Description of a patient cohort with Hereditary Sensory Neuropathy type 1 without retinal disease Macular Telangiectasia type 2 - implications for retinal screening in HSN1.

Pathogenic variants in the genes encoding serine palmitoyl transferase (SPTLC1 or SPTLC2) are the most common causes of the rare peripheral nerve disorder Hereditary Sensory Neuropathy Type 1 (HSN1). Macular telangiectasia type 2 (MacTel), a retinal disorder associated with disordered serine-glycine metabolism, has been described in some patients with HSN1. This study aims to further investigate this association in a cohort of people with HSN1. Fourteen patients with a clinically and genetically confirmed diagnosis of HSN1 from the National Hospital for Neurology and Neurosurgery (NHNN, University College London Hospitals NHS Foundation Trust, London, United Kingdom) were recruited to the MacTel Registry, between July 2018 and April 2019. Two additional patients were identified from the dataset of the international clinical registry study (www.lmri.net). Ocular examination included fundus autofluorescence, blue light and infrared reflectance, macular pigment optical density mapping and optical coherence tomography. Twelve patients had a pathogenic variant in the SPTLC1 gene, with p.Cys133Trp in 11 cases (92%) and p.Cys133Tyr in one case (8%). Four patients had a variant in the SPTLC2 gene. None of the patients showed clinical evidence of MacTel. The link between HSN1 and MacTel seems more complex than can solely be explained by the genetic variants. An extension of the spectrum of SPTLC1/2-related disease with phenotypic pleiotropy is proposed. HSN1 patients should be screened for visual symptoms and referred for specialist retinal screening, but the association of the two diseases is likely to be variable and remains unexplained.

Reliability of Retinal Pathology Quantification in Age-Related Macular Degeneration: Implications for Clinical Trials and Machine Learning Applications.

Purpose: To investigate the interreader agreement for grading of retinal alterations in age-related macular degeneration (AMD) using a reading center setting. Methods: In this cross-sectional case series, spectral-domain optical coherence tomography (OCT; Topcon 3D OCT, Tokyo, Japan) scans of 112 eyes of 112 patients with neovascular AMD (56 treatment naive, 56 after three anti-vascular endothelial growth factor injections) were analyzed by four independent readers. Imaging features specific for AMD were annotated using a novel custom-built annotation platform. Dice score, Bland-Altman plots, coefficients of repeatability, coefficients of variation, and intraclass correlation coefficients were assessed. Results: Loss of ellipsoid zone, pigment epithelium detachment, subretinal fluid, and drusen were the most abundant features in our cohort. Subretinal fluid, intraretinal fluid, hypertransmission, descent of the outer plexiform layer, and pigment epithelium detachment showed highest interreader agreement, while detection and measures of loss of ellipsoid zone and retinal pigment epithelium were more variable. The agreement on the size and location of the respective annotation was more consistent throughout all features. Conclusions: The interreader agreement depended on the respective OCT-based feature. A selection of reliable features might provide suitable surrogate markers for disease progression and possible treatment effects focusing on different disease stages. Translational Relevance: This might give opportunities for a more time- and cost-effective patient assessment and improved decision making as well as have implications for clinical trials and training machine learning algorithms.

Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning.

PURPOSE: We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). DESIGN: Development and validation of a deep-learning model for feature segmentation. METHODS: Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. RESULTS: On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. CONCLUSIONS: The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.

[Food and beverages available in automatic food dispensers in health care facilities of the Portugal North Health Region]. / Alimentos disponibilizados nas máquinas de venda automática nos centros de saúde e hospitais da Região de Saúde do Norte.

Patients and health staff frequently need to stay in health care facilities for quite a long time. Therefore, it's necessary to create the conditions that allow the ingestion of food during those periods, namely through the existence of automatic food dispensers. However, the available food and beverages might not always be compatible with a healthy diet. The aim of this work was to evaluate if the food and beverages available in automatic food dispensers in public Ambulatory Care Facilities (ACF) and Hospitals of the Portugal North Health Region were contributing to a healthy diet, during the year of 2007. A questionnaire was elaborated and sent to the Coordinators of the Health Sub-Regions and to the Hospital Administrators. The questionnaire requested information about the existence of automatic food dispensers in the several departments of each health care facility, as well as which food and beverages were available and most sold. Afterwards, the pre-processing of the results involved the classification of the food and beverages in three categories: recommended, sometimes recommended and not recommended. The questionnaire reply ratio was 71% in ACF and 83% in Hospitals. Automatic food dispensers were available in all the Hospitals and 86.5% of ACF. It wasn't possible to acquire food in 37% of the health facility departments. These departments were all located in ACF. The more frequently available beverages in departments with automatic food dispensers were coffee, still water, tea, juices and nectars and soft drinks. Still water, coffee, yogurt, juices and nectars and soft drinks were reported as the most sold. The more frequently avaliable food items were chocolate, recommended cookies, not recommended cakes, recommended sandwiches and sometimes recommended croissants. The food items reported as being the most sold were recommended sandwiches, chocolate, recommended cookies, sometimes recommended croissants and not recommended cookies. The beverages in the recommended and sometimes recommended groups were the most frequently available and sold. The not recommended food items were reported as being the most available, while both the recommended and not recommended food items were equally reported as being the most sold. Results show that unhealthy food and beverages are widely available in public health care facilities of the Portugal North Health Region.