69 research outputs found

    Inhibitory Activity of YKL-40 in Mammary Epithelial Cell Differentiation and Polarization Induced by Lactogenic Hormones: A Role in Mammary Tissue Involution

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    We previously reported that a secreted glycoprotein YKL-40 acts as an angiogenic factor to promote breast cancer angiogenesis. However, its functional role in normal mammary gland development is poorly understood. Here we investigated its biophysiological activity in mammary epithelial development and mammary tissue morphogenesis. YKL-40 was expressed exclusively by ductal epithelial cells of parous and non-parous mammary tissue, but was dramatically up-regulated at the beginning of involution. To mimic ductal development and explore activity of elevated YKL-40 during mammary tissue regression in vivo, we grew a mammary epithelial cell line 76N MECs in a 3-D Matrigel system in the presence of lactogenic hormones including prolactin, hydrocortisone, and insulin. Treatment of 76N MECs with recombinant YKL-40 significantly inhibited acinar formation, luminal polarization, and secretion. YKL-40 also suppressed expression of E-cadherin but increased MMP-9 and cell motility, the crucial mechanisms that mediate mammary tissue remodeling during involution. In addition, engineering of 76N MECs with YKL-40 gene to express ectopic YKL-40 recapitulated the same activities as recombinant YKL-40 in the inhibition of cell differentiation. These results suggest that YKL-40-mediated inhibition of cell differentiation and polarization in the presence of lactogenic hormones may represent its important function during mammary tissue involution. Identification of this biophysiological property will enhance our understanding of its pathologic role in the later stage of breast cancer that is developed from poorly differentiated and highly invasive cells

    Responsible AI Considerations in Text Summarization Research: A Review of Current Practices

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    AI and NLP publication venues have increasingly encouraged researchers to reflect on possible ethical considerations, adverse impacts, and other responsible AI issues their work might engender. However, for specific NLP tasks our understanding of how prevalent such issues are, or when and why these issues are likely to arise, remains limited. Focusing on text summarization -- a common NLP task largely overlooked by the responsible AI community -- we examine research and reporting practices in the current literature. We conduct a multi-round qualitative analysis of 333 summarization papers from the ACL Anthology published between 2020-2022. We focus on how, which, and when responsible AI issues are covered, which relevant stakeholders are considered, and mismatches between stated and realized research goals. We also discuss current evaluation practices and consider how authors discuss the limitations of both prior work and their own work. Overall, we find that relatively few papers engage with possible stakeholders or contexts of use, which limits their consideration of potential downstream adverse impacts or other responsible AI issues. Based on our findings, we make recommendations on concrete practices and research directions
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