most antagonism on the growth inhibition of A549 and HT-29. From both cell lines, the results of the 3-compound combinations could be explained by the results of the 2-compound combinations. Last, the design of anti-cancer adlay supplement must be tumor-specific since that the cytotoxicity of the compounds varies depending on the cell line tested and that such variation may in turn influence those combined effects of the pure compounds.
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Books on the topic "Brain tumour (Brat)"

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Preusser, Matthias, Gabriele Schackert, and Brigitta G. Baumert. Metastatic brain tumours. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199651870.003.0019.

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Brain metastasis is a common clinical challenge in cancer patients, particularly those with lung cancer, breast cancer, and melanoma. The prognosis is poor, with median overall survival times measured in months for most patient populations. Established treatments include neurosurgical resection, radiotherapy (including stereotactic radiosurgery and stereotactic radiotherapy, whole-brain radiotherapy, and new radiation techniques), and supportive care measures. Recently, more and more targeted therapies such as EGFR inhibitors, HER2 antagonists, BRAF inhibitors, ALK inhibitors, and immune checkpoint inhibitors are demonstrating some efficacy in brain metastasis patients and should be considered in the clinical setting.
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Ramaswamy, Vijay, Jason T. Huse, and Yasmin Khakoo. Pediatric Brain Tumors. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0140.

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Cerebellar astrocytoma of childhood most commonly refers to cerebellar pilocytic astrocytoma, a World health Organization (WHO) Grade I tumor. However, on occasion cerebellar astrocytomas may demonstrate more aggressive histology including fibrillary astrocytomas, pilomyxoid astrocytomas, and rarely malignant lesions. In the near future, the diagnosis of cerebellar astrocytomas will be simplified by molecular analysis for BRAF fusions rather than a purely morphological approach. The emergence of next-generation sequencing can be expected to identify single nucleotide variations and further expand our understanding of both pilocytic astrocytomas as well as rare variants that occur in the cerebellum. Therapies targeting BRAF (B-raf protooncogene) are currently in clinical trial for adult malignancies and will eventually reach the pediatric population, allowing a targeted approach to recurrent and surgically inaccessible cases of pilocytic astrocytomas.
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Book chapters on the topic "Brain tumour (Brat)"

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Pei, Linmin, and Yanling Liu. "Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 315–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08999-2_26.

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Tang, Jiarui, Tengfei Li, Hai Shu, and Hongtu Zhu. "Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 431–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_38.

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Hsu, Cheyu, Chunhao Chang, Tom Weiwu Chen, Hsinhan Tsai, Shihchieh Ma, and Weichung Wang. "Brain Tumor Segmentation (BraTS) Challenge Short Paper: Improving Three-Dimensional Brain Tumor Segmentation Using SegResnet and Hybrid Boundary-Dice Loss." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 334–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_30.

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Isensee, Fabian, Philipp Kickingereder, Wolfgang Wick, Martin Bendszus, and Klaus H. Maier-Hein. "Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challenge." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 287–97. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75238-9_25.

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Maurya, Satyajit, Virendra Kumar Yadav, Sumeet Agarwal, and Anup Singh. "Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 312–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_28.

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Peiris, Himashi, Zhaolin Chen, Gary Egan, and Mehrtash Harandi. "Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 171–81. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08999-2_13.

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Weninger, Leon, Oliver Rippel, Simon Koppers, and Dorit Merhof. "Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challenge." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 3–12. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11726-9_1.

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Bukhari, Syed Talha, and Hassan Mohy-ud-Din. "E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 276–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09002-8_25.

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Fidon, Lucas, Sébastien Ourselin, and Tom Vercauteren. "Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 Challenge." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 200–214. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72087-2_18.

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Henry, Théophraste, Alexandre Carré, Marvin Lerousseau, Théo Estienne, Charlotte Robert, Nikos Paragios, and Eric Deutsch. "Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 327–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72084-1_30.

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Conference papers on the topic "Brain tumour (Brat)"

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Kamble, Vijaya, and Rohin Daruwala. "Classification Comparative Analysis for Detection of Brain Tumor Using Neural Network, Logistic Regression & KNN Classifier with VGG19 Convolution Neural Network Feature Extraction." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.6.

