Ratna Chakrabarti

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BACKGROUND LIM kinase 1 (LIMK1) is an actin and microtubule cytoskeleton modulatory protein that is overexpressed in a number of cancerous tissues and cells and also promotes invasion and metastasis of prostate and breast cancer cells. Membrane type matrix metalloproteinase 1 (MT1-MMP) is a critical modulator of extracellular matrix (ECM) turnover through(More)
BACKGROUND LIM kinase 1 (LIMK1), a LIM domain containing serine/threonine kinase, modulates actin dynamics through inactivation of the actin depolymerizing protein cofilin. Recent studies have indicated an important role of LIMK1 in growth and invasion of prostate and breast cancer cells; however, the molecular mechanism whereby LIMK1 induces tumor(More)
Development of resistance to androgen deprivation therapy (ADT) is a major obstacle for the management of advanced prostate cancer. Therapies with androgen receptor (AR) antagonists and androgen withdrawal initially regress tumors but development of compensatory mechanisms including AR bypass signaling leads to re-growth of tumors. MicroRNAs (miRNAs) are(More)
Optimization of an optical coherence imaging (OCI) system on the basis of task performance is a challenging undertaking. We present a mathematical framework based on task performance that uses statistical decision theory for the optimization and assessment of such a system. Specifically, we apply the framework to a relatively simple OCI system combined with(More)
Polymeric micro- and nanoparticles are becoming a mainstay in biomedicine, medical diagnostics, and therapeutics, where they are used in implementing sensing mechanisms, as imaging contrast agents, and in drug delivery. Current approaches to the fabrication of such particles are typically finely tuned to specific monomer or polymer species, size ranges, and(More)
To determine if candidate cancer biomarkers have utility in a clinical setting, validation using immunohistochemical methods is typically done. Most analyses of such data have not incorporated the multivariate nature of the staining profiles. In this article, we consider modelling such data using recently developed ideas from the machine learning community.(More)
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