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1,201 results for “Oncology”
The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients - Dataset (FASTQ FILES)
<p>Dataset containing FASTQ files of the sequenced samples, generated by the Illumina MiSeq platform. </p> <p><span>This data is freely available under a CC-BY license; if you use it in your work, please cite our paper, "The impact of cefuroxime prophylaxis on human intestinal microbiota in surgical oncological patients" (DOI 10.3389/frmbi.2022.1092771).</span></p>
Symptom Management Implementation of Patient Reported Outcomes in Oncology
ClinicalTrials.gov study NCT03850912. IPD Sharing: YES. Countries: 1. Publications: 20.
Randomized controlled oncology trials with tumor stage inclusion criteria
Open the record for dataset details and reuse information.
Metadata of Rotterdam Oncology Documentation (RONCDOC)
<p>Metadata of retrospective medical data and pathological data of Head and Neck oncology adult patients from the Erasmus Medical Center in Rotterdam.</p> <p>Patients including from jan-2006 till dec-2023 and still ongoing.</p> <p>Number of patients included today (18-12-2023) : 7571 (Male: 5353, Female: 2216; 2 unknown)</p>
Behavioural ecology meets oncology: quantifying the recovery of animal behaviour to a transient exposure to a cancer risk factor
<p>Wildlife is increasingly exposed to sublethal transient cancer risk factors, including mutagenic substances, which activate their anti-cancer defences, promote tumourigenesis, and may negatively impact populations. Little is known about how exposure to cancer risk factors impacts the behaviour of wildlife. Here, we investigated the effects of a sublethal, short-term exposure to a carcinogen at environmentally relevant concentrations on the activity patterns of wild <em>Girardia tigrina</em> planaria during a two-phase experiment, consisting of a 7-day exposure to cadmium period followed by a 7-day recovery period. To comprehensively explore the effects of the exposure on activity patterns, we employed the double hierarchical generalized linear model framework which explicitly models residual intraindividual variability in addition to the mean and variance of the population. We found that exposed planaria were less active compared to unexposed individuals and were able to recover to pre-exposure activity levels albeit with a reduced variance in activity at the start of the recovery phase. Planaria showing high activity levels were less predictable with larger daily activity variations and higher residual variance. Thus, the shift in behavioural variability induced by an exposure to a cancer risk factor can be quantified using advanced tools from the field of behavioural ecology. This is required to understand how tumourous processes affect the ecology of species.</p>
Replication Data for: Precision Oncology, Cell Signaling and Targeted Therapy: A Holistic Approach to Molecular Cancer Therapeutics
<p>In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment. </p>
Molecular Oncology Almanac GK-Pilot Dataset
<p>An export of the <a href="https://moalmanac.org/">Molecular Oncology Almanac (MOA) knowledgebase</a> represented under the <a href="http://gk-pilot.readthedocs.io">pilot GA4GH Genomic Knowledge framework</a> championed by the VICC and ClinGen Driver Projects.</p> <p>Data files include:</p> <ul> <li>moa_gk_pilot_statements.ndjson: MOA evidence represented as genomic knowledge statements</li> <li>moa_gk_pilot_variation_descriptors.ndjson: MOA variation records represented as VRSATILE variation descriptors</li> <li>moa_gk_pilot_methods.ndjson: MOA curation methodology represented as a Method object</li> </ul>
Urine NMR metabolomics for precision oncology in colorectal cancer
<p>Tables summarizing the data used for the review. Up to 7 tables, and a list of the included studies is provided.</p>
Medical publications with information as to whether a publication reports a randomized controlled trial and/or if it covers an oncology topic
<p><strong>Background:</strong></p> <p>Most tools trying to automatically extract information from medical publications are domain agnostic and process publications from any field. However, only retrieving trials from dedicated fields could have advantages for further processing of the data.</p> <p><strong>Dataset collection:</strong></p> <p>A random sample of 900 publications from seven major journals (British Medical Journal, JAMA, JAMA Oncology, Journal of Clinical Oncology, Lancet, Lancet Oncology, New England Journal of Medicine) published between 2010 and 2022 were annotated. Publications that described randomized controlled trials (RCTs) received the label "RCT". Publications that covered oncological topics received the label "ONCOLGY". Trials that fulfilled both criteria were assigned both labels. Trials that were neither RCTs nor covered oncology topics were assigned no label. 100 randomly sampled trials from the New England Journal of Medicine were used as the unseen test set as the journal publishes both oncology and non-oncology articles. </p> <p><strong>Data properties:</strong></p> <p>Each trial is a row in the CSV file. For each trial, there is a doi, a publication date, a title, an abstract, the abstract sections (introduction, methods, results, conclusion), several tags associated with the annotation process (text, _input_hash, _task_hash, options, _view_id, config, accept, answer, _timestamp, _annotator_id,_session_id), and the assigned labels (answer).</p>
Original NGS dataset from publication "Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center".
