Use of convolutional neural networks for the detection of u-serrated patterns in direct immunofluorescence images to facilitate the diagnosis of epidermolysis bullosa acquisita

The u-serrated immunodeposition pattern in direct immunofluorescence (DIF) microscopy is a recognizable feature and confirmative for the diagnosis of epidermolysis bullosa acquisita (EBA). Due to unfamiliarity with serrated patterns, serration pattern recognition is still of limited use in routine DIF microscopy. The objective of this study was to investigate the feasibility of using convolutional neural networks (CNNs) for the recognition of u-serrated patterns that can assist in the diagnosis of EBA. The nine most commonly used CNNs were trained and validated by using 220,800 manually delineated DIF image patches from 106 images of 46 different patients. The data set was split into 10 subsets: nine training subsets from 42 patients to train CNNs and the last subset from the remaining four patients for a validation data set of diagnostic accuracy. This process was repeated 10 times with a different subset used for validation. The best-performing CNN achieved a specificity of 89.3% and a corresponding sensitivity of 89.3% in the classification of u-serrated DIF image patches, an expert level of diagnostic accuracy. Experiments and results show the effectiveness of CNN approaches for u-serrated pattern recognition with a high accuracy. The proposed approach can assist clinicians and pathologists in recognition of u-serrated patterns in DIF images and facilitate the diagnosis of EBA

Automatic classification of serrated patterns in direct immunouorescence images

Direct immunofluorescence (DIF) images are used by clinical experts for the diagnosis of autoimmune blistering diseases. The analysis of serration patterns in DIF images concerns two types of patterns, namely n- and u-serrated. Manual analysis is time-consuming and challenging due to noise. We propose an algorithm for the automatic classification of serrated patterns in DIF images. We first segment the epidermal basement membrane zone (BMZ) where n- and u-serrated patterns are typically found. Then, we apply a bank of B-COSFIRE filters to detect ridges and determine their orientations with respect to the BMZ. Finally, we classify an image by comparing its normalized histogram of relative orientations with those of the training images using a nearest neighbor approach. We achieve a recognition rate of 84.4% on a UMCG data set of 416 DIF images, which is comparable to 83.4% by clinical experts.peer-reviewe

Outcomes and comorbidities of SCN1A-related seizure disorders

PURPOSE: Differentiating between Dravet syndrome and non-Dravet SCN1A-related phenotypes is important for prognosis regarding epilepsy severity, cognitive development, and comorbidities. When a child is diagnosed with genetic epilepsy with febrile seizures plus (GEFS+) or febrile seizures (FS), accurate prognostic information is essential as well, but detailed information on seizure course, seizure freedom, medication use, and comorbidities is lacking for this milder patient group. In this cross-sectional study, we explore disease characteristics in milder SCN1A-related phenotypes and the nature, occurrence, and relationships of SCN1A-related comorbidities in both patients with Dravet and non-Dravet syndromes. METHODS: A cohort of 164 Dutch participants with SCN1A-related seizures was evaluated, consisting of 116 patients with Dravet syndrome and 48 patients with either GEFS+, febrile seizures plus (FS+), or FS. Clinical data were collected from medical records, semi-structured telephone interviews, and three questionnaires: the Functional Mobility Scale (FMS), the Pediatric Quality of Life Inventory (PedsQL) Measurement Model, and the Child or Adult Behavior Checklists (CBCL/ABCL). RESULTS: Walking disabilities and severe behavioral problems affect 71% and 43% of patients with Dravet syndrome respectively and are almost never present in patients with non-Dravet syndromes. These comorbidities are strongly correlated to lower quality-of-life (QoL) scores. Less severe comorbidities occur in patients with non-Dravet syndromes: learning problems and psychological/behavioral problems are reported for 27% and 38% respectively. The average QoL score of the non-Dravet group was comparable with that of the general population. The majority of patients with non-Dravet syndromes becomes seizure-free after 10 years of age (85%). CONCLUSIONS: Severe behavioral problems and walking disabilities are common in patients with Dravet syndrome and should receive specific attention during clinical management. Although the epilepsy course of patients with non-Dravet syndromes is much more favorable, milder comorbidities frequently occur in this group as well. Our results may be of great value for clinical care and informing newly diagnosed patients and their parents about prognosis

Mutations in the histone methyltransferase gene KMT2B cause complex early-onset dystonia.

Histone lysine methylation, mediated by mixed-lineage leukemia (MLL) proteins, is now known to be critical in the regulation of gene expression, genomic stability, cell cycle and nuclear architecture. Despite MLL proteins being postulated as essential for normal development, little is known about the specific functions of the different MLL lysine methyltransferases. Here we report heterozygous variants in the gene KMT2B (also known as MLL4) in 27 unrelated individuals with a complex progressive childhood-onset dystonia, often associated with a typical facial appearance and characteristic brain magnetic resonance imaging findings. Over time, the majority of affected individuals developed prominent cervical, cranial and laryngeal dystonia. Marked clinical benefit, including the restoration of independent ambulation in some cases, was observed following deep brain stimulation (DBS). These findings highlight a clinically recognizable and potentially treatable form of genetic dystonia, demonstrating the crucial role of KMT2B in the physiological control of voluntary movement.Funding for the project was provided by the Wellcome Trust for UK10K (WT091310) and DDD Study. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund [grant number HICF-1009-003] - see www.ddduk.org/access.html for full acknowledgement. This work was supported in part by the Intramural Research Program of the National Human Genome Research Institute and the Common Fund, NIH Office of the Director. This work was supported in part by the German Ministry of Research and Education (grant nos. 01GS08160 and 01GS08167; German Mental Retardation Network) as part of the National Genome Research Network to A.R. and D.W. and by the Deutsche Forschungsgemeinschaft (AB393/2-2) to A.R. Brain expression data was provided by the UK Human Brain Expression Consortium (UKBEC), which comprises John A. Hardy, Mina Ryten, Michael Weale, Daniah Trabzuni, Adaikalavan Ramasamy, Colin Smith and Robert Walker, affiliated with UCL Institute of Neurology (J.H., M.R., D.T.), King’s College London (M.R., M.W., A.R.) and the University of Edinburgh (C.S., R.W.)