Publications by authors named "E Colon"

The ability to observe and interpret images and clinical information is essential for veterinarians in clinical practice. The purpose of this study is to determine the utility of a novel teaching method in veterinary medicine, the incorporation of art interpretation using the Visual Thinking Strategies (VTS), on students' observational and clinical interpretation skills when evaluating radiographs and patient charts. Students were asked to observe and interpret a set of radiographs and a patient chart, subsequently involved in art interpretation using VTS, and then asked to observe and interpret a different set of radiographs and a different patient chart.

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Purpose: HER2-low has garnered significant attention for the treatment of HER2-negative breast cancer. We aimed to determine the prevalence of HER2-low expression in Hispanic/Latino women with breast cancer (BC).

Methods: We searched in Embase, LILACS, and Medline databases for articles reporting the expression of HER2 immunohistochemistry with scores reported as 0, 1+, 2+, or 3+, with equivocal cases (2+) confirmed through in situ hybridization (ISH).

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Viruses are key drivers of microbial diversity, nutrient cycling, and co-evolution in ecosystems, yet their study is hindered due to challenges in culturing. Traditional gene-centric methods, which focus on a few hallmark genes like for capsids, miss much of the viral genome, leaving key viral proteins and functions undiscovered. Here, we introduce two powerful annotation-free metrics, V-score and V-score, designed to quantify the "virus-likeness" of protein families and genomes and create an open-access searchable database, 'V-Score-Search'.

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Aging is associated with increased breast cancer risk and outcomes are worse for the oldest and youngest patients, regardless of subtype. It is not known how cells in the breast tumor microenvironment are impacted by age and how they might contribute to age-related disease pathology. Here, we discover age-associated differences in cell states and interactions in human estrogen receptor-positive (ER+) and triple-negative breast cancers (TNBC) using new computational analyses of existing single-cell gene expression data.

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Article Synopsis
  • The study aimed to assess the accuracy of a fully automated AI solution for interpreting HER2 immunohistochemistry (IHC) in breast cancer, acknowledging the need for more reliable HER2 scoring due to the effectiveness of HER2-targeted therapies.
  • In a two-arm study involving 120 HER2 IHC whole-slide images and four surgical pathologists, the AI solution showed improved interobserver agreement and scoring accuracy compared to manual readings, particularly for distinguishing between HER2 0 and 1+ cases.
  • The results indicate that the AI tool can enhance the consistency and reproducibility of HER2 scoring, supporting pathologists in following ASCO/CAP guidelines more effectively.
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