<p>Original hepatitis C virus hypervariable region 1 NGS sequences in fastq format from patients analyzed in the study "Next-generation sequencing analysis of a cluster of hepatitis C virus infections in a haematology and oncology center". </p> <p> </p>
Primary in vivo screen for MAPK/ERK pathway inhibitors among 433 annotated oncology related compounds
<p>The aim was to test if a C. elegans model could identify known MEK inhibitors in a blinded screen of 433 annotated oncology related compounds. Primary hits were further validated for pathway specificity. Please see the paper cross-reference for more details.</p> <p>We bought a chemical library (SciLife lab, Stockholm, Sweden) with 433 oncology-related compounds (see table S1 for the full list), where compounds had been acoustically dispensed from 10 mM stocks into 96-well plates. DMSO concentrations were adjusted to 0.5 % in all wells, including negative controls. Trametinib was used as a positive control at 7 muM. All chemicals were kept at -20 degrees C until use. The C. elegans strain ST65 was used. We dispensed 50 muL of worm culture into each well and incubated animals for 4-5 days at 20 degrees C until DMSO control animals reached early adulthood. Animals were then sedated for 30 minutes by adding 4 muL 20 mM levamisole per well before scoring.<br><br>Primary hit selection: 1) Wells were excluded if they contained progeny or fluorescent specles from drugs or bacteria, indicated by too many objects classified as vulvae. The cutoff was set to vulvae >2x the number of adults in the positive control. 2) Wells were excluded they contained < 30 % adults compared to the positive control, indicative of severely reduced larval growth. 3) Remaining compounds with vulvae/adult scores < 0.4 were considered primary hits. Scoring of the Muv phenotype in adult lin-1(e1777) mutants was done manually from ImageXpress images. See the paper cross-reference for more details.</p>
Ex vivo modeling of precision immuno-oncology responses in lung cancer
<p>Single-cell RNA-sequencing (scRNA-seq) data from paired lung cancer organoids and immune cells. The experiment was performed using the Single Cell 5' solution of 10X Genomics. </p> <p>The dataset includes 15 samples from 4 multiplexed experiments. The multiplexing was performed using the Feature Barcoding technology of 10X Genomics.</p> <table> <tbody> <tr> <td><strong>Sample name</strong></td> <td><strong>Multiplexed experiment</strong></td> <td><strong>Donor<br></strong></td> <td><strong>Hashtag name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>1-PBMCs_lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Untreated, baseline PBMCs for Lung-19</td> </tr> <tr> <td>2-PBMCs_lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Untreated, baseline PBMCs for Lung-35</td> </tr> <tr> <td>3-PBMCs_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Untreated, baseline PBMCs for Lung-25</td> </tr> <tr> <td>4-T_cells_Lung-19</td> <td>PBMCs_demux</td> <td>Lung-19</td> <td>Hashtag_4</td> <td>Tumor-stimulated immune cells for Lung-19</td> </tr> <tr> <td>5-T_cells_Lung-35</td> <td>PBMCs_demux</td> <td>Lung-35</td> <td>Hashtag_5</td> <td>Tumor-stimulated immune cells for Lung-35</td> </tr> <tr> <td>6-T_cells_Lung-25</td> <td>PBMCs_demux</td> <td>Lung-25</td> <td>Hashtag_6</td> <td>Tumor-stimulated immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-19</td> <td>Hashtag_1</td> <td>Tumor cells alone for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-35</td> <td>Hashtag_2</td> <td>Tumor cells alone for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells</td> <td>Tumor_cells_1_demux</td> <td>Lung-25</td> <td>Hashtag_3</td> <td>Tumor cells alone