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Статті в журналах з теми "Brain tumour (Brat)"
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.
Повний текст джерелаS, 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.
Повний текст джерелаT, 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.
Повний текст джерелаAlmajmaie, 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.
Повний текст джерелаRavikumar, 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.
Повний текст джерелаLokody, Isabel. "BRAF mutation drives rare brain tumour." Nature Reviews Cancer 14, no. 3 (February 24, 2014): 157. http://dx.doi.org/10.1038/nrc3693.
Повний текст джерелаBerghoff, 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.
Повний текст джерелаLima, 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.
Повний текст джерелаFrank, 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.
Повний текст джерелаSakamoto, 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.
Повний текст джерелаДисертації з теми "Brain tumour (Brat)"
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.
Повний текст джерелаGlial 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
Padovani, Laëtitia. "Caractérisation moléculaire des tumeurs cérébrales circonscrites de l'enfant." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM5018.
Повний текст джерелаThe 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
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.
Повний текст джерелаManuell 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.
Hussain, Rashid. "Exploring metabolic interventions for CIN cancer therapy." Thesis, 2017. http://hdl.handle.net/2440/119191.
Повний текст джерелаThesis (Ph.D.) -- University of Adelaide, School of Biological Sciences, 2017
"Molecular analysis of BRAF and microsatellite analysis of chromosome 14q in astrocytic tumors." 2004. http://library.cuhk.edu.hk/record=b5892088.
Повний текст джерелаThesis 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
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.
Повний текст джерелаHuang, 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.
Повний текст джерела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.
Книги з теми "Brain tumour (Brat)"
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.
Повний текст джерелаRamaswamy, Vijay, Jason T. Huse, and Yasmin Khakoo. Pediatric Brain Tumors. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0140.
Повний текст джерелаЧастини книг з теми "Brain tumour (Brat)"
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.
Повний текст джерелаTang, 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.
Повний текст джерелаHsu, 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.
Повний текст джерелаIsensee, 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.
Повний текст джерелаMaurya, 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.
Повний текст джерелаPeiris, 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.
Повний текст джерелаWeninger, 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.
Повний текст джерелаBukhari, 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.
Повний текст джерелаFidon, 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.
Повний текст джерелаHenry, 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.
Повний текст джерелаТези доповідей конференцій з теми "Brain tumour (Brat)"
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.
Повний текст джерелаMaram, 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.
Повний текст джерелаMalhotra, 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.
Повний текст джерелаGhosh, 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.
Повний текст джерелаZahedi, 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.
Повний текст джерелаWang, 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.
Повний текст джерелаLi, 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.
Повний текст джерелаBadr 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.
Повний текст джерелаEl-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.
Повний текст джерелаEl-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.
Повний текст джерела