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15,247 results for “Breast cancer;”
Data from: A study on genetic variants of Fibroblast Growth Factor Receptor 2 (FGFR2) and the risk of breast cancer from North India
Genome-Wide Association Studies (GWAS) have identified Fibroblast growth factor receptor 2 (FGFR2) as a candidate gene for breast cancer with single nucleotide polymorphisms (SNPs) located in intron 2 region as the susceptibility loci strongly associated with the risk. However, replicate studies have often failed to extrapolate the association to diverse ethnic regions. This hints towards the existing heterogeneity among different populations, arising due to differential linkage disequilibrium (LD) structures and frequencies of SNPs within the associated regions of the genome. It is therefore important to revisit the previously linked candidates in varied population groups to unravel the extent of heterogeneity. In an attempt to investigate the role of FGFR2 polymorphisms in susceptibility to the risk of breast cancer among North Indian women, we genotyped rs2981582, rs1219648, rs2981578 and rs7895676 polymorphisms in 368 breast cancer patients and 484 healthy controls by Polymerase chain reaction-Restriction fragment length polymorphism (PCR-RFLP) assay. We observed a statistically significant association with breast cancer risk for all the four genetic variants (P<0.05). In per-allele model for rs2981582, rs1219648, rs7895676 and in dominant model for rs2981578, association remained significant after bonferroni correction (P<0.0125). On performing stratified analysis, significant correlations with various clinicopathological as well as environmental and lifestyle characteristics were observed. It was evident that rs1219648 and rs2981578 interacted with exogenous hormone use and advanced clinical stage III (after Bonferroni correction, P<0.000694), respectively. Furthermore, combined analysis on these four loci revealed that compared to women with 0–1 risk loci, those with 2–4 risk loci had increased risk (OR = 1.645, 95%CI = 1.152–2.347, P = 0.006). In haplotype analysis, for rs2981578, rs2981582 and rs1219648, risk haplotype (GTG) was associated with a significantly increased risk compared to the common (ACA) haplotype (OR = 1.365, 95% CI = 1.086–1.717, P = 0.008). Our results suggest that intron 2 SNPs of FGFR2 may contribute to genetic susceptibility of breast cancer in North India population.
Breast cancer patients' language use across four stages
<p>The current study reported a language analysis of breast cancer patients' posts in an online support group. Adopting web-scrapping techniques, the study analyzed 27,078 online posts made by 1,443 users along multiple linguistic dimensions to glance into the trajectory of the patients' psychosocial adaptation of the disease. The findings suggested that breast cancer patients' emotional experiences and adjustment in the course of illness vary from one stage to another. They reached the peak of emotional expression, struggle and despair, and self-focus at Stage III, whereas wiped out negative emotions and signaled a desire for connections with others at Stage IV.</p>
Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification
<p>We uploaded the dataset of included patients of manuscript: Petrillo A, Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rosaria Rubulotta M, Setola SV, Mattace Raso M, Maio F, Raiano C, Siani C, Di Bonito M, Botti G. Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification. Breast J. 2020 May;26(5):860-872. doi: 10.1111/tbj.13739. Epub 2019 Dec 30. PMID: 31886607.</p>
Effects of the Hypnotic Alkylphenol Derivative Propofol on Breast Cancer Progression. A Focus on Preclinical and Clinical Studies.
