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2,550
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Dataset results
2,550 results for “Glioblastoma”
Study of AMG 596 in Patients With EGFRvIII Positive Glioblastoma
ClinicalTrials.gov study NCT03296696. IPD Sharing: YES. Countries: 6. Publications: 1.
A Study of the Effectiveness and Safety of Nivolumab Compared to Bevacizumab and of Nivolumab With or Without Ipilimumab in Glioblastoma Patients
ClinicalTrials.gov study NCT02017717. IPD Sharing: Not stated. Countries: 12. Publications: 4.
Disulfiram/Copper With Concurrent Radiation Therapy and Temozolomide in Patients With Newly Diagnosed Glioblastoma
ClinicalTrials.gov study NCT02715609. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Deuterium metabolic imaging phenotypes mouse glioblastoma heterogeneity through glucose turnover kinetics
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Glioblastoma-astrocytes coculture data
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Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
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Data from: Spatiotemporal modeling reveals high-resolution invasion states in glioblastoma
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Competitive binding of E3 ligases TRIM26 and WWP2 controls SOX2 in glioblastoma
<p>This repository contains proteomics data and 10x Genomics single-cell RNA sequencing data for "Competitive binding of E3 ligases TRIM26 and WWP2 controls SOX2 in glioblastoma." Proteomics data for three samples (one IgG control and two replicates of the SOX2 IP) is stored in the file ProteomicsData.zip. scRNAseq for four samples (from three tumors) is stored in a Seurat object (<a href="https://zenodo.org/api/files/2eac7284-dbc2-44d6-bd94-270ebc55a8c6/Cycling.SCT.PCA.UMAP.TSNE.CLUST.200522.rds">Cycling.SCT.PCA.UMAP.TSNE.CLUST.200522.rds</a>). Raw fastq files have been deposited in Annotare. As of 9/18/20, they are still in the curation stage.</p>
Data and code for comparison of different machine learning methods and dimensionality reduction for classification astrocytoma and glioblastoma tissues by mass spectra
<p>This upload contains all replication material for "Comparison of different machine learning methods and dimensionality reduction for classification astrocytoma and glioblastoma tissues by mass spectra" (forthcoming).</p> <p><strong>Authors:</strong> E.S. Zhvansky, A.A. Sorokin, V.A. Shurkhay, V.A. Eliferov, D.S. Bormotov, D.G. Ivanov, D.S. Zavorotnyuk, A.A. Potapov.</p> <p><strong>Code and data are located within data_and_code.zip.</strong> Code is written in Python 3.7.7 using Jupyter Notebook, MATLAB R2019b, and Python 3.5.2.</p> <p>Please find the readme.txt for code using and the code to replicate the main findings of the paper described below:</p> <ul> <li>venn_diagramm.py for Venn diagram figures.</li> <li>SSM.m for SSM calculation and visualization.</li> <li>DR_ML.ipynb for dimensionality reduction and machine learning algorithms comparing on the datasets.</li> </ul> <p> </p>
Data and code for analysis of ion currents in mass spectrometric profiles using glioblastoma tissue
<p>This upload contains all replication material for "Analysis of ion currents in mass spectrometric profiles using glioblastoma tissue" (forthcoming).</p> <p><strong>Authors:</strong> E.S. Zhvansky, A.A. Sorokin, D.S. Zavorotnyuk, V.A. Shurkhay, D.S. Bormotov, A.A. Potapov.</p> <p><strong>Code and data are located within spectra_data_and_code.zip.</strong> Code is written in MATLAB R2019b.</p> <p>Please find the readme.txt for code using and the code to replicate the main findings of the paper (figures_replication.m).</p>
Data used for training glioblastoma NF1 classifier
<p>All data is publicly available and downloaded from UCSC Xena<br /> https://genome-cancer.ucsc.edu/proj/site/xena/datapages/?cohort=TCGA%20Pan-Cancer</p> <p>Because the database is continously updated and to ensure reproducibility, access data from this cached download.</p> <p>RNAseq and Clincal data were downloaded on 8 March 2016<br /> Mutation data was downloaded on 12 June 2015</p>
Clinical and genomic predictors of adverse events in newly diagnosed glioblastoma
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WT1 targeting multi-epitope vaccine design for glioblastoma multiforme using immuno-informatics approaches
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Single-nucleotide-resolution genomic maps of O6-methylguanine from the glioblastoma drug temozolomide
<p>Temozolomide kills cancer cells by forming <em>O</em><sup>6</sup>-methylguanine (<em>O</em><sup>6</sup>-MeG), which leads to cell cycle arrest and apoptosis. However,<em> O</em><sup>6</sup>-MeG repair by <em>O</em><sup>6</sup>-methylguanine-DNA methyltransferase (MGMT) contributes to drug resistance. Characterizing genomic profiles of <em>O</em><sup>6</sup>-MeG could elucidate how <em>O</em><sup>6</sup>-MeG accumulation is influenced by repair, but there are no methods to map genomic locations of <em>O</em><sup>6</sup>-MeG. Here, we developed an immunoprecipitation- and polymerase-stalling-based method, termed <em>O</em><sup>6</sup>-MeG-seq, to locate <em>O</em><sup>6</sup>-MeG across the whole genome at single-nucleotide resolution. We analyzed <em>O</em><sup>6</sup>-MeG formation and repair with regards to sequence contexts and functional genomic regions in a glioblastoma-derived cell line and evaluated the impact of MGMT expression. <em>O</em><sup>6</sup>-MeG signatures were highly similar to mutational signatures from patients previously treated with temozolomide. Furthermore, MGMT did not preferentially repair <em>O</em><sup>6</sup>-MeG with respect to sequence context, chromatin state or gene expression level, however, may protect oncogenes from mutations. Finally, we found an MGMT-independent strand bias in <em>O</em><sup>6</sup>-MeG accumulation in highly expressed genes. These data provide high resolution insight on how <em>O</em><sup>6</sup>-MeG formation and repair are impacted by genome structure and nucleotide sequence. Further, <em>O</em><sup>6</sup>-MeG-seq is expected to enable future studies of DNA modification signatures as diagnostic markers for addressing drug resistance and preventing secondary cancers.</p>
