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PURPOSE: Peripheral T cell CXCR3 expression has been found uniquely lower in patients having neovascular age-related macular degeneration (nAMD) than in healthy individuals. The CXCR3-axis has been shown to have angiostatic and antifibrotic properties. We have recently investigated systemic markers in patients with myeloproliferative neoplasms (MPNs) because of their higher prevalence of AMD, and we have observed higher systemic chronic low-grade inflammation and immunosenescence signs in MPNs with drusen (MPNd) compared to those with normal retinas (MPNn). The MPNs evolve in a biological continuum from early cancer-stages (essential thrombocytosis, polycythemia vera) to the advanced myelofibrosis stage. Especially myelofibrosis is characterized by bone marrow angiogenesis and fibrosis, similarly to retinal observations in nAMD. We speculate if we can find lower CXCR3 expression in MPNs, particularly myelofibrosis and if differences are seen between MPNd and MPNn. We also wanted to compare expression in nAMD and intermediate (i)AMD.METHODS: Patients in this cross-sectional study were 29 nAMD, 28 iAMD, 35 MPNd, and 27 MPNn. We performed flowcytometry on blood to measure CXCR3 expression.RESULTS: CD8+CXCR3 expression in nAMD was 6,1%, significantly lower than in iAMD 16%, MPNd 11%, MPNn 12% (p-values<0.05). Similar results were seen for CD4+CXCR3 expression. We also found CXCR3 expression decreasing over the MPN-continuum. For instance, in myelofibrosis, intermediate monocytes expression was 6.2%, significantly lower than 18% in ET and 18% in PV (p-values<0.05).CONCLUSIONS: We find CXCR3 downregulation on T-cells and some monocyte subset in nAMD compared to iAMD, MPNd, and MPNn, in line with previous nAMD studies. We also find CXCR3 downregulation in most monocyte subsets over the MPN continuum. Systemic leukocyte CXCR3 expression could both be involved in changes seen in the retina and the bone marrow. Further understanding the CXCR3-axi

OBJECTIVE: To explore the possibilities of wearable multi-modal monitoring in epilepsy and to identify effective strategies for seizure-detection.METHODS: Thirty patients with suspected epilepsy admitted to video electroencephalography (EEG) monitoring were equipped with a wearable multi-modal setup capable of continuous recording of electrocardiography (ECG), accelerometry (ACM) and behind-the-ear EEG. A support vector machine (SVM) algorithm was trained for cross-modal automated seizure detection. Visualizations of multi-modal time series data were used to generate ideas for seizure detection strategies.RESULTS: Three patients had more than five seizures and were eligible for SVM classification. Classification of 47 focal tonic seizures in one patient found a sensitivity of 84% with a false alarm rate (FAR) of 8/24 h. In two patients each with nine focal nonmotor seizures it yielded a sensitivity of 100% and a FAR of 13/24 h and 5/24. Visual comparisons of features were used to identify strategies for seizure detection in future research.CONCLUSIONS: Multi-modal monitoring in epilepsy using wearables is feasible and automatic seizure detection may benefit from multiple modalities when compared to uni-modal EEG.SIGNIFICANCE: This study is unique in exploring a combination of wearable EEG, ECG and ACM and can help inform future research on monitoring of epilepsy.

PURPOSE: To meet the demands imposed by the continuing growth of the Age-related macular degeneration (AMD) patient population, automation of follow-ups by detecting retinal oedema using deep learning might be a viable approach. However, preparing and labelling data for training is time consuming. In this study, we investigate the feasibility of training a convolutional neural network (CNN) to accurately detect retinal oedema on optical coherence tomography (OCT) images of AMD patients with labels derived directly from clinical treatment decisions, without extensive preprocessing or relabelling.METHODS: A total of 50 439 OCT images with associated treatment information were retrieved from databases at the Department of Ophthalmology, Rigshospitalet, Copenhagen, Denmark between 01.06.2007 and 01.06.2018. A CNN was trained on the retrieved data with the recorded treatment decisions as labels and validated on a subset of the data relabelled by three ophthalmologists to denote presence of oedema.RESULTS: Moderate inter-grader agreement on presence of oedema in the relabelled data was found (76.4%). Despite different training and validation labels, the CNN performed on par with inter-grader agreement in detecting oedema on OCT images (AUC 0.97, accuracy 90.9%) and previously published models based on relabelled datasets.CONCLUSION: The level of performance shown by the current model might make it valuable in detecting disease activity in automated AMD patient follow-up systems. Our approach demonstrates that high accuracy is not necessarily constrained by incongruent training and validation labels. These results might encourage the use of existing clinical databases for development of deep learning based algorithms without labour-intensive preprocessing in the future.