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Image_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.TIF
Published 2020“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”
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Image_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.TIF
Published 2020“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”
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Table_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.xls
Published 2020“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”
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Data_Sheet_1_The Proximal Drivers of Large Fires: A Pyrogeographic Study.docx
Published 2020“…As in most fire-prone environments, the majority of annual burned area is due to a relatively small number of large fires. We train and test an Artificial Neural Network’s ability to predict spatial patterns in the probability of large fires (>1,250 ha) in forests and grasslands as a function of proxies of the four major controls on fire activity. …”
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Data_Sheet_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.docx
Published 2020“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”
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Data_Sheet_1_Matrix Metallopeptidase 14: A Candidate Prognostic Biomarker for Diffuse Large B-Cell Lymphoma.docx
Published 2020“…However, the correlation between MMP14 expression, prognosis, and immune cell infiltration in diffuse large B-cell lymphoma (DLBCL) remains unclear.</p>Methods<p>We investigated the influence of MMP14 on clinical prognosis using data obtained from three Gene Expression Omnibus (GEO) database sets (GSE98588, GSE10846, and GSE4475). …”
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Supplementary Material for: Effects of White Matter Hyperintensities on 90-Day Functional Outcome after Large Vessel and Non-Large Vessel Stroke
Published 2020“…<b><i>Material and Methods:</i></b> We reviewed acute ischemic stroke patients admitted between 2009 and 2017 at a large healthcare system in the USA. …”
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Table_1_Mutational Profile and Clonal Evolution of Relapsed/Refractory Diffuse Large B-Cell Lymphoma.xlsx
Published 2021“…The genes most frequently altered (> 20%) were (in decreasing order of frequency) CDKN2A, PIM1, CD79B, TP53, MYD88, MYC, BTG2, BTG1, CDKN2B, DTX1, CD58, ETV6, and IRF4. …”
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Triple B←N Lewis Pair-Functionalized Triazatruxenes with Large Stokes Shifts
Published 2023Subjects: -
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<b>Supporting data for manuscript</b> "<b>Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins</b>"
Published 2025“…<p dir="ltr">The CSV file 'Eyreetal_DrainingVein_SourceData' contains the averaged time series traces and extracted metrics from individual experiments used across Figures 1-5 in the manuscript "Voluntary locomotion induces an early and remote hemodynamic decrease in the large cerebral veins". …”
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DataSheet_1_Mutational Profile and Clonal Evolution of Relapsed/Refractory Diffuse Large B-Cell Lymphoma.xlsx
Published 2021“…The genes most frequently altered (> 20%) were (in decreasing order of frequency) CDKN2A, PIM1, CD79B, TP53, MYD88, MYC, BTG2, BTG1, CDKN2B, DTX1, CD58, ETV6, and IRF4. …”
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