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. Many randomized, double-blind, placebo-controlled, multicenter clinical trials suggest that NBP is a safe and effective treatment for ischemic stroke. To sum up, the current research is mainly focused on the short-term treatment of stroke patients with RCT (randomized controlled trial). Therefore, we designed this study to confirm the role of butylphthalide in secondary stroke prevention in the real world. METHODS: This study will be a multicenter, prospective real-world trial. We would recruit 8000 patients with ischemic stroke from 78 public hospitals in China. All participants will be allocated to one of two parallel treatment groups according to their own wills: (1) butylphthalide group: 0.2 g of butylphthalide capsules three times daily plus routine treatment (aspirin 50-300 mg/d, clopidogrel 75 mg/d, etc.); (2) control group: routine treatment (aspirin 50-300 mg/d, clopidogrel 75 mg/d, etc.). Treatment duration is 90 consecutive days or more. The primary outcome is recurrence rate of stroke within 1 month, 3 months, 6 months and 1 year in butylphthalide group and control group. The secondary outcomes included NIHSS score, the mRS score, other clinical cardiovascular events within one year (sudden death / myocardial infarction / arrhythmia / heart failure, etc.), and adverse events of patients in groups. NIHSS will be captured in the first month after discharge, and the others will be captured at the same time points as the primary end point. DISCUSSION: This trial will be exploring the efficacy and safety of butylphthalide in secondary prevention of ischemic stroke to expand the scope of application of butylphthalide soft capsules and provide new ideas for enriching the secondary prevention of stroke. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR). TRIAL REGISTRATION NUMBER: ChiCTR2000034481. Registered on 6 July 2020, http://www.chictr.org.cn/showproj.aspx?proj=55800.

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. METHODS: Our model is based on the text classification model trained by the history of present illness (HPI) in electronic medical records (EMRs) that present a definite pathological diagnosis of AE or infectious encephalitis (IE). The definitive diagnosis of IE was based on the results of traditional etiological examinations. The definitive diagnosis of AE was based on the results of neuronal antibodies, and the diagnostic criteria of definite autoimmune limbic encephalitis proposed by Graus et al. used as the reference standard for antibody-negative AE. First, we automatically recognized and extracted symptoms for all HPI texts in EMRs by training a dataset of 552 cases. Second, four text classification models trained by a dataset of 199 cases were established for differential diagnosis of AE and IE based on a post-structuring text dataset of every HPI, which was completed using symptoms in English language after the process of normalization of synonyms. The optimal model was identified by evaluating and comparing the performance of the four models. Finally, combined with three typical symptoms and the results of standard paraclinical tests such as cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), or electroencephalogram (EEG) proposed from Graus criteria, an assisted early diagnostic model for AE was established on the basis of the text classification model with the best performance. RESULTS: The comparison results for the four models applied to the independent testing dataset showed the naïve Bayesian classifier with bag of words achieved the best performance, with an area under the receiver operating characteristic curve of 0.85, accuracy of 84.5% (95% confidence interval [CI] 74.0-92.0%), sensitivity of 86.7% (95% CI 69.3-96.2%), and specificity of 82.9% (95% CI 67.9-92.8%), respectively. Compared with the diagnostic criteria proposed previously, the early diagnostic sensitivity for possible AE using the assisted diagnostic model based on the independent testing dataset was improved from 73.3% (95% CI 54.1-87.7%) to 86.7% (95% CI 69.3-96.2%). CONCLUSIONS: The assisted diagnostic model could effectively increase the early diagnostic sensitivity for AE compared to previous diagnostic criteria, assist physicians in establishing the diagnosis of AE automatically after inputting the HPI and the results of standard paraclinical tests according to their narrative habits for describing symptoms, avoiding misdiagnosis and allowing for prompt initiation of specific treatment.

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. Method: The cerebrospinal fluid laboratory of Xijing Hospital provides 53,255 cells from 90 LM patients in the research. We used two deep convolutional neural networks (CNN) models to classify cells in the CSF. A five-way cell classification model (CNN1) consists of lymphocytes, monocytes, neutrophils, erythrocytes, and cancer cells. A four-way cancer cell classification model (CNN2) consists of lung cancer cells, gastric cancer cells, breast cancer cells, and pancreatic cancer cells. Here, the CNN models were constructed by Resnet-inception-V2. We evaluated the performance of the proposed models on two external datasets and compared them with the results from 42 doctors of various levels of experience in the human-machine tests. Furthermore, we develop a computer-aided diagnosis (CAD) software to generate cytology diagnosis reports in the research rapidly. Results: With respect to the validation set, the mean average precision (mAP) of CNN1 is over 95% and that of CNN2 is close to 80%. Hence, the proposed deep learning model effectively classifies cells in CSF to facilitate the screening of cancer cells