Literatura académica sobre el tema "Combinatory therapy"
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Artículos de revistas sobre el tema "Combinatory therapy"
Park, Hongsuk, Sungpil Cho, Yong Hwan Han, Margit M. Janat-Amsbury, Sihem Boudina y You Han Bae. "Combinatory gene therapy for obesity control". Nanomedicine: Nanotechnology, Biology and Medicine 12, n.º 2 (febrero de 2016): 454. http://dx.doi.org/10.1016/j.nano.2015.12.019.
Texto completoSenichkin, V. V., G. S. Kopeina, A. V. Zamaraev, I. N. Lavrik y B. D. Zhivotovsky. "Nutrient restriction in combinatory therapy of tumors". Molecular Biology 50, n.º 3 (mayo de 2016): 362–78. http://dx.doi.org/10.1134/s0026893316030109.
Texto completoSingh, Brahma N., Prateeksha, Vijai K. Gupta, Jieyin Chen y Atanas G. Atanasov. "Organic Nanoparticle-Based Combinatory Approaches for Gene Therapy". Trends in Biotechnology 35, n.º 12 (diciembre de 2017): 1121–24. http://dx.doi.org/10.1016/j.tibtech.2017.07.010.
Texto completoYou, Mengmeng, Kangli Wang, Yongming Pan, Lingchen Tao, Quanxin Ma, Guozhi Zhang y Fuliang Hu. "Combined royal jelly 10-hydroxydecanoic acid and aspirin has a synergistic effect against memory deficit and neuroinflammation". Food & Function 13, n.º 4 (2022): 2336–53. http://dx.doi.org/10.1039/d1fo02397g.
Texto completoHa, Ji-Hui y Young-Jin Kim. "Photodynamic and Cold Atmospheric Plasma Combination Therapy Using Polymeric Nanoparticles for the Synergistic Treatment of Cervical Cancer". International Journal of Molecular Sciences 22, n.º 3 (25 de enero de 2021): 1172. http://dx.doi.org/10.3390/ijms22031172.
Texto completoLiu, Aiyun, Huaisong Wang, Xiaoshuang Hou, Yu Ma, Gongjun Yang, Yanglong Hou y Ya Ding. "Combinatory antitumor therapy by cascade targeting of a single drug". Acta Pharmaceutica Sinica B 10, n.º 4 (abril de 2020): 667–79. http://dx.doi.org/10.1016/j.apsb.2019.08.011.
Texto completoPurwati, Andang Miatmoko, Nasronudin, Eryk Hendrianto, Deya Karsari, Aristika Dinaryanti, Nora Ertanti et al. "An in vitro study of dual drug combinations of anti-viral agents, antibiotics, and/or hydroxychloroquine against the SARS-CoV-2 virus isolated from hospitalized patients in Surabaya, Indonesia". PLOS ONE 16, n.º 6 (18 de junio de 2021): e0252302. http://dx.doi.org/10.1371/journal.pone.0252302.
Texto completoYouf, Raphaëlle, Max Müller, Ali Balasini, Franck Thétiot, Mareike Müller, Alizé Hascoët, Ulrich Jonas et al. "Antimicrobial Photodynamic Therapy: Latest Developments with a Focus on Combinatory Strategies". Pharmaceutics 13, n.º 12 (24 de noviembre de 2021): 1995. http://dx.doi.org/10.3390/pharmaceutics13121995.
Texto completoSui, Hongshu, Ningxia Ma, Ying Wang, Hui Li, Xiaoming Liu, Yanping Su y Jiali Yang. "Anti-PD-1/PD-L1 Therapy for Non-Small-Cell Lung Cancer: Toward Personalized Medicine and Combination Strategies". Journal of Immunology Research 2018 (8 de agosto de 2018): 1–17. http://dx.doi.org/10.1155/2018/6984948.
Texto completoXu, Ronald X., Jeff S. Xu, Tao Zuo, Rulong Shen, Tim H. Huang y Michael F. Tweedle. "Drug-loaded biodegradable microspheres for image-guided combinatory epigenetic therapy in cells". Journal of Biomedical Optics 16, n.º 2 (2011): 020507. http://dx.doi.org/10.1117/1.3548878.