<p>Propofol is a hypnotic alkylphenol derivative with many biological activities. It is predominantly used in anesthesia and is the most used parenteral anesthetic agent in the United States. Accumulating preclinical studies have shown that this compound may inhibit cancer recurrence and metastasis. Nevertheless, other investigations provided evidence that this compound may promote breast cancer cell progression by modulating different molecular pathways. Clinical data on this topic are scarce and derive from retrospective analyses. For this reason, we reviewed and evaluated the available data to reveal insight into this controversial issue. More preclinical and clinical investigations are necessary to determine the potential role of propofol in the proliferation of breast cancer cells.</p>
Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination
<p>We uploaded the daset releatet t the manuscript: Fusco R, Granata V, Pariante P, Cerciello V, Siani C, Di Bonito M, Valentino M, Sansone M, Botti G, Petrillo A. Blood oxygenation level dependent magnetic resonance imaging and diffusion weighted MRI imaging for benign and malignant breast cancer discrimination. Magn Reson Imaging. 2021 Jan;75:51-59. doi: 10.1016/j.mri.2020.10.008. Epub 2020 Oct 17. PMID: 33080334.</p>
Boesenbergia pandurata as Anti-breast Cancer: Molecular Docking and ADMET Study
<p><em>Boesenbergia pandurata</em> or fingerroot is known to have various pharmacological activities, including anticancer. Extracts from these plants are known to inhibit the growth of cancer cells, including breast cancer. Anti-breast cancer activity is significantly influenced by the inhibition of two receptors: ER-α and HER2. However, it is unknown which metabolites of <em>B. pandurata</em> play the most crucial role in their anticancer activity. This study aimed to determine the metabolites of <em>B. pandurata</em> with the best potential as ER-α and HER2 inhibitors. The method used was molecular docking of several <em>B. pandurata</em> metabolites against ER-α and HER2 receptors, followed by an ADMET study of several metabolites with the best docking results. The docking results showed eight metabolites with the best docking results for the two receptors based on the docking score and ligand-receptor interactions. Of these eight compounds, compounds <strong>11</strong> ((2S)-7,8-dihydro-5-hydroxy-2-methyl-2-(4''-methyl-3''-pentenyl)-8-phenyl-2H,6H-benzo(1,2-b-5,4-b')dipyran-6-one) and <strong>34</strong> (geranyl-2,4-dihydroxy-6-phenethylbenzoate) showed the potential to inhibit both receptors. Both ADMET profiles also show mixed results but still allow for further development. In conclusion, the metabolites of <em>B. pandurata</em> especially compounds <strong>11</strong> and <strong>34</strong>, can be developed as anti-breast cancer through the inhibition of ER-α and HER2.</p>
University of Chicago Breast Cancer Recurrence Score Dataset
<p>Extracted tiles from slide images used for validation of our deep learning recurrence score model.</p> <p>Please refer to the <a href="https://github.com/fmhoward/DLRS">github project page</a> for instructions on use.</p> <p>The UCH_BRCA_RS zip file contains the image tiles used for model validation. The 'tfrecords' element in the 'UCH_BRCA_RS' entry in the dataset.json file should be updated to reflect the location where this folder is extracted.</p> <p>The ROI zip file contains the tumor region annotations used for tumor likelihood model training. The TCGA BRCA project 'roi' entries in the dataset.json file should be updated to point to these subfolders.</p> <p>The PROJECTS zip file contains trained models used for the published analysis of this work. This should be extracted into the PROJECTS directed included as part of the github repository. </p>
Underlying data for Association between Serum Insulin and Interleukin-6 levels with Breast Cancer in Post-Menopausal Iraqi Women
<p>Underlying data for Association between Serum Insulin and Interleukin-6 levels with Breast Cancer in Post-Menopausal Iraqi Women</p>
Molecular subtypes and dietary patterns in breast cancer patients: a latent class analysis
<p>Molecular subtypes and dietary patterns in breast cancer patients: a latent class analysis</p>
Multiplex imaging of breast cancer lymph node metastases identifies prognostic single-cell populations independent of clinical classifiers
<p>This repository contains the continuation of dataset 10.5281/zenodo.7494413 and 10.5281/zenodo.7494509.</p> <p>The file tiff_stacks_masks.zip contains the IMC image stacks and single-cell masks as tiff files.</p> <p>The IHC_TMAs.zip contains the scans of the IHC stains of ZTMA25 and the QuPATH projects used to extract the single-cell data (incl. the single-cell measurements as csv files).</p>
Data for: Link between glucose metabolism and EMT drives triple negative breast cancer migratory heterogeneity
