http://bura.brunel.ac.uk/handle/2438/25437 Tue, 14 Apr 2026 06:39:21 GMT 2026-04-14T06:39:21Z http://bura.brunel.ac.uk/handle/2438/29863 Title: Pushing the envelope – How the genome interacts with the nuclear envelope in health and disease Authors: Perira Torres, R; Samarakone, C; Bridger, J; de Castro, I Editors: Donev, R Abstract: The nuclear envelope has for long been considered more than just the physical border between the nucleoplasm and the cytoplasm, emerging as a crucial player in genome organisation and regulation within the 3D nucleus. Consequently, its study has become a valuable topic in the research of cancer, ageing and several other diseases where chromatin organisation is compromised. In this chapter, we will delve into its several sub-elements, such as the nuclear lamina, nuclear pore complexes and nuclear envelope proteins, and their diverse roles in nuclear function and maintenance. We will explore their functions beyond nuclear structure and transport focusing on their interactions with chromatin and their paramount influence in its organisation, regulation and expression at the nuclear periphery. Finally, we will outline how this chromatin organisation and regulation at the nuclear envelope is affected in diseases, including laminopathies, cancer, neurodegenerative diseases and during viral infections. Fri, 04 Oct 2024 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/29863 2024-10-04T00:00:00Z http://bura.brunel.ac.uk/handle/2438/27598 Title: MesoGraph: Automatic profiling of mesothelioma subtypes from histological images Authors: Eastwood, M; Sailem, H; Marc, ST; Gao, X; Offman, J; Karteris, E; Fernandez, AM; Jonigk, D; Cookson, W; Moffatt, M; Popat, S; Minhas, F; Robertus, JL Abstract: Copyright © 2023 The Authors. Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score. Description: Data and code availability: • Tissue Micro-array cores and labels for the primary cohort are linked in the github repository at: https://github.com/measty/MesoGraph The Mesobank data is available from Mesobank (https://www.mesobank.com/) on request. This would require the completion of mesobank’s standard application form. It would then be reviewed to make sure that the proposed use of the data is covered by mesobank’s generic ethical approval, and a suitable Data Sharing Agreement would need to be in place before any data is released. • All original code is publicly available at: https://github.com/measty/MesoGraph. • Any additional data is available from the lead contact on request.; Supplemental information is available online at: https://www.sciencedirect.com/science/article/pii/S2666379123004032#appsec2 . Mon, 09 Oct 2023 00:00:00 GMT http://bura.brunel.ac.uk/handle/2438/27598 2023-10-09T00:00:00Z