Texto completoTesis sobre el tema "Combinatory therapy"
Hargrave, Gillian. "Understanding the independent and combinatory effects of radiation therapy and doxorubicin on endothelial cell function : the role of JNK". Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=29262.
Texto completoDARICI, SALIHA NUR. "LEUCEMIA MIELOIDE ACUTA CON MUTAZIONE FLT3-ITD: razionale per l'uso combinato di inibitori di fosfoinositide 3-chinasi e recettori tirosin chinasici". Doctoral thesis, Università degli studi di Modena e Reggio Emilia, 2022. http://hdl.handle.net/11380/1278342.
Texto completoAcute myeloid leukemia (AML) has a very poor 5-year survival of ~20% in Europe. The internal tandem duplication (ITD) mutation of the Fms-like receptor tyrosine kinase 3 (FLT3) (FLT3-ITD) is the most frequent mutation (~25%) in normal karyotype AML. In recent clinical studies, few patients display prolonged remissions with receptor tyrosine kinase (RTK) inhibitors, such as FLT3 inhibitors (FLT3i) therapy, highlighting a substantial unmet need for novel effective treatment. Persistence of leukemia stem cells (LSC) drive AML leukemogenesis, responsible for drug resistance and disease relapse following conventional chemotherapy. Growing evidence recognizes that FLT3-ITD mutation leads to the constitutive activation of FLT3 kinase and its downstream pathways, including PI3K/AKT/mTOR signaling, strongly associated with LSC survival and crosstalk between LSC and stromal cells associated bone marrow (BM) tumor environment (TME). The TME provides protection of FLT3-ITD AML cells against FLT3 inhibitors. Thus, the PI3K/AKT/mTOR pathway may represent as a putative target for FLT3-ITD AML. This study aims to test the hypothesis that PI3K/AKT/mTOR inhibition could sensitize FLT3-ITD AML cells to RTKi-lead targeted therapy using human AML cell lines and primary patient blasts. First, I uncover the phenotypic profile of FLT3-ITD versus FLT3 wildtype cell lines following treatment with selected FLT3i or PI3K/AKT/mTORi that have failed treatment of AML as monotherapy in clinical studies. More specifically, I determine the drug efficacy by means of cell growth measurement and assessment of cell cycle status and apoptosis. I was able to demonstrate that BAY-806946 (pan PI3Ki) and PF-04691502 (dual PI3K/mTORi) exerted growth inhibitory activity caused by G1 cell cycle arrest and apoptosis, and this effect was irrespective of FLT3 status. Quizartinib (FLT3i) selectively inhibited cell growth in FLT3-ITD AML and this effect was mainly caused by apoptosis. The observed drug-induced apoptotic effect was however not as efficient as chemotherapy. Next, I provide proof-of-concept for the combination of quizartinib and BAY-806946 using both FLT3-ITD AML cell lines and primary patient blasts. When evaluating on primary patient blasts, I take into consideration the protective role of mesenchymal stromal cells and physiological growth factors to mimic the BM microenvironment. Hereby, I co-cultured FLT3-ITD AML blasts with stromal cell line MS-5 and added growth factors essential for AML survival and differentiation such as IL-3, TPO and G-CSF at physiological concentration. As expected, treatment with BAY-806946 enhanced both cytostatic and cytotoxic effect of quizartinib in FLT3-ITD AML cell line MOLM-13 as well as primary patient blasts in co-culture. More importantly, enhanced apoptosis was measured in the stem cell like CD34+CD38- population. Lastly, I elucidate the cytokine profile and persistent phosphoproteins as putative targets following combination treatment. Ultimately, this study demonstrates the potential of PI3K/AKT/mTORi to enhance the efficacy of RTKi quizartinib for the treatment of FLT3-ITD AML.
Ripoll, Manon. "Synthèse de nano-vecteurs dérivés des polydiacétylènes pour la co-délivrance d’un ARN interférent et d’un anticancéreux". Thesis, Strasbourg, 2017. http://www.theses.fr/2017STRAF076/document.
