Bilateral pontine brachium lesions in one autoantibodies directed against MOG positive patient: A case report.

RATIONALE: Myelin oligodendrocyte glycoprotein (MOG) antibody-related disease is a relatively recent entity in inflammatory demyelinating disease. Its clinical presentation varies in severity and the lack of specific imaging features makes it easy to misdiagnose. We now report the case of a MOG antibody-positive patient who presented with diplopia and dizziness, and whose brain magnetic resonance imaging (MRI) showed abnormal signals in the bilateral pontine brachium. PATIENT CONCERNS: A previously healthy 52-year-old woman presented with diplopia and dizziness, and was hospitalized 4 days after onset. DIAGNOSES: Brain MRI demonstrated abnormal hyperintense signals in the bilateral pontine brachium on T2-weighted fluid attenuated inversion recovery imaging. MRI enhancement showed abnormal enhancement foci in bilateral pontine brachium and pons. Cerebrospinal fluid examination showed Oligoclonal IgG bands were negative. The IgG index was normal, and serum aquaporin-4 antibody was negative, while serum MOG-Ab was positive (1:100). In conjunction with a positive serum MOG antibody and exclusion of other diseases, diagnosis of MOG antibody-related disease was made. INTERVENTIONS: Intravenous methylprednisolone followed by oral corticosteroids. OUTCOMES: Symptoms resolved completely. At 4-month follow-up. Follow-up after 4 months showed disappearance of the abnormal signal in the left pontine brachium and diminution of abnormal high signal in the right compared to the previous one, and there was no recurrence 1 year after the onset of the disease. LESSONS: If brain MRI indicating bilateral, multiple, and diffuse abnormal signals in the pontine brachium, and a discrepancy between the clinical symptoms and the imaging severity, a diagnosis of demyelinating disease should be considered highly probable. In such cases, anti-MOG antibody testing is essential for further defining the etiology. The clinical phenotype and imaging manifestations of MOG antibody-positive brainstem encephalitis may lack sufficient specificity to be readily identifiable. Timely diagnosis and early glucocorticoid therapy are beneficial in improving prognosis and preventing recurrence.

Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke.

Background: Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis. Objective: To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS. Methods: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes. Results: Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility. Conclusion: The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.

FGF21 attenuates neuroinflammation following subarachnoid hemorrhage through promoting mitophagy and inhibiting the cGAS-STING pathway.

BACKGROUND: Subarachnoid hemorrhage (SAH) represents a form of cerebrovascular event characterized by a notable mortality and morbidity rate. Fibroblast growth factor 21 (FGF21), a versatile hormone predominantly synthesized by the hepatic tissue, has emerged as a promising neuroprotective agent. Nevertheless, the precise impacts and underlying mechanisms of FGF21 in the context of SAH remain enigmatic. METHODS: To elucidate the role of FGF21 in inhibiting the microglial cGAS-STING pathway and providing protection against SAH-induced cerebral injury, a series of cellular and molecular techniques, including western blot analysis, real-time polymerase chain reaction, immunohistochemistry, RNA sequencing, and behavioral assays, were employed. RESULTS: Administration of recombinant fibroblast growth factor 21 (rFGF21) effectively mitigated neural apoptosis, improved cerebral edema, and attenuated neurological impairments post-SAH. Transcriptomic analysis revealed that SAH triggered the upregulation of numerous genes linked to innate immunity, particularly those involved in the type I interferon (IFN-I) pathway and microglial function, which were notably suppressed upon adjunctive rFGF21 treatment. Mechanistically, rFGF21 intervention facilitated mitophagy in an AMP-activated protein kinase (AMPK)-dependent manner, thereby preventing mitochondrial DNA (mtDNA) release into the cytoplasm and dampening the activation of the DNA-sensing cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway. Conditional knockout of STING in microglia markedly ameliorated the inflammatory response and mitigated secondary brain injuries post-SAH. CONCLUSION: Our results present the initial evidence that FGF21 confers a protective effect against neuroinflammation-associated brain damage subsequent to SAH. Mechanistically, we have elucidated a novel pathway by which FGF21 exerts this neuroprotection through inhibition of the cGAS-STING signaling cascade.

Identifying New Subtypes of Multiple System Atrophy Using Cluster Analysis.

Background: Multiple system atrophy (MSA) is a disease with diverse symptoms and the commonly used classifications, MSA-P and MSA-C, do not cover all the different symptoms seen in MSA patients. Additionally, these classifications do not provide information about how the disease progresses over time or the expected outcome for patients. Objective: To explore clinical subtypes of MSA with a natural disease course through a data-driven approach to assist in the diagnosis and treatment of MSA. Methods: We followed 122 cases of MSA collected from 3 hospitals for 3 years. Demographic characteristics, age of onset, clinical signs, scale assessment scores, and auxiliary examination were collected. Age at onset; time from onset to assisted ambulation; and UMSARS I, II, and IV, COMPASS-31, ICARS, and UPDRS III scores were selected as clustering elements. K-means, partitioning around medoids, and self-organizing maps were used to analyze the clusters. Results: The results of all three clustering methods supported the classification of three MSA subtypes: The aggressive progression subtype (MSA-AP), characterized by mid-to-late onset, rapid progression and severe clinical symptoms; the typical subtype (MSA-T), characterized by mid-to-late onset, moderate progression and moderate severity of clinical symptoms; and the early-onset slow progression subtype (MSA-ESP), characterized by early-to-mid onset, slow progression and mild clinical symptoms. Conclusions: We divided MSA into three subtypes and summarized the characteristics of each subtype. According to the clustering results, MSA patients were divided into three completely different types according to the severity of symptoms, the speed of disease progression, and the age of onset.