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Hagan, M.

Publications and source records attributed to Hagan, M..

2 recordsLinked to original sources

Non-viral vasculogenic reprogramming restores cognition and mitigates pathology in Alzheimer's disease

Alzheimers Disease (AD) is characterized by progressive cognitive decline associated with amyloid-beta (A{beta}) plaques, neurofibrillaiy tangles, inflammation, synaptic loss, and profuse neuronal death. Accumulating evidence demonstrates that cerebrovascular impairment precedes the emergence of neuropathological hallmarks, implicating vascular dysfunction as an early contributor to AD onset and progression. We investigated a non-viral strategy to generate pro-vasculogenic fibroblasts by transiently overexpressing Et{upsilon}2, Foxc2, and Flii (EFF) as a potential cell-based therapy for neurovascular deficits in AD. To assess therapeutic potential, FFF-primc[d] fibroblasts were injected into a mouse model of AD (3xTg-AD) and wild-type controls via the intracerebroventricular (ICV) route, followed by cognitive assessments and subsequent brain tissue analyses. Our findings demonstrate that FFF-primed fibroblasts acquire vasculogenic properties, enhance cerebral blood flow (CBF), and alleviate spatial memory deficits in 3xTg-AD mice. Moreover, transplanted FFF-primed fibroblasts exhibited long-term survival, integrated into the brain vasculature, and promoted cortical vascular remodeling in the AD brain. Notably, ICV deployment of these cells is also correlated with reduced cortical amyloid-beta load, suggesting potential therapeutic benefits in reducing AD pathology. Transcriptomic analysis identified the activation of genes involved in fatty acid oxidation, such as Ppar, known for its anti-amyloidogenic and anti-inflammatory effects. Collectively, these findings highlight non- viral, reprogramming-based vasculogenic cell therapy as a promising strategy for Alzheimers disease, capable of alleviating cognitive decline and addressing AD pathology across cellular and tissue scales.

neuroscience↗

HistoClean: Open-source Software for Histological Image Pre-processing and Augmentation to Improve Development of Robust Convolutional Neural Networks

The growth of digital pathology over the past decade has opened new research pathways and insights in cancer prediction and prognosis. In particular, there has been a surge in deep learning and computer vision techniques to analyse digital images. Common practice in this area is to use image pre-processing and augmentation to prevent bias and overfitting, creating a more robust deep learning model. Herein we introduce HistoClean; user-friendly, graphical user interface that brings together multiple image processing modules into one easy to use toolkit. In this study, we utilise HistoClean to pre-process images for a simple convolutional neural network used to detect stromal maturity, improving the accuracy of the model at a tile, region of interest, and patient level. HistoClean is free and open-source and can be downloaded from the Github repository here: https://github.com/HistoCleanQUB/HistoClean.

cancer biology↗