Texto completoIn the nanomedecine field, a new approach consists in developing synthetic vectors able to co-deliver into a cancer cell, an antitumoral drug and siRNAs that target protein(s) involved in MDR. The work described in this manuscript was dedicated to the development of micellar nanovectors for the intracellular co-delivery of these two therapeutic agents. The first part details the synthesis and the formulation of nanometric photopolymerized diacetylenic micelles adapted for the delivery and intracellular release of the siRNA. Then, the encapsulation and delivery properties of these micelles, bearing histidine polar heads have been investigated in vitro and in vivo for the application of combination therapy. Finally, the last part presents the functionalization by electrostatic interaction of these cationic vectors with antibodies, priorly modified by anionic oligonucleotides. This original and versatile system allowed achieving an active targeting of tumoral cells
Filipe, Pedro Matos Pinto Santos. "A Computational Method to Predict the Combinatory Effect of Drugs in Cancer". Master's thesis, 2019. http://hdl.handle.net/10316/88210.
Texto completoO cancro é uma das causas mais significativas de mortalidade em todo o mundo. O impacto de fatores ambientais e a crescente adoção de comportamentos de risco têm contribuído para a alta incidência de cancro, especialmente em países subdesenvolvidos, onde o acesso ao diagnóstico precoce, ao tratamento ou aos cuidados paliativos é limitado. Além disso, a elevada heterogeneidade e a complexidade biológica das células tumorais tornam a resistência à terapia um problema emergente. Uma das alternativas para combater o problema das resistências é encontrar estratégias terapêuticas alternativas de baixo custo que não envolvam todas as etapas do processo complexo e oneroso da investigação e desenvolvimento de novos fármacos. O desenvolvimento de novas terapias combinatórias, uma abordagem que aumenta a eficácia e reduz a probabilidade de resistência aos fármacos, é uma solução frequentemente utilizada e relativamente acessível, que requer menos ensaios clínicos em comparação com o desenvolvimento de novos medicamentos. A ascensão de tecnologias Ómicas de elevado rendimento possibilitaram a aquisição de grandes quantidades de dados, desde a génomica, transcriptómica, proteómica até à metabolómica, que têm permitindo caracterizar as células tumorais do ponto de vista biológico e funcional. No entanto, lidar com esta enorme quantidade de dados, de modo a que possam ser extraídos conhecimentos biológicas proveitosos que possam ajudar no desenvolvimento de terapias mais direcionadas, é uma tarefa complexa. Os métodos de Aprendizagem Computacional (AC) são abordagens cada vez mais populares e baratas, para a análise de dados de Ómicas e na integração desse conhecimento com outros dados relacionados com o cancro.Neste trabalho, propomos um novo modelo de AC para a previsão de novas soluções de terapias combinatórias, recorrendo para isso a dados de Ómicas que caracterizam as linhas celulares de tumores (especificamente para dados de expressão, metilação e variação do número de cópias) e às propriedades físico-químicas e estruturais de fármacos aprovados pela FDA para quimioterapia no cancro. A abordagem com melhor desempenho, um Ensemble Model composto por uma Deep Neural Network, uma Random Forest e uma Support Vector Machine, obteve uma accuracy de 0.74, precision de 0.75 e recall de 0.90. Este modelo possibilitou a previsão de novas combinações terapêuticas através da realização de ensaios de screening de fármacos conducentes à eliminação de candidatos menos vantajosos. Além disso fomos mais ambiciosos e desenvolvemos uma nova base de dados de Proteínas Membranares (os alvos mais comuns para a quimioterapia) que contém as suas principais características e das suas interfaces. Esta base de dados é uma ferramenta importante para estudos futuros no âmbito deste trabalho.
