Optical Genome Mapping for the Molecular Diagnosis of Facioscapulohumeral Muscular Dystrophy: Advancement and Challenges

Facioscapulohumeral muscular dystrophy (FSHD) is the second most common muscular dystrophy in adults, and it is associated with local D4Z4 chromatin relaxation, mostly via the contraction of the D4Z4 macrosatellite repeat array on chromosome 4q35. In this study, we aimed to investigate the use of Optical Genome Mapping (OGM) as a diagnostic tool for testing FSHD cases from the UK and India and to compare OGM performance with that of traditional techniques such as linear gel (LGE) and Pulsed-field gel electrophoresis (PFGE) Southern blotting (SB). A total of 6 confirmed and 19 suspected FSHD samples were processed with LGE and PFGE, respectively. The same samples were run using a Saphyr Genome-Imaging Instrument (1-color), and the data were analysed using custom EnFocus FSHD analysis. OGM was able to confirm the diagnosis of FSHD1 in all FSHD1 cases positive for SB (n = 17), and D4Z4 sizing highly correlated with PFGE-SB (p < 0.001). OGM correctly identified cases with mosaicism for the repeat array contraction (n = 2) and with a duplication of the D4Z4 repeat array. OGM is a promising new technology able to unravel structural variants in the genome and seems to be a valid tool for diagnosing FSHD1

Neuromuscular disease genetics in under-represented populations: increasing data diversity

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses ‘solved’ or ‘possibly solved’ ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% ‘solved’ and ∼13% ‘possibly solved’ outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally

Neuromuscular disease genetics in underrepresented populations : increasing data diversity

DATA AVAILABILITY : At the end of the study, participants de-identified exome and genome data will be archived in the European Molecular Biology Laboratory European Bioinformatics Institute’s European Genome-Phenome Archive (EMBL EBI EGA), with community access to this and selected de-identified REDCap data managed via an ICGNMD Data Access Committee.Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.This work was supported by a Medical Research Council strategic award to establish an International Centre for Genomic Medicine in Neuromuscular Diseases (ICGNMD) MR/S005021/1. Additional ICGNMD support including travel and subsistence costs was received from the National Brain Appeal (UK Charity 290173) and University College London Global Engagement Funds. Fellowships for R.S.S.F. and K.N. were funded by the Guarantors of Brain (UK Charity 1197319). The authors acknowledge and are grateful for: conference bursaries from the World Muscle Society to R.S.S.F. S.R., K.N., O.Y.K., P.J.T., V.V.Y. S.V.D.M. and R.L. are members of the European Reference Network for Rare Neuromuscular Diseases (ERN EURO-MND). M.P.K.: National Institute of Neurological Disorders and Stroke (1K23NS112463), American Association of Neuromuscular & Electrodiagnostic Medicine Development Award and Allen Foundation. D.B.: National Institute of Neurological Disorders and Stroke (K23NS117310) and support from Biogen for the KCTN1 Natural History Study. G.M.R.: University College London and UCLH Biomedical Research Centre funding, Health Education England and University College London Hospitals NHS Foundation Trust Innovation Fund. R.M.F., R.W.T. and K.P.: Wellcome core support (203105/Z/16/Z). R.M.F. received additional support from the Lily Foundation and the Leigh Syndrome International Consortium. A.T.: EU Horizon 2020 research and innovation Solve-RD project, No. 779257. F.H.W., M.S., M.B. and A.V.: South African Medical Research Council award ‘The genetics of Neuromuscular Diseases in South African patient populations: the ICGNMD study’. K.T. is funded by a J. C. Bose Fellowship, Science and Engineering Research Board (SERB) Department of Science and Technology, India. P.G. is supported by the Centre for DNA Fingerprinting and Diagnostics (CDFD) Core Research Grant, Department of Biotechnology, Government of India. R.H.: Wellcome award 109915/Z/15/Z, UK Medical Research Council award MR/N025431/1, the Lily Foundation, Evelyn Trust Research Grant (Ref 19/14), Action for A-T and UK Research and Innovation Newton Fund (MR/NO27302/1). P.F.C.: Wellcome awards 212219/Z/18/Z and 224486/Z/21/Z, UK Medical Research Council awards MC_PC_21046, MR/S035699/1 and MR/ S01165X/1, LifeArc Philanthropic Fund, NIHR BioResource for Translational Research in Common and Rare Diseases, Alzheimer’s Society, NIHR BioResource for Genes and Cognition and Leverhulme Trust. R.D.S.P.: UK Medical Research Council MR/ S002065/1 and MC_PC_21046, and the Lily Foundation. H.H.: UK Medical Research Council, Wellcome, UCLH Biomedical Research Centre (NIHR-BRC), Rosetrees Trust, and SOLVE-RD. M.M.R.: Wellcome grant G104817, National Institute of Neurological Disorders and Stroke and Office of Rare Diseases grants U54NS065712 and 1UOINS109403-01 and Muscular Dystrophy Association grant.https://www.edusoft.ro/brain/index.php/brainam2024Paediatrics and Child HealthSDG-03:Good heatlh and well-bein