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In recent years due to advancements in digital imaging machine learning techniques are used in medical image analysis for the prognosis and diagnosis of various abnormalities in the human body. Various Machine learning algorithms, convolution and deep neural networks are used for classification, detection and prediction of various brain tumors. The proposed approach is a different comparative classification analysis approach which is based on three different classification namely KNN classifier,Logistic regression & neural network as classifier. It is based on a deep learning feature extraction technique using VGG19. This VGG 19-layer image recognition model trained on Imgenet. Generally, MRI data sequences are analyzed in terms of different modalities and every modality contains rich tissue information. So, feature exaction from MRI sequences is very important task for brain tumor classification. Our approach demonstrated fair classification on BRATS Benchmarks 2018 data set with different modalities and sizes of images,results are without any human annotations. Based on selected classifiers all the classifiers gives accuracy above 90%. It is good compared to other state of art methods.
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Maram, BharathSimhaReddy, and Pooja Rana. "Brain Tumour Detection on BraTS 2020 Using U-Net." In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2021. http://dx.doi.org/10.1109/icrito51393.2021.9596530.

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Malhotra, Radhika, Jasleen Saini, Barjinder Singh Saini, and Savita Gupta. "Improving Brain Tumor Segmentation with Data Augmentation Strategies." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.2.

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In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.
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Ghosh, Arindam, and Sanjeev Thakur. "Review of Brain Tumor MRI Image Segmentation Methods for BraTS Challenge Dataset." In 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2022. http://dx.doi.org/10.1109/confluence52989.2022.9734134.

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Zahedi, Shadi, Andrea M. Griesinger, Todd C. Hankinson, Michael H. Handler, Nicholas K. Foreman, Andrew Thorburn, and Jean M. Mulcahy Levy. "Abstract A25: Autophagy inhibition reverses resistance to targeted BRAF therapy in CNS tumors." In Abstracts: AACR Special Conference: Advances in Brain Cancer Research; May 27-30, 2015; Washington, DC. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.brain15-a25.

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Wang, Jiawan, Zhan Yao, Philip Jonsson, Amy Allen, Alice Can Ran Qin, David Pisapia, Neal Rosen, Barry S. Taylor, and Christine A. Pratilas. "Abstract A129: A second-site mutation in BRAF confers resistance to RAF inhibition in a BRAF V600E-mutant brain tumor." In Abstracts: AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; October 26-30, 2017; Philadelphia, PA. American Association for Cancer Research, 2018. http://dx.doi.org/10.1158/1535-7163.targ-17-a129.

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Li, Shaohua, Xiuchao Sui, Xiangde Luo, Xinxing Xu, Yong Liu, and Rick Goh. "Medical Image Segmentation using Squeeze-and-Expansion Transformers." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/112.

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Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transformers, which have unlimited "effective receptive fields" even at high feature resolutions. The core of Segtran is a novel Squeeze-and-Expansion transformer: a squeezed attention block regularizes the self attention of transformers, and an expansion block learns diversified representations. Additionally, we propose a new positional encoding scheme for transformers, imposing a continuity inductive bias for images. Experiments were performed on 2D and 3D medical image segmentation tasks: optic disc/cup segmentation in fundus images (REFUGE'20 challenge), polyp segmentation in colonoscopy images, and brain tumor segmentation in MRI scans (BraTS'19 challenge). Compared with representative existing methods, Segtran consistently achieved the highest segmentation accuracy, and exhibited good cross-domain generalization capabilities.
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Badr El-Din, Nariman K., Doaa A. Ali, Mai Alaa El-Dein, and Mamdooh Ghoneum. "Abstract 5312: Biobran/MGN-3, arabinoxylan from rice bran, sensitizes breast adenocarcinoma tumor cells to paclitaxol in mice." In Proceedings: AACR 106th Annual Meeting 2015; April 18-22, 2015; Philadelphia, PA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.am2015-5312.

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El-Din, Nariman K. Badr, Sayed K. Areida, Kavan O. Ahmed, and Mamdooh Ghoneum. "Abstract 3932: Enhancing the effectiveness of radiation therapy with arabinoxylan rice bran (MGN-3/ Biobran) in mouse bearing solid tumor." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-3932.

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El-Din, Nariman K. Badr, Sayed K. Areida, Kavan O. Ahmed, and Mamdooh Ghoneum. "Abstract 3932: Enhancing the effectiveness of radiation therapy with arabinoxylan rice bran (MGN-3/ Biobran) in mouse bearing solid tumor." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-3932.

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