Tetrabromobisphenol A (TBBPA) is a brominated flame retardant that induces endometrial adenocarcinoma and other uterine tumors in Wistar Han rats; however, early molecular events or biomarkers of TBBPA exposure remain...
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Tetrabromobisphenol A (TBBPA) is a brominated flame retardant that induces endometrial adenocarcinoma and other uterine tumors in Wistar Han rats; however, early molecular events or biomarkers of TBBPA exposure remain unknown. We investigated the effects of TBBPA on growth factor receptor activation (phospho-RTK) in uteri of rats following early-life exposures. Pregnant Wistar Han rats were exposed to TBBPA (0, 0.1, 25, 250 mg/kg/day) via oral gavage on gestation day 6 through weaning of pups (PND 21). Pups were exposed through lactation, and by daily gavage from PND 22 to PND 90. Uterine horns were collected (at PND 21, PND 33, PND 90) and formalin-fixed or frozen for histologic, immunohistochemical, phospho-RTK arrays, or western blot analysis. At PND 21, the phosphor-RTKs, FGFR2, FGFR3, TRKC and EPHA1 were significantly increased at different treatment concentrations. Several phospho-RTKs were also significantly overexpressed at PND 33 which included epithelial growth factor receptor (EGFR), Fibroblast Growth Factor Receptor 3-4 (FGFR2, FGFR3, FGFR4), insulin-like growth factor receptor 1 (IGF1R), INSR, AXL, MERTK, PDGFRa and b, RET, Tyrosine Kinase with Immunoglobulin Like and EGF Like Domains 1 and 2 (TIE1; TIE2), TRKA, VEGFR2 and 3, and EPHA1 at different dose treatments. EGFR, an RTK overexpressed in endometrial cancer in women, remained significantly increased for all treatment groups at PND 90. Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2) and IGF1R were overexpressed at PND 33 and remained increased through PND 90, although ERBB2 was statistically significant at PND 90. The phospho-RTKs, FGFR3, AXL, DTK, HGFR, TRKC, VEGFR1 and EPHB2 and 4 were also statistically significant at PND 90 at different dose treatments. The downstream effector, phospho-MAPK44/42 was also increased in uteri of treated rats. Our findings show RTKs are dysregulated following early life TBBPA exposures and their sustained activation may contribute to TBBPA-induced uterine tumors observe
The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts...
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The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features
The well-known Mori-Zwanzig theory tells us that model reduction leads to memory effect. For a long time, modeling the memory effect accurately and efficiently has been an important but nearly impossible task in devel...
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We introduce a general framework for constructing coarse-grained potential models without ad hoc approximations such as limiting the potential to two- A nd/or three-body contributions. The scheme, called Deep Coarse-G...
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An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular...
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