Cancer is one of the more significant causes of mortality worldwide. The impact of environmental factors and the growing adoption of risk behaviours have contributed to the high incidence of cancer, especially in low-income countries, where access to screening, early diagnosis, treatment or palliative care is limited. Furthermore, the high tumour heterogeneity and biological complexity make drug-resistance an overwhelming problem. Overcoming this problem can be achieved by finding new and low-cost therapeutic alternatives that do not involve taking the extremely pricy path of original drug R&D. The development of new combinatory therapies is a commonly used cheaper solution that requires fewer clinical experiments when compared to the development process of new drugs, and that often increases the efficacy and reduces the probability of drug resistance. In the last years, the rises of new high-throughput OMICs technologies have made possible the acquisition of large amounts of OMICs data, allowing the unprecedented characterization of cancer biology and behaviour. Dealing with these massive amounts of data so that prolific biological interpretations can be extracted that may aid in the development of more targeted therapies has been a daunting task. Machine Learning (ML) methods are increasingly popular cheaper and faster approaches used to analyse OMICs and integrate this knowledge with other cancer-related data. In this work, we propose a new ML model for the prediction of an innovative combinatory therapeutic solution, developed using OMICs data that characterize cancer cell lines (specifically expression, methylation and copy number variation) and structural and physico-chemical properties of drugs approved by the FDA for cancer chemotherapy. The best performing approach, an ensemble model comprising a Deep Neural Network, a Random Forest and a Support Vector Machine, achieved 0.74 accuracy, 0.75 precision and 0.90 recall and was suited for the prediction of new combinations for chemotherapy by reliably performing drug screening assays and eliminating less advantageous candidates. We went further ahead and developed also a new database of Membrane Proteins, the most common targets for chemotherapy, and analyse their main interfacial features and characteristics. This database is indeed an important tool for future studies regarding the subject of this work.
Martinez, Banderas Aldo. "Cancer Therapy based on Core-Shell Iron-Iron Oxide Nanowires". Diss., 2020. http://hdl.handle.net/10754/666239.
Texto completoRuzzolini, Jessica. "Un aiuto dalla natura nella lotta contro i tumori: proprietà antineoplastiche di prodotti derivanti da piante di olivo". Doctoral thesis, 2021. http://hdl.handle.net/2158/1239183.
Texto completoLibros sobre el tema "Combinatory therapy"
Kahn, Michael. High Throughput Screening for Novel Anti-Inflammatories (Progress in Inflammation Research). Birkhauser, 2000.
Buscar texto completoCapítulos de libros sobre el tema "Combinatory therapy"
Yuan, Bo, Masahiko Imai, Hidetomo Kikuchi, Shin Fukushima, Shingo Hazama, Takenori Akaike, Yuta Yoshino et al. "Cytocidal Effects of Polyphenolic Compounds, Alone or in Combination with, Anticancer Drugs Against Cancer Cells: Potential Future Application of the Combinatory Therapy". En Apoptosis and Medicine. InTech, 2012. http://dx.doi.org/10.5772/50218.
Texto completoGoudouna, Sozita. "Intermedial Breath: Defying the Boundaries between Displaying and Staging". En Beckett's Breath. Edinburgh University Press, 2018. http://dx.doi.org/10.3366/edinburgh/9781474421645.003.0007.
Texto completo"Case 31. Use of Combinaton Therapy In A Patient With Complex Dyslipidemia". En Case Studies in Lipid Management, 173–78. CRC Press, 2006. http://dx.doi.org/10.3109/9780203641347-34.
Texto completoActas de conferencias sobre el tema "Combinatory therapy"
Soares, Heloisa P., Ming Ming, Michelle Mellon, James Sinnet-Smith y Enrique Rozengurt. "Abstract 5453: Dual mTOR/PI3K inhibitors induce ERK pathway overactivation in pancreatic cell lines: Rationale for combinatory therapy". En Proceedings: AACR Annual Meeting 2014; April 5-9, 2014; San Diego, CA. American Association for Cancer Research, 2014. http://dx.doi.org/10.1158/1538-7445.am2014-5453.
Texto completoGanapathy, Manonmani, Hareesh Babu Bhaskaran Nair, Nameer Kirma, Naveen Krishnegowda, Ratna Vadlamudi, Tagliaferri Mary, Cohen Isaac y Rajeshwar Tekmal. "Abstract 4605: Combinatory use of synthetic and natural ER beta modulators in treating hormonal-therapy-resistant breast tumors". En Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC. American Association for Cancer Research, 2010. http://dx.doi.org/10.1158/1538-7445.am10-4605.
Texto completoSu, Yapeng, Wei Wei, Min Xue, Lidia Robert, Jennifer Tsoi, Thomas Graeber, Antoni Ribas y James Heath. "Abstract 879: Single cell analysis resolves combinatory targeted therapy for arresting the BRAFi-induced cellular dedifferentiation of metastatic melanomas". En Proceedings: AACR 107th Annual Meeting 2016; April 16-20, 2016; New Orleans, LA. American Association for Cancer Research, 2016. http://dx.doi.org/10.1158/1538-7445.am2016-879.
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