Innovations in acute stroke reperfusion strategies

Vascular neurology is witnessing unprecedented innovations in the management of acute ischemic stroke, especially in reperfusion strategies. The emergence of mechanical thrombectomy with new generation devices has revolutionized the treatment of acute ischemic stroke with large vessel occlusion. The reperfusion strategies are evolving with the extension of the window period for thrombolysis and endovascular therapy through the concept of “tissue clock” in addition to the established “time clock.” The newer generation of thrombolytic drugs like tenecteplase are promising exciting times ahead in acute stroke care. In this “viewpoint,'” the evolution of reperfusion therapy in acute ischemic stroke will be discussed followed by recent innovations in reperfusion strategies

MEFFNet: Forecasting Myoelectric Indices of Muscle Fatigue in Healthy and Post-Stroke During Voluntary and FES-Induced Dynamic Contractions

Myoelectric indices forecasting is important for muscle fatigue monitoring in wearable technologies, adaptive control of assistive devices like exoskeletons and prostheses, functional electrical stimulation (FES)-based Neuroprostheses, and more. Non-stationary temporal development of these indices in dynamic contractions makes forecasting difficult. This study aims at incorporating transfer learning into a deep learning model, Myoelectric Fatigue Forecasting Network (MEFFNet), to forecast myoelectric indices of fatigue (both time and frequency domain) obtained during voluntary and FES-induced dynamic contractions in healthy and post-stroke subjects respectively. Different state-of-the-art deep learning models along with the novel MEFFNet architecture were tested on myoelectric indices of fatigue obtained during ${a}\text {)}$ voluntary elbow flexion and extension with four different weights (1 kg, 2 kg, 3 kg, and 4 kg) in sixteen healthy subjects, and ${b}\text {)}$ FES-induced elbow flexion in sixteen healthy and seventeen post-stroke subjects under three different stimulation patterns (customized rectangular, trapezoidal, and muscle synergy-based). A version of MEFFNet, named as pretrained MEFFNet, was trained on a dataset of sixty thousand synthetic time series to transfer its learning on real time series of myoelectric indices of fatigue. The pretrained MEFFNet could forecast up to 22.62 seconds, 60 timesteps, in future with a mean absolute percentage error of 15.99 ± 6.48% in voluntary and 11.93 ± 4.77% in FES-induced contractions, outperforming the MEFFNet and other models under consideration. The results suggest combining the proposed model with wearable technology, prosthetics, robotics, stimulation devices, etc. to improve performance. Transfer learning in time series forecasting has potential to improve wearable sensor predictions

WILSON’S DISEASE: ATYPICAL IMAGING FEATURES

Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17- year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities) and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present

Wilson’s disease: Atypical Imaging features

Wilson’s disease is a genetic movement disorder with characteristic clinical and imaging features. We report a 17-year-old boy who presented with sialorrhea, hypophonic speech, paraparesis with repeated falls and recurrent seizures along with cognitive decline. He had bilateral Kayser Flescher rings. Other than the typical features of Wilson’s disease in cranial MRI, there were extensive white matter signal abnormalities (T2 and FLAIR hyperintensities) and gyriform contrast enhancement which are rare imaging features in Wilson's disease. A high index of suspicion is required to diagnose Wilson’s disease when atypical imaging features are present

Reversible dementia: The imitation game

Rapidly progressive dementia (RPD) is an emergency in behavioural or cognitive neurology. Many rare neuroinfections like Neurosyphilis may be missed, if they are not thoroughly evaluated. We report a patient with subacute onset and progressive cognitive decline, extrapyramidal involvement and myoclonic jerks who was initially suspected as probable autoimmune encephalitis or Creutzfeldt-Jakob disease (CJD). Investigations revealed positive serum and cerebrospinal fluid (CSF) Venereal Disease Research Laboratory test (VDRL). On treatment with penicillin, he developed Jarisch-Herxheimer reaction and was treated symptomatically. After two weeks of penicillin, he improved significantly and except for mild short term memory recall, he is asymptomatic for last two years

Latest Trends in Outcome Measures in Dementia and Mild Cognitive Impairment Trials

Disease modification trials in dementia and mild cognitive impairment (MCI) have not met with success. One potential criticism of these trials is the lack of sensitive outcome measures. A large number of outcome measures have been employed in dementia and MCI trials. This review aims to describe and analyze the utility of cognitive/clinical outcome measures in Alzheimer’s disease (AD) and MCI trials. Methods: A PubMed search was conducted using relevant MeSH terms and exploded keywords. The search was confined to English language publications of human studies from the last five years which describe the latest trends in the use of outcome measures. Results: Despite broad use, the outcome measures employed are heterogeneous, with little data on correlations between scales. Another problem is that most studies are over-reliant on clinician/researcher assessment and cognitive outcomes, and there is a definite lack of stakeholder input. Finetuning of the paradigm is also required for people with early-stage disease, mild to moderate disease, and advanced dementia, as the outcome measures in these subgroups have varying relevance. Disease modification/prevention is an appropriate goal in early disease, whereas palliation and freedom from discomfort are paramount in later stages. The outcome measures selected must be suitable for and sensitive to these particular care goals. Although there is a shift to enrich MCI cohorts using a biomarker-based approach, the clinical relevance of such outcome measures remains uncertain. Conclusions: Outcome measures in dementia/MCI trials remain inhomogeneous and diverse, despite extensive use. Outcome measures fall within several paradigms, including cognitive, functional, quality-of-life, biomarker-based, and patient-reported outcome measures. The success of future disease-modifying trials is reliant to a large extent on the selection of outcome measures which combine all outcomes of clinical relevance as well as clinical meaning. Outcome measures should be tied to the type and stage of dementia and to the specific interventions employed