From Deworming to Cancer Therapy: Benzimidazoles in Hematological Malignancies.

Drug repurposing is a strategy to discover new therapeutic uses for existing drugs, which have well-established toxicity profiles and are often more affordable. This approach has gained significant attention in recent years due to the high costs and low success rates associated with traditional drug development. Drug repositioning offers a more time- and cost-effective path for identifying new treatments.

DeepSSM: A blueprint for image-to-shape deep learning models.

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization.

Signaling pathways and regulation of gene expression in hematopoietic cells.

Cellular functions are regulated by signal transduction pathway networks consisting of protein-modifying enzymes that control the activity of many downstream proteins. Protein kinases and phosphatases regulate gene expression by reversible phosphorylation of transcriptional factors, which are their direct substrates. Casein kinase II (CK2) is a serine/threonine kinase that phosphorylates a large number of proteins that have critical roles in cellular proliferation, metabolism and survival.

Leveraging unsupervised image registration for discovery of landmark shape descriptor.

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation.

Uncertain-DeepSSM: From Images to Probabilistic Shape Models.

Statistical shape modeling (SSM) has recently taken advantage of advances in deep learning to alleviate the need for a time-consuming and expert-driven workflow of anatomy segmentation, shape registration, and the optimization of population-level shape representations. DeepSSM is an end-to-end deep learning approach that extracts statistical shape representation directly from unsegmented images with little manual overhead. It performs comparably with state-of-the-art shape modeling methods for estimating morphologies that are viable for subsequent downstream tasks.