BACKGROUND:Assessment of cognitive impairment (CI) in multiple sclerosis (MS) patients is very useful, but it requires time-consuming expert evaluation with specialized materials. The Brief International Cognitive Assessment for Multiple Sclerosis (B...

Background: Encephalitis is a severe and potentially life-threatening inflammatory disorder of the central nervous system. Without prompt diagnosis and appropriate treatment, it often results in poor clinical outcomes. The study aimed to develop an artificial intelligence-based model that distinguishes autoimmune encephalitis from infectious encephalitis, encompassing a broad spectrum of autoimmune encephalitis phenotypes, serostatuses, and neuroimmunological entities. Methods: We conducted a retrospective analysis of patients diagnosed with autoimmune encephalitis, including paraneoplastic neurological syndromes and/or infectious encephalitis, at Vilnius University Hospital Santaros Klinikos from 2016 to 2024. Supervised machine learning techniques were used to train the models, and Shapley Additive Explanations analysis was applied to improve their interpretability. Results: A total of 233 patients were included in the study. The Random Forest model demonstrated the best performance in differentiating the etiology of encephalitis, achieving an AUROC of 0.966. Further analysis revealed that laboratory, electroencephalography, and clinical data were the most influential predictors, whereas imaging data contributed less to classification accuracy. Conclusions: We developed a machine learning model capable of distinguishing infectious encephalitis from both seropositive and seronegative autoimmune encephalitis. Since autoimmune cases may be misdiagnosed as infectious in the absence of detectable antibodies, our model has the potential to support clinical decision-making and reduce diagnostic uncertainty.

Autoimmune encephalitis is an increasingly recognized cause of encephalitis. Detection of disease-specific antibodies is the cornerstone of diagnosis. Despite growing clinical awareness and the routine availability of antibody assays in most centers, diagnosis remains challenging due to diverse clinical presentations, the low diagnostic yield of commercial antibody kits, difficulties in interpreting antibody results, and a substantial proportion of paraclinically silent patients. This narrative review summarizes current diagnostic approaches to autoimmune encephalitis, with particular emphasis on antibody detection strategies, the diagnostic yield of different techniques, serum vs. cerebrospinal fluid testing, and the diagnostic value of supportive cerebrospinal fluid biomarkers. In addition, we discuss patients with seronegative or paraclinically silent disease, in whom diagnosis relies primarily on clinical criteria and the exclusion of alternative etiologies. Finally, we outline future perspectives, including advanced immunological techniques and machine learning-based diagnostic models, which may facilitate earlier identification and more accurate classification of autoimmune encephalitis. Improved integration of clinical assessment with cerebrospinal fluid biomarkers and novel analytical tools may reduce diagnostic delay and support the timely initiation of immunotherapy, ultimately improving neurological outcomes.

Background: Transthyretin (TTR) p.Ala45Thr variant is pathogenic, typically associated with cardiomyopathy and peripheral neuropathy. Leptomeningeal and central nervous system (CNS) involvement is rare, and longitudinal treatment data remain limited. Methods: We conducted a single-patient longitudinal observational study of a patient with genetically confirmed hereditary transthyretin amyloidosis (ATTRv) carrying the TTR p.Ala45Thr variant with CNS involvement. The patient was followed over 5 years using multimodal assessments, including neurological and cognitive testing, cerebrospinal fluid (CSF) analysis, magnetic resonance imaging (MRI), and electrophysiological studies. Results: A 48-year-old woman presented with progressive neurological symptoms beginning at age 37, including gait ataxia, dysarthria, and left hypoesthesia. Family history was notable for early-onset neurological decline in maternal relatives. Baseline MRI showed cerebellar micro-hemorrhages, leptomeningeal enhancement, and a cervical spinal cord lesion (C4–C7). CSF protein was markedly elevated, and genetic testing confirmed the TTR p.Ala45Thr variant. Cardiac and peripheral nerve evaluations were unremarkable. Tolcapone therapy was initiated and titrated to the maximal dose. Over 5 years, follow-up assessments demonstrated progressive neurological and cognitive decline, increased CSF protein levels, and enlargement of the spinal cord lesion, with mild progression of leptomeningeal and ependymal enhancement on MRI. Conclusion: This report highlights progressive CNS involvement in Ala45Thr-associated ATTR despite treatment with a CNS-penetrant TTR stabilizer. These findings suggest that disease progression may continue despite TTR stabilization in some patients and underscore the need for early intervention, systematic CNS monitoring, and further development of CNS-targeted therapies.