Search results for: taxane

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taxane</title> <meta name="description" content="Search results for: taxane"> <meta name="keywords" content="taxane"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block d-lg-none navbar-toggler ml-auto" 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class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="taxane"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: taxane</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3</span> Modeling Taxane-Induced Peripheral Neuropathy Ex Vivo Using Patient-Derived Neurons</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=G.%20Cunningham">G. Cunningham</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Cantor"> E. Cantor</a>, <a href="https://publications.waset.org/abstracts/search?q=X.%20Wu"> X. Wu</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Shen"> F. Shen</a>, <a href="https://publications.waset.org/abstracts/search?q=G.%20Jiang"> G. Jiang</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Philips"> S. Philips</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20Bales"> C. Bales</a>, <a href="https://publications.waset.org/abstracts/search?q=Y.%20Xiao"> Y. Xiao</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20R.%20Cummins"> T. R. Cummins</a>, <a href="https://publications.waset.org/abstracts/search?q=J.%20C.%20Fehrenbacher"> J. C. Fehrenbacher</a>, <a href="https://publications.waset.org/abstracts/search?q=B.%20P.%20Schneider"> B. P. Schneider</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: Taxane-induced peripheral neuropathy (TIPN) is the most devastating survivorship issue for patients receiving therapy. Dose reductions due to TIPN in the curative setting lead to inferior outcomes for African American patients, as prior research has shown that this group is more susceptible to developing severe neuropathy. The mechanistic underpinnings of TIPN, however, have not been entirely elucidated. While it would be appealing to use primary tissue to study the development of TIPN, procuring nerves from patients is not realistically feasible, as nerve biopsies are painful and may result in permanent damage. Therefore, our laboratory has investigated paclitaxel-induced neuronal morphological and molecular changes using an ex vivo model of human-induced pluripotent stem cell (iPSC)-derived neurons. Methods: iPSCs are undifferentiated and endlessly dividing cells that can be generated from a patient’s somatic cells, such as peripheral blood mononuclear cells (PBMCs). We successfully reprogrammed PBMCs into iPSCs using the Erythroid Progenitor Reprograming Kit (STEMCell Technologiesᵀᴹ); pluripotency was verified by flow cytometry analysis. iPSCs were then induced into neurons using a differentiation protocol that bypasses the neural progenitor stage and uses selected small-molecule modulators of key signaling pathways (SMAD, Notch, FGFR1 inhibition, and Wnt activation). Results: Flow cytometry analysis revealed expression of core pluripotency transcription factors Nanog, Oct3/4 and Sox2 in iPSCs overlaps with commercially purchased pluripotent cell line UCSD064i-20-2. Trilineage differentiation of iPSCs was confirmed with immunofluorescent imaging with germ-layer-specific markers; Sox17 and ExoA2 for ectoderm, Nestin, and Pax6 for mesoderm, and Ncam and Brachyury for endoderm. Sensory neuron markers, β-III tubulin, and Peripherin were applied to stain the cells for the maturity of iPSC-derived neurons. Patch-clamp electrophysiology and calcitonin gene-related peptide (CGRP) release data supported the functionality of the induced neurons and provided insight into the timing for which downstream assays could be performed (week 4 post-induction). We have also performed a cell viability assay and fluorescence-activated cell sorting (FACS) using four cell-surface markers (CD184, CD44, CD15, and CD24) to select a neuronal population. At least 70% of the cells were viable in the isolated neuron population. Conclusion: We have found that these iPSC-derived neurons recapitulate mature neuronal phenotypes and demonstrate functionality. Thus, this represents a patient-derived ex vivo neuronal model to investigate the molecular mechanisms of clinical TIPN. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=chemotherapy" title="chemotherapy">chemotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=iPSC-derived%20neurons" title=" iPSC-derived neurons"> iPSC-derived neurons</a>, <a href="https://publications.waset.org/abstracts/search?q=peripheral%20neuropathy" title=" peripheral neuropathy"> peripheral neuropathy</a>, <a href="https://publications.waset.org/abstracts/search?q=taxane" title=" taxane"> taxane</a>, <a href="https://publications.waset.org/abstracts/search?q=paclitaxel" title=" paclitaxel"> paclitaxel</a> </p> <a href="https://publications.waset.org/abstracts/117612/modeling-taxane-induced-peripheral-neuropathy-ex-vivo-using-patient-derived-neurons" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/117612.