<p>Intracellular and environmental cues result in heterogeneous cancer cell populations with different metabolic and migratory behaviors. While glucose metabolism and EMT have previously been linked, we aim to understand how this relationship fuels cancer cell migration. We show that while glycolysis drives single-cell migration in confining microtracks, fast and slow cells display different migratory sensitivities to glycolysis and oxidative phosphorylation inhibition. Phenotypic sorting of highly and weakly migratory subpopulations (MDA<sup>+</sup>, MDA<sup>-</sup>) reveals that more mesenchymal, highly migratory MDA<sup>+</sup> preferentially use glycolysis while more epithelial, weakly migratory MDA<sup>-</sup> utilize mitochondrial respiration. These phenotypes are plastic and MDA<sup>+</sup> can be made less glycolytic, mesenchymal, and migratory and MDA<sup>-</sup> can be made more glycolytic, mesenchymal, and migratory via modulation of glucose metabolism or EMT. These findings reveal an intrinsic link between EMT and glucose metabolism that controls migration. Identifying mechanisms fueling phenotypic heterogeneity is essential to develop targeted metastatic therapeutics.</p>
Assessing Tumor-Infiltrating Lymphocytes in Breast Cancer: A Proposal for Combining Immunohistochemistry and Gene Expression Analysis to Refine Scoring
<p>Whole tissue scans of histochemistry (H&E) and immunohistochemistry (CD3, CD4, CD8 andCD45) images that have been used to calculate TIL scores.</p> <p>All stainings are numbers for each patient..</p>
Breast MRI molecular cancer subtype
<p>This data set is part of the public development data for the <a href="http://auc23.grand-challenge.org/">2023 Automated Universal Classification Challenge</a> (AUC23). The data set concerns the classification of breast cancer molecular subtypes on dynamic contrast-enhanced magnetic resonance imaging (MRI) and was derived from <a href="https://sites.duke.edu/mazurowski/resources/breast-cancer-mri-dataset/">Duke Hospital</a>. The data set is a subset of the data originally introduced and described by <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6134102/">Saha et al. (2018)</a>, with no additional images or patient information. Data was restructured in compliance with the <a href="https://auc23.grand-challenge.org/">AUC23</a> challenge format. The dataset is a single-institutional, retrospective collection of 737 biopsy-confirmed patients from 1 January 2000 to 23 March 2014 with invasive breast cancer and available pre-operative MRI at Duke Hospital.</p> <p>Images are 3D tensors:</p> <ul> <li>0: 3D T1-subtraction dynamic contrast-enhanced MRI</li> </ul> <p>Classification labels:</p> <ul> <li>0: Luminal A, estrogen-receptor (ER) and/or progesterone-receptor (PR) positive<strong>,</strong> human epidermal growth factor receptor 2 (HER2) negative</li> <li>1: Luminal B, ER and/or PR negative, HER2 positive</li> <li>2: HER2, ER and PR negative, HER2 positive</li> <li>3: Triple negative, ER, PR, and HER2 negative</li> </ul> <p>Folder structure:</p> <p>imagesTr (root folder with all patients and studies)<br> ├── Breast_MRI_0001_0000.mha (3D T1-subtraction MRI imaging for study 0001)<br> ├── Breast_MRI_0003_0000.mha (3D T1-subtraction MRI imaging for study 0003)<br> ├── ...</p> <p>Please cite the following article if you are using the <a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226903">Duke-Breast-Cancer-MRI Dataset</a>:</p> <pre><code>A. Saha, M. R. Harowicz, L. J. Grimm, C. E. Kim, S. V. Ghate, R. Walsh, M. A. Mazurowski, "A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features". Br J Cancer. 2018 Aug;119(4):508-516. doi: 10.1038/s41416-018-0185-8. Epub 2018 Jul 23. PMID: 30033447; PMCID: PMC6134102. </code></pre>
Custom R scripts and input files for manuscript "Evolutionary histories of breast cancer and related clones"
<p>Supporting files for manuscript <em>"Evolutionary histories of breast cancer and related clones"</em>.</p> <p>Contents:</p> <ul> <li>Custom R script and an input file (*.txt) to evaluate clonal structure of single-cell derived organoids using the Gaussian mixture models</li> <li>Custom R script and an input file (*.txt) to estimate mutation rate in normal organoids using the linear regression models </li> <li>Custom R script and input files (*.txt) to generate mutation matrices for phylogenetic analysis using MEGA and treemut </li> <li>Custom R scripts and an input file (*.txt) to estimate the timing of 1q gain, 1q gain doubling, and MRCA emergence</li> </ul>
Methylene Blue Sentinel Lymph Node Biopsy for Breast Cancer Learning Curve in Covid-19 era: How many cases are enough?
<p>Methylene Blue Sentinel Lymph Node Biopsy for Breast Cancer Learning Curve in Covid-19 era: How many cases are enough?</p>
Spiritual well-being and breast cancer
<p>Dataset.</p>
Breast cancer IITH project
<p>The dataset was curated for investigating imaging intratumor heterogeneity (IITH). In this setting, radiomics features from baseline dynamic contrast-enhanced MRI (DCE-MRI) of 711 breast cancer patients were extracted and clinical data was provided.</p>
Dose-Dense Docetaxel Before or After Doxorubicin/Cyclophosphamide in Axillary Node-Positive Breast Cancer
ClinicalTrials.gov study NCT00201708. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Pyrotinib Combined With Brain Radiotherapy in Breast Cancer Patients With Brain Metastases
ClinicalTrials.gov study NCT04582968. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Gemcitabine Plus Cisplatin Versus Gemcitabine Plus Paclitaxel in Triple Negative Breast Cancer (TNBC)
ClinicalTrials.gov study NCT01287624. IPD Sharing: Not stated. Countries: 1. Publications: 2.
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.