Sudden Death and Left Ventricular Involvement in Arrhythmogenic Cardiomyopathy

BACKGROUND: Arrhythmogenic cardiomyopathy (ACM) is an inherited heart muscle disorder characterized by myocardial fibrofatty replacement and an increased risk of sudden cardiac death (SCD). Originally described as a right ventricular disease, ACM is increasingly recognized as a biventricular entity. We evaluated pathological, genetic, and clinical associations in a large SCD cohort. METHODS: We investigated 5205 consecutive cases of SCD referred to a national cardiac pathology center between 1994 and 2018. Hearts and tissue blocks were examined by expert cardiac pathologists. After comprehensive histological evaluation, 202 cases (4%) were diagnosed with ACM. Of these, 15 (7%) were diagnosed antemortem with dilated cardiomyopathy (n=8) or ACM (n=7). Previous symptoms, medical history, circumstances of death, and participation in competitive sport were recorded. Postmortem genetic testing was undertaken in 24 of 202 (12%). Rare genetic variants were classified according to American College of Medical Genetics and Genomics criteria. RESULTS: Of 202 ACM decedents (35.4±13.2 years; 82% male), no previous cardiac symptoms were reported in 157 (78%). Forty-one decedents (41/202; 20%) had been participants in competitive sport. The adjusted odds of dying during physical exertion were higher in men than in women (odds ratio, 4.58; 95% CI, 1.54-13.68; P=0.006) and in competitive athletes in comparison with nonathletes (odds ratio, 16.62; 95% CI, 5.39-51.24; P<0.001). None of the decedents with an antemortem diagnosis of dilated cardiomyopathy fulfilled definite 2010 Task Force criteria. The macroscopic appearance of the heart was normal in 40 of 202 (20%) cases. There was left ventricular histopathologic involvement in 176 of 202 (87%). Isolated right ventricular disease was seen in 13%, isolated left ventricular disease in 17%, and biventricular involvement in 70%. Among whole hearts, the most common areas of fibrofatty infiltration were the left ventricular posterobasal (68%) and anterolateral walls (58%). Postmortem genetic testing yielded pathogenic variants in ACM-related genes in 6 of 24 (25%) decedents. CONCLUSIONS: SCD attributable to ACM affects men predominantly, most commonly occurring during exertion in athletic individuals in the absence of previous reported cardiac symptoms. Left ventricular involvement is observed in the vast majority of SCD cases diagnosed with ACM at autopsy. Current Task Force criteria may fail to diagnose biventricular ACM before death

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

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

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

Protein expression of transmembrane protease serine 4 in the gastrointestinal tract and in healthy, cancer, and SARS‑CoV‑2 infected lungs

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Copyright: © Kerslake et al. In addition to the angiotensin‑converting enzyme 2 (ACE2), a number of host cell entry mediators have been identified for severe acute respiratory syndrome coronavirus‑2 (SARS‑CoV‑2), including transmembrane protease serine 4 (TMPRSS4). The authors have recently demonstrated the upregulation of TMPRSS4 in 11 different cancers, as well as its specific expression within the central nervous system using in silico tools. The present study aimed to expand the initial observations and, using immunohistochemistry, TMPRSS4 protein expression in the gastrointestinal (GI) tract and lungs was further mapped. Immunohistochemistry was performed on tissue arrays and lung tissues of patients with non‑small cell lung cancer with concurrent coronavirus disease 2019 (COVID‑19) infection using TMPRSS4 antibody. The results revealed that TMPRSS4 was abundantly expressed in the oesophagus, stomach, small intestine, jejunum, ileum, colon, liver and pancreas. Moreover, the extensive TMPRSS4 protein expression in the lungs of a deceased patient with COVID‑19 with chronic obstructive pulmonary disease and bronchial carcinoma, as well in the adjacent normal tissue, was demonstrated for the first time, at least to the best of our knowledge. On the whole, the immunohistochemistry data of the present study suggest that TMPRSS4 may be implicated in the broader (pulmonary and extra‑pulmonary) COVID‑19 symptomatology; thus, it may be responsible for the tropism of this coronavirus both in the GI tract and lungs.Cancer Treatment and Research Trust; University Hospitals Coventry and Warwickshire NHS Trust (grant no. 12899)

Histologically proven myocardial carcinoid metastases: the value of multimodality imaging

We present a case of a patient with intramyocardial metastases from a carcinoid tumor. These findings were detected using cardiovascular magnetic resonance imaging, with functional metabolic activity analyzed using nuclear imaging and confirmed by histologic findings at surgical biopsy. This case highlights the value of cardiovascular magnetic resonance imaging and the importance of multimodality imaging

COVID‑19 and SARS‑CoV‑2 host cell entry mediators: Expression profiling of TMRSS4 in health and disease

