Flow cytometry identifies changes in peripheral and intrathecal lymphocyte patterns in CNS autoimmune disorders and primary CNS malignancies.

Background: Immune dysregulation is a hallmark of autoimmune diseases of the central nervous system (CNS), characterized by an excessive immune response, and primary CNS tumors (pCNS-tumors) showing a highly immunosuppressive parenchymal microenvironment.

Methods: Aiming to provide novel insights into the pathogenesis of CNS autoimmunity and cerebral tumor immunity, we analyzed the peripheral blood (PB) and cerebrospinal fluid (CSF) of 81 autoimmune limbic encephalitis (ALE), 148 relapsing-remitting multiple sclerosis (RRMS), 33 IDH-wildtype glioma, 9 primary diffuse large B cell lymphoma of the CNS (CNS-DLBCL), and 110 controls by flow cytometry (FC). Additionally, an in-depth immunophenotyping of the PB from an independent cohort of 20 RRMS and 18 IDH-wildtype glioblastoma patients compared to 19 controls was performed by FC combined with unsupervised computational approaches.

Myasthenia Gravis: utilising cross-platform quantitative content analysis to uncover and validate unmet needs.

Background: Recent years have seen a rapid growth in the number of online health communities targeted at patients with long-term conditions. Myasthenia Gravis (MG) is a rare neurological disease for which such communities have not been analysed before. The aim of this study was to better understand the needs of the MG population through the collation and categorisation of questions that users of MG social media were asking fellow users on these platforms.

Preventing long-term disability in CIDP: the role of timely diagnosis and treatment monitoring in a multicenter CIDP cohort.

Background: Chronic inflammatory demyelinating polyneuropathy (CIDP) is an inflammatory disease affecting the peripheral nerves and the most frequent autoimmune polyneuropathy. Given the lack of established biomarkers or risk factors for the development of CIDP and patients' treatment response, this research effort seeks to identify potential clinical factors that may influence disease progression and overall treatment efficacy.

Methods: In this multicenter, retrospective analysis, we have screened 197 CIDP patients who presented to the University Hospitals in Düsseldorf, Berlin, Cologne, Essen, Magdeburg and Munich between 2018 and 2022.

Identification of disease phenotypes in acetylcholine receptor-antibody myasthenia gravis using proteomics-based consensus clustering.

Background: The clinical heterogeneity of myasthenia gravis (MG), an autoimmune disease defined by antibodies (Ab) directed against the postsynaptic membrane, constitutes a challenge for patient stratification and treatment decision making. Novel strategies are needed to classify patients based on their biological phenotypes aiming to improve patient selection and treatment outcomes.

Methods: For this purpose, we assessed the serum proteome of a cohort of 140 patients with anti-acetylcholine receptor-Ab-positive MG and utilised consensus clustering as an unsupervised tool to assign patients to biological profiles.

Triage Performance Across Large Language Models, ChatGPT, and Untrained Doctors in Emergency Medicine: Comparative Study.

Background: Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage.