Mouse Anti Human huntingtin Hybridoma [NX4]

In recent years, computer vision techniques have made large advances in image recognition and been applied to aid radiological diagnosis. Computational pathology aims to develop similar tools for aiding pathologists in diagnosing digitized histopathological slides, which would improve diagnostic accuracy and productivity amidst increasing workloads. However, there is a lack of publicly-available databases of (1) localized patch-level images annotated with (2) a large range of Histological Tissue Type (HTT). As a result, computational pathology research is constrained to diagnosing specific diseases or classifying tissues from specific organs, and cannot be readily generalized to handle unexpected diseases and organs. In this paper, we propose a new digital pathology database, the "Atlas of Digital Pathology" (or ADP), which comprises of 17,668 patch images extracted from 100 slides annotated with up to 57 hierarchical HTTs. Our data is generalized to different tissue types across different organs and aims to provide training data for supervised multi-label learning of patch-level H17 in a digitized whole slide image. We demonstrate the quality of our image labels through pathologist consultation and by training three state-of-theart neural networks on tissue type classification. Quantitative results support the visually consistency of our data and we demonstrate a tissue type-based visual attention aid as a sample tool that could be developed from our database.

Background: Huntington's disease (HD) is a progressive neurodegenerative disorder that prominently affects the basal ganglia, leading to affective, cognitive, behavioral, and motor decline. The primary site of neuron loss in HD is the striatal part of the basal ganglia, with GABAergic medium size spiny neurons (MSNs) being nearly completely lost in advanced HD. Objective: Based on the hypothesis that mutant huntingtin (mHTT) protein injures neurons via transcriptional dysregulation, we set out to establish a transcriptional profile of HD disease progression in the well characterized transgenic mouse model, R6/2, and two Knock-in models (KI); zQ175KI (expressing mutant mouse/human chimeric Htt protein) and HdhQ200 HET KI (carrying one allele of expanded mouse CAG repeats). Methods: In this study, we used quantitative PCR (qPCR) to evaluate striatal mRNA levels of markers of neurotransmission, neuroinflammation, and energy metabolism. Results: After analyzing and comparing transcripts from pre-symptomatic and symptomatic stages, markers expressed in the basal ganglia MSNs, which are typically involved in maintaining normal neurotransmission, showed a genotype-specific decrease in mRNA expression in a pattern consistent with human studies. In contrast, transcripts associated with neuroinflammation and energy metabolism were mostly unaffected in these animal models of HD. Conclusion: Our results show that transcripts linked to neurotransmission are significantly reduced and are consistent with disease progression in both zQ175KI and R6/2 transgenic mouse models.