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">122</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> Diagnosing and Treating Breast Cancer during Pregnancy: Neonatal Outcomes after Chemotherapy</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Elyce%20Cardonick">Elyce Cardonick</a>, <a href="https://publications.waset.org/abstracts/search?q=Shistri%20Dhar"> Shistri Dhar</a>, <a href="https://publications.waset.org/abstracts/search?q=Linsdey%20Seidman"> Linsdey Seidman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Background: When breast cancer is diagnosed during pregnancy, the prognosis is comparable to non-pregnant women matched for prognostic indicators when pregnant women receive treatment without delay. Chemotherapy, including taxanes, can be given during pregnancy with normal neonatal development in exposed fetuses. Methods: Cases of primary breast cancer were extracted from the Cancer and Pregnancy Registry and longitudinal study at Cooper Medical School, which collects cases of pregnant women diagnosed and treated for cancer into a single database. Obstetrical, oncology and pediatric records were reviewed, including annual neonatal developmental, behavioral and medical assessments. Results: 270 pregnant women were diagnosed with primary breast cancer at a mean gestational age of 14.7+9weeks. Mean maternal age at diagnosis 34.5+4.5 years. Receptor status is comparable to non-pregnant women of reproductive age. Forty-nine women were advised to terminate. Two hundred two women underwent surgery;244 women received chemotherapy in pregnancy after the first trimester; the majority of Doxorubucin/Cytoxan; 81 of the cases included a taxane. At a mean of 90 months, follow up obtained on 255 newborns.192/255 newborns are meeting developmental milestones. Respiratory illnesses, including asthma, and bronchiolitis, were reported in 64 newborns, the most common medical condition reported. Thirty-one children are undergoing treatment for GERD, 11 for urinary tract infections, and 7 are undergoing treatment for anemia. Twenty-six children with expressive or articulation language delays, 21/26 are mild. Eleven children with gross/ 7 with fine motor delays. Eight children are treated for ADHD, 4 for anxiety and 4 have social skill impairment. The majority of children with developmental, language or motor delays were born preterm. Conclusion: After chemotherapy exposure in utero for breast cancer, the majority of newborns are meeting developmental milestones and are medically healthy. The goal for treating pregnant women with breast cancer is to aim for delivery close to the term. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=breast%20cancer" title="breast cancer">breast cancer</a>, <a href="https://publications.waset.org/abstracts/search?q=pregnancy" title=" pregnancy"> pregnancy</a>, <a href="https://publications.waset.org/abstracts/search?q=chemotherapy" title=" chemotherapy"> chemotherapy</a>, <a href="https://publications.waset.org/abstracts/search?q=newborn" title=" newborn"> newborn</a> </p> <a href="https://publications.waset.org/abstracts/152106/diagnosing-and-treating-breast-cancer-during-pregnancy-neonatal-outcomes-after-chemotherapy" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/152106.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> DeepNIC a Method to Transform Each Tabular Variable into an Independant Image Analyzable by Basic CNNs</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Nguyen%20J.%20M.">Nguyen J. M.</a>, <a href="https://publications.waset.org/abstracts/search?q=Lucas%20G."> Lucas G.</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruan%20S."> Ruan S.</a>, <a href="https://publications.waset.org/abstracts/search?q=Digonnet%20H."> Digonnet H.</a>, <a href="https://publications.waset.org/abstracts/search?q=Antonioli%20D."> Antonioli D.</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=tabular%20data" title="tabular data">tabular data</a>, <a href="https://publications.waset.org/abstracts/search?q=CNNs" title=" CNNs"> CNNs</a>, <a href="https://publications.waset.org/abstracts/search?q=NICs" title=" NICs"> NICs</a>, <a href="https://publications.waset.org/abstracts/search?q=DeepNICs" title=" DeepNICs"> DeepNICs</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20forest%20of%20perfect%20trees" title=" random forest of perfect trees"> random forest of perfect trees</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/166192/deepnic-a-method-to-transform-each-tabular-variable-into-an-independant-image-analyzable-by-basic-cnns" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/166192.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">125</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); 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Search results for: taxane