for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-19</td> <td>Hashtag_7</td> <td>Tumor cells and ts-immune cells for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-35</td> <td>Hashtag_8</td> <td>Tumor cells and ts-immune cells for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells</td> <td>Tumor_cells_2_demux</td> <td>Lung-25</td> <td>Hashtag_9</td> <td>Tumor cells and ts-immune cells for Lung-25</td> </tr> <tr> <td>1-Lung-19_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-19</td> <td>Hashtag_10</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-19</td> </tr> <tr> <td>2-Lung-35_T_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-35</td> <td>Hashtag_12</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-35</td> </tr> <tr> <td>3-Lung-25_tumor_cells_T_cells_Nivolumab</td> <td>Tumor_cells_3_demux</td> <td>Lung-25</td> <td>Hashtag_13</td> <td>Tumor cells and ts-immune cells + Nivolumab for Lung-25</td> </tr> </tbody> </table> <p>This Zenodo repository provides:</p> <ul> <li>Processed RNA-seq and hashtag oligo sequencing (HTO-seq) data (<em>feature_bc_matrices.zip</em>)</li> <li>Hashtag names and sequences (<em>Custom_CMO_set.csv</em>), which are needed to rerun Cellranger</li> <li>Seurat v5 objects (<em>seurat_object_all_tumor_cells.rds, seurat_object_all_immune_cells.rds, seurat_object_PBMCs_demux.rds</em>)</li> </ul> <p>This Zenodo repository does <strong>not </strong>provide:</p> <ul> <li>Sensitive raw sequencing data</li> <li>Sensitive metadata</li> </ul> <p>The raw data generated from the scRNA sequencing is available at the European Genome-phenome Archive (EGA; <a href="https://ega-archive.org">https://ega-archive.org</a>) under accession number EGAD50000000845.</p> <div> <div> <p> </p> <p><strong>To cite our work</strong>:</p> </div> Bassel Alsaed <em>et al.</em> Ex vivo modeling of precision immuno-oncology responses in lung cancer.<em>Sci. Adv.</em><strong>10</strong>,eadq6830(2024).DOI:<a href="https://doi.org/10.1126/sciadv.adq6830">10.1126/sciadv.adq6830</a></div>
Dataset of "Gender analysis and co-authorship networks in the scientific production of oncology in Spain (2011–2021)"
Open the record for dataset details and reuse information.
Dataset for Preferred and actual involvement of caregivers in oncologic treatment decision-making: A systematic review
<p>This dataset shows the search that was done for the publication Preferred and actual involvement of caregivers in oncologic treatment decision-making: A systematic review, which can be found at <a href="https://doi.org/10.1016/j.jgo.2023.101525">https://doi.org/10.1016/j.jgo.2023.101525</a>. </p>
COLUMBIA-1: Novel Oncology Therapies in Combination With Chemotherapy and Bevacizumab as First- Line Therapy in MSS-CRC
ClinicalTrials.gov study NCT04068610. IPD Sharing: YES. Countries: 5. Publications: 1.
Pharmacokinetics of Intravenous Acyclovir in Oncologic Paediatric Patients
ClinicalTrials.gov study NCT05198570. IPD Sharing: NO. Countries: 1. Publications: 1.
CALIPSO: Calfactant for Acute Lung Injury in Pediatric Stem Cell Transplant and Oncology Patients
ClinicalTrials.gov study NCT00999713. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Validation of a Screening Tool in Geriatric Oncology
ClinicalTrials.gov study NCT00963911. IPD Sharing: NO. Countries: 1. Publications: 2.
Sexual Dysfunction in Gynecologic Oncology Patients
ClinicalTrials.gov study NCT03801031. IPD Sharing: NO. Countries: 1. Publications: 1.
Supportive Oncology Care at Home Post-Discharge
ClinicalTrials.gov study NCT04637035. IPD Sharing: YES. Countries: 1. Publications: 1.
ScienceDex guides
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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.