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Background: Breast cancer is considered to be 2nd most common cancer subtype investigated worldwide. It is mainly prevalent in postmenopausal women. Estrogen Receptor (ER) is a primary transcription factor for survival and growth of tumors. Around 80% BCs of all classes are ER-positive (ER+). Powerful evidence for estrogen proved to be involved in BC pathogenesis both exogenously and endogenously. It brings the concept of ER inhibitors to treat BC with distinct mechanisms into focus and ER PROTACs (Proteolysis-Targeting Chimeras), AIs (Aromatase inhibitors), SERMs (Selective estrogen receptor modulators), and SERDs (Selective estrogen receptor degrader) were developed. For over 30 years, Tamoxifen, a triphenylethylene SERM, was the drug of choice solely to treat ER+BC patients. Although several SERMs got approval by US FDA after tamoxifen, complicacies remain because of dangerous adverse effects like endometrial carcinoma, hot flashes, and VTE (Venous thromboembolism). In addition to that drug-resistant tumors put a surging need for novel, potent candidates with no or low adverse effects for ER+ BC prevention. Objectives: This article explores the possibilities of SERMs as effective BC agents. Methods: A detailed literature survey of the history and recent advancements of SERMs has been carried out, taking BC as the primary target. This review provides information about ER structure, signaling, pharmacological action, chemical classification with SAR analysis, benefits and adverse effect of SERMs as potential BC agents. Results: Exhaustive literature studies suggested that SERMs having an agonistic, antagonistic or mixed activity to ER could efficiently inhibit BC cell proliferation. Conclusion: Each chemical class of SERMs comprises of some salient features and potentials, which may be further investigated to obtain novel effective SERMs in BC therapy.

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Clinical documents are a repository of information about patients' conditions. However, this wealth of data is not properly tapped by the existing analysis tools. Dengue is one of the most widespread water borne diseases known today. Every year, dengue has been threatening lives the world over. Systems already developed have concentrated on extracting disorder mentions using dictionary look-up, or supervised learning methods. This project aims at performing Named Entity Recognition to extract disorder mentions, time expressions and other relevant features from clinical data. These can be used to build a model, which can in turn be used to predict the presence or absence of the disease, dengue. Further, we perform a frequency analysis which correlates the occurrence of dengue and the manifestation of its symptoms over the months. The system produces appreciable accuracy and serves as a valuable tool for medical experts.