Research involving estrogen and progesterone receptors (ER and PR) have greatly contributed to our understanding of cell signaling and transcriptional regulation. In addition to the classical ER and PR nuclear actions, new signaling pathways have recently been identified due to ER and PR association with cell membranes and signal transduction proteins. Bio-informatics has unveiled how ER and PR recognize their ligands, selective modulators and co-factors, which has helped to implement them as key targets in the treatment of benign and malignant tumors. Knowledge regarding ER and PR is vast and complex; therefore, this review will focus on their isoforms, signaling pathways, co-activators and co-repressors, which lead to target gene regulation. Moreover it will highlight ER and PR involvement in benign and malignant diseases as well as pharmacological substances influencing cell signaling and provide established and new structural insights into the mechanism of activation and inhibition of these receptors.
Background The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students’ attitudes to online learning to provide a broad scientific basis to guide future development of medical education. Methods Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education. Results The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching. Conclusion This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new “online era” in medical education.
Breast cancer is one of the leading causes of mortality in women. Early detection and treatment are imperative for improving survival rates, which have steadily increased in recent years as a result of more sophisticated computer-aided-diagnosis (CAD) systems. A critical component of breast cancer diagnosis relies on histopathology, a laborious and highly subjective process. Consequently, CAD systems are essential to reduce inter-rater variability and supplement the analyses conducted by specialists. In this paper, a transfer-learning based approach is proposed, for the task of breast histology image classification into four tissue sub-types, namely, normal, benign, in situ carcinoma and invasive carcinoma. The histology images, provided as part of the BACH 2018 grand challenge, were first normalized to correct for color variations resulting from inconsistencies during slide preparation. Subsequently, image patches were extracted and used to fine-tune Google's Inception-V3 and ResNet50 convolutional neural networks (CNNs), both pre-trained on the ImageNet database, enabling them to learn domain-specific features, necessary to classify the histology images. The ResNet50 network (based on residual learning) achieved a test classification accuracy of 97.50% for four classes, outperforming the Inception-V3 network which achieved an accuracy of 91.25%.
Tamoxifen is an important selective estrogen receptor (ER) modulator for treatment of steroid hormone positive breast cancer. In addition to the beneficial effect, tamoxifen is one risk factor for endometrial carcinoma (EnCa) development. We hypothesized that, (1) dysregulation of gene expression and protein phosphorylation of the insulin-like growth factor (IGF) and steroid hormone receptor-signaling occur early in benign endometrial tissues and (2) signaling differences would be detected between patients with or without tamoxifen treatment. Seventy-eight tissues, including 2 benign cohorts from patients treated with (n 5 24) or without tamoxifen (n 5 28) (hyperproliferative endometrium, hyperplasia, polyps), EnCa (n 5 12) with endometrium controls (n 5 14) were analyzed for expression of 15 genes from the IGF and steroid hormone receptor-signaling, including the target genes Syncytin-1, PAX2 and c-myc. Total and phosphorylated protein expression were examined for ERa, PTEN, AKT, mTOR and Syncytin-1. Compared to controls similar significant deregulation of IGF and steroid hormone receptor-signaling, Syncytin-1 and PAX2 occurred in both benign cohorts, irrelevant of tamoxifen treatment. Comparing both benign cohorts with and without tamoxifen significant expression differences were noted. Increased total protein and phosphorylation of pERaSer118, pPTEN-Thr380, pAKT-Thr308, pAKT-Ser473, pmTORSer2448 and Syncytin-1 were noted in early benign tissue stages associating with tamoxifen, especially polyps. Functional kinetic studies following tamoxifen treatment of the PTEN mutated RL95-2 EnCa cell line, demonstrated a doubling of phosphorylation of pERa-Ser118 and a 4.2-fold induction of pAKT-Thr308 along with Syncytin-1 induction. This study supports that dysregulated IGF and steroid hormone receptor signaling is prominent in endometrial benign stages and these alterations could represent clinical indicators for the risk of EnCa and also help in development of new therapies. ' 2008 Wiley-Liss, Inc.Key words: endometrial carcinoma; tamoxifen; IGF; Syncytin-1; PAX2 Endometrial carcinoma (EnCa) primarily occurs in postmenopausal women and represents the 7th most common malignant disorder. 1 The estimated incidence and mortality rate for EnCa was recently reported at 33.5 and 9.3 per 100,000 in Europe, respectively.2 The mean age-adjusted 5-year survival for EnCa in Europe was also calculated at 78%, 1 percent below the female breast cancer rate at 79%.3 According to the International Federation of Gynaecology and Obstetrics, EnCa is staged according to tumor location (Ia is endometrial to IVb with distant metastasis) and graded according to the growth pattern (G1-3). More than 85% of all EnCa cases are histologically classified as endometrioid (type I), mainly expressing steroid hormone receptors. The rare, but more aggressive nonendometrioid EnCa (type II) often lacks ER and PR expression and may develop directly from transformed endometrial surface epithelium. 4 Estrogens are considered to act as tumor promoters in th...
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