Copyright: © Katopodis et al. Severe acute respiratory syndrome (SARS) coronavirus‑2 (SARS‑CoV‑2), the causative viral agent for the ongoing COVID‑19 pandemic, enters its host cells primarily via the binding of the SARS‑CoV‑2 spike (S) proteins to the angiotensin‑converting enzyme 2 (ACE2). A number of other cell entry mediators have also been identified, including neuropilin‑1 (NRP1) and transmembrane protease serine 2 (TMPRSS2). More recently, it has been demonstrated that transmembrane protease serine 4 (TMPRSS4) along with TMPRSS2 activate the SARS‑CoV‑2 S proteins, and enhance the viral infection of human small intestinal enterocytes. To date, a systematic analysis of TMPRSS4 in health and disease is lacking. In the present study, using in silico tools, the gene expression and genetic alteration of TMPRSS4 were analysed across numerous tumours and compared to controls. The observations were also expanded to the level of the central nervous system (CNS). The findings revealed that TMPRSS4 was overexpressed in 11 types of cancer, including lung adenocarcinoma, lung squamous cell carcinoma, cervical squamous cell carcinoma, thyroid carcinoma, ovarian cancer, cancer of the rectum, pancreatic cancer, colon and stomach adenocarcinoma, uterine carcinosarcoma and uterine corpus endometrial carcinoma, whilst it was significantly downregulated in kidney carcinomas, acute myeloid leukaemia, skin cutaneous melanoma and testicular germ cell tumours. Finally, a high TMPRSS4 expression was documented in the olfactory tubercle, paraolfactory gyrus and frontal operculum, all brain regions which are associated with the sense of smell and taste. Collectively, these data suggest that TMPRSS4 may play a role in COVID‑19 symptomatology as another SARS‑CoV‑2 host cell entry mediator responsible for the tropism of this coronavirus both in the periphery and the CNS

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images

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 .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.CRUK-STFC Early Detection Innovation Award. F.M. and M.E. also acknowledge funding support from EPSRC grant EP/W02909X/1

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Copyright © 2023 The Authors. Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINSPRISM project, kindly funded by Cancer Research UK through the CRUK-STFC Early Detection Innovation Award

Vascular histopathology and connective tissue ultrastructure in spontaneous coronary artery dissection: pathophysiological and clinical implications.

AIMS: Spontaneous coronary artery dissection (SCAD) is a cause of acute coronary syndromes and in rare cases sudden cardiac death (SCD). Connective tissue abnormalities, coronary inflammation, increased coronary vasa vasorum density and coronary fibromuscular dysplasia have all been implicated in the pathophysiology of SCAD but have not previously been systematically assessed. We designed a study to investigate the coronary histological and dermal collagen ultrastructural findings in SCAD. METHODS AND RESULTS: 36 autopsy SCAD cases were compared with 359 SCAD survivors. Coronary and myocardial histology and immunohistochemistry were undertaken. Transmission electron microscopy (TEM) of dermal extracellular matrix components (ECM) of n = 31 SCAD survivors and n = 16 healthy volunteers were compared. Autopsy cases were more likely male (19% versus 5%; p = 0.0004) with greater proximal left coronary involvement (56% versus 18%; p < 0.0001) compared to SCAD survivors. N = 24 (66%) of cases showed no myocardial infarction on macro- or microscopic examination consistent with arrhythmogenic death. There was significantly (p < 0.001) higher inflammation in cases with delayed-onset death vs sudden death and significantly more inflammation surrounding the dissected vs. non-dissected vessel segments. N = 17 (47%) cases showed limited intimal fibro-elastic thickening but no features of fibromuscular dysplasia and no endothelial or internal elastic lamina abnormalities. There were no differences in vasa vasorum density between SCAD and control cases. TEM revealed no general ultrastructural differences in ECM components or markers of fibroblast metabolic activity. CONCLUSIONS: Assessment of SCD requires careful exclusion of SCAD, particularly in cases without myocardial necrosis. Peri-coronary inflammation in SCAD is distinct from vasculitides and likely a reaction to, rather than a cause for SCAD. Coronary fibromuscular dysplasia or increased vasa vasorum density do not appear pathophysiologically important. Dermal connective tissue changes are not common in SCAD survivors. TRANSLATIONAL PERSPECTIVE: SCAD, especially of distal coronary territories should be carefully assessed at post mortem in SCD cases, even where there are no signs of myocardial infarction. The immediate cause of SCAD is likely to be the development of a spontaneous intramural haematoma rather than an intimal disruption or 'tear'. This does not seem to be directly related to increased vasa vasorum density, coronary fibromuscular dysplasia or local inflammation (except as a response to injury). Although SCAD is rarely associated with hereditary connective tissue disorders, there does not seem to be a more generalizable global connective tissue ultrastructural abnormality in most cases

Imaging intact human organs with local resolution of cellular structures using hierarchical phase-contrast tomography

Imaging intact human organs from the organ to the cellular scale in three dimensions is a goal of biomedical imaging. To meet this challenge, we developed hierarchical phase-contrast tomography (HiP-CT), an X-ray phase propagation technique using the European Synchrotron Radiation Facility (ESRF)’s Extremely Brilliant Source (EBS). The spatial coherence of the ESRF-EBS combined with our beamline equipment, sample preparation and scanning developments enabled us to perform non-destructive, three-dimensional (3D) scans with hierarchically increasing resolution at any location in whole human organs. We applied HiP-CT to image five intact human organ types: brain, lung, heart, kidney and spleen. HiP-CT provided a structural overview of each whole organ followed by multiple higher-resolution volumes of interest, capturing organotypic functional units and certain individual specialized cells within intact human organs. We demonstrate the potential applications of HiP-CT through quantification and morphometry of glomeruli in an intact human kidney and identification of regional changes in the tissue architecture in a lung from a deceased donor with coronavirus disease 2019 (COVID-19)