Efficacy and safety of butylphthalide in secondary prevention of stroke: study protocol for a multicenter, real world trial based on Internet.

Background: As one of the leading causes of morbidity and mortality, stroke and its recurrence has attracted more and more attention. Dl-3-n-butylphthalidle(NBP) has been widely used for treating acute ischemic stroke in China and shows a great clinical effect. NBP plays a role in different pathophysiological processes in the treatment of ischemic stroke, including antioxidants, anti-inflammatory, anti-apoptotic, anti-thrombosis, and mitochondrial protection.

Nervous system manifestations related to COVID-19 and their possible mechanisms.

In December 2019, the novel coronavirus disease (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection broke. With the gradual deepening understanding of SARS-CoV-2 and COVID-19, researchers and clinicians noticed that this disease is closely related to the nervous system and has complex effects on the central nervous system (CNS) and peripheral nervous system (PNS). In this review, we summarize the effects and mechanisms of SARS-CoV-2 on the nervous system, including the pathways of invasion, direct and indirect effects, and associated neuropsychiatric diseases, to deepen our knowledge and understanding of the relationship between COVID-19 and the nervous system.

Construction of an Assisted Model Based on Natural Language Processing for Automatic Early Diagnosis of Autoimmune Encephalitis.

Introduction: Early diagnosis and etiological treatment can effectively improve the prognosis of patients with autoimmune encephalitis (AE). However, anti-neuronal antibody tests which provide the definitive diagnosis require time and are not always abnormal. By using natural language processing (NLP) technology, our study proposes an assisted diagnostic method for early clinical diagnosis of AE and compares its sensitivity with that of previously established criteria.

Deep Learning-Based Classification of Cancer Cell in Leptomeningeal Metastasis on Cytomorphologic Features of Cerebrospinal Fluid.

Background: It is a critical challenge to diagnose leptomeningeal metastasis (LM), given its technical difficulty and the lack of typical symptoms. The existing gold standard of diagnosing LM is to use positive cerebrospinal fluid (CSF) cytology, which consumes significantly more time to classify cells under a microscope.

Objective: This study aims to establish a deep learning model to classify cancer cells in CSF, thus facilitating doctors to achieve an accurate and fast diagnosis of LM in an early stage.