Glioblastoma multiforme (GBM)
<p>Glioblastoma multiforme (GBM) data from the TCGA.</p>
Imaging mass cytometry data from IDH wildtype glioblastomas
<p>Myeloid cells are highly prevalent in glioblastoma (GBM), existing in a spectrum of phenotypic and activation states. We now have limited knowledge of the tumor microenvironment (TME) determinants that influence the localization and the functions of the diverse myeloid cell populations in GBM. In this dataset, we have used imaging mass cytometry to identify and map the various myeloid populations in the human GBM tumor microenvironment (TME) using known markers for myeloid and neoplastic cells in GBM. Our analyses of these data found that different myeloid populations had distinct and reproducible compartmentalization patterns in the GBM TME that were driven by tissue hypoxia and varied homotypic and heterotypic cellular interactions. This dataset consists of imaging mass cytometry data (16-bit TIFF images) for 8 glioblastomas and 1 tonsil sourced from the Salford Royal NHS Trust Biobank.</p>
Publication analysis on glioblastoma
<p>Supplementary material 1: original data of glioblastoma publication, Supplementary material 2: python code for analysis.</p> <p> </p> <p><a href="https://zenodo.org/record/3370810#collapseOne">Preview</a></p>
Nanocomposite formulation for a sustained release of free drug and drug-loaded responsive nanoparticles: an approach for a local therapy of glioblastoma multiforme
<p>Malignant gliomas are a type of primary brain tumour that originates in glial cells. Among them, glioblastoma multiforme (GBM) is the most common and the most aggressive brain tumour in adults, classified as grade IV by the World Health Organization. The standard care for GBM, known as the Stupp protocol includes surgical resection followed by oral chemotherapy with temozolomide (TMZ). This treatment option provides a median survival prognosis of only 16–18 months to patients mainly due to tumour recurrence. Therefore, enhanced treatment options are urgently needed for this disease. Here we show the development, characterization, and in vitro and in vivo evaluation of a new composite material for local therapy of GBM post-surgery. We developed responsive nanoparticles that were loaded with paclitaxel (PTX), and that showed penetration in 3D spheroids and cell internalization. These nanoparticles were found to be cytotoxic in 2D (U-87 cells) and 3D (U-87 spheroids) models of GBM. The incorporation of these nanoparticles into a hydrogel facilitates their sustained release in time. Moreover, the formulation of this hydrogel containing PTX-loaded responsive nanoparticles and free TMZ was able to delay tumour recurrence in vivo after resection surgery. Therefore, our formulation represents a promising approach to develop combined local therapies against GBM using injectable hydrogels containing nanoparticles.</p>
Mesoporous Silica Nanoparticles for pH-Responsive Delivery of Iridium Metallotherapeutics and Treatment of Glioblastoma Multiforme
<p>Using nanoparticles for controlled drug delivery to cancer, in response to its weakly acidic environment, represents a promising approach toward increasing the effectiveness and reducing the adverse effects of cancer therapy. Hence, the aim of this study is to construct novel mesoporous silica nanoparticle (MSN)-based acidification-responsive drug delivery systems for targeted cancer therapy. Herein, the surface of MSN is covalently functionalized with Ir(III)-based complex through a pH-cleavable hydrazone-based linker and characterized by nitrogen sorption, SEM, FTIR, EDS, TGA, DSC, DLS, and zeta potential measurements. Enhanced release of Ir(III)-complexes is evidenced by UV/VIS spectroscopy at the weakly acidic environments (pH 5 and pH 6) in comparison to the release at physiological conditions. The in vitro toxicity of the prepared materials is tested on healthy MRC-5 cells while their potential for the efficient treatment of glioblastoma multiforme is demonstrated on the U251 cell line.</p>
Single-cell heterogeneity of EGFR and CKD4 co-amplification is linked to immune infiltration in glioblastoma
<p>This upload contains RDS objects of preprocessed publicly available scRNAseq data, required to run scRNAseq analyses in the manuscript "Single cell heterogeneity of EGFR and CDK4 co-amplification is linked to immune infiltration in glioblastoma". The corresponding GitHub repo <a href="https://github.com/Michorlab/GBM_OR_immune">https://github.com/Michorlab/GBM_OR_immune</a> contains code to analyze the data here, as well as plots and tables generated on the basis of this data.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.