Corpus overview


MeSH Disease

Human Phenotype


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    High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms

    Authors: Christiana Franke; Caroline Ferse; Jakob Kreye; Momsen Reincke; Elisa Sanchez-Sendin; Andrea Rocco; Mirja Steinbrenner; Stefan Angermair; Sascha Treskatsch; Daniel Zickler; Kai-Uwe Eckardt; Rick Dersch; Jonas Hosp; Heinrich J. Audebert; Matthias Endres; Christoph J. Ploner; Harald Pruess

    doi:10.1101/2020.07.01.20143214 Date: 2020-07-06 Source: medRxiv

    COVID-19 intensive care patients occasionally develop neurological symptoms. The absence of SARS-CoV-2 in most cerebrospinal fluid (CSF) samples suggests the involvement of further mechanisms including autoimmunity HP. We therefore determined whether anti-neuronal or anti-glial autoantibodies are present in eleven consecutive severely ill COVID-19 patients presenting with unexplained neurological symptoms. These included myoclonus MESHD myoclonus HP, cranial nerve involvement, oculomotor disturbance, delirium MESHD delirium HP, dystonia MESHD dystonia HP and epileptic seizures MESHD seizures HP. Most patients showed signs of CSF inflammation MESHD and increased levels of neurofilament light chain. All patients had anti-neuronal autoantibodies in serum SERO or CSF when assessing a large panel of autoantibodies against intracellular and surface antigens relevant for central nervous system diseases MESHD using cell-based assays and indirect immunofluorescence on murine brain sections. Antigens included proteins well-established in clinical routine, such as Yo or NMDA receptor, but also a variety of specific undetermined epitopes on brain sections. These included vessel endothelium, astrocytic proteins and neuropil of basal ganglia, hippocampus or olfactory bulb. The high frequency of autoantibodies targeting the brain in the absence of other explanations suggests a causal relationship to clinical symptoms, in particular to hyperexcitability ( myoclonus MESHD myoclonus HP, seizures MESHD seizures HP). While several underlying autoantigens still await identification in future studies, presence of autoantibodies may explain some aspects of multi-organ disease MESHD in COVID-19 and can guide immunotherapy in selected cases.

    Leverging Deep Learning to Simulate Coronavirus Spike proteins has the potential to predict future Zoonotic sequences

    Authors: Lisa Caroline Crossman

    doi:10.1101/2020.04.20.046920 Date: 2020-04-20 Source: bioRxiv

    MotivationCoronaviridae are a family of positive-sense RNA viruses capable of infecting humans and animals. These viruses usually cause a mild to moderate upper respiratory tract infection MESHD respiratory tract infection HP, however, they can also cause more severe symptoms, gastrointestinal and central nervous system diseases MESHD. These viruses are capable of flexibly adapting to new environments, hence health threats from coronavirus are constant and long-term. Immunogenic spike proteins are glyco-proteins found on the surface of Coronaviridae particles that mediate entry to host cells. The aim of this study was to train deep learning neural networks to produce simulated spike protein sequences, which may be able to aid in knowledge and/or vaccine design by creating alternative possible spike sequences that could arise from zoonotic sources in future. ResultsHere we have trained deep learning recurrent neural networks (RNN) to provide computer-simulated coronavirus spike protein sequences in the style of previously known sequences and examine their characteristics. Training used a dataset of alpha, beta, gamma and delta coronavirus spike sequences. In a test set of 100 simulated sequences, all 100 had most significant BLAST matches to Spike proteins in searches against NCBI non-redundant dataset (NR) and also possessed concomitant Pfam domain matches. ConclusionsSimulated sequences from the neural network may be able to guide us in future with prospective targets for vaccine discovery in advance of a potential novel zoonosis MESHD. We may effectively be able to fast-forward through evolution using neural networks to investigate sequences that could arise.

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MeSH Disease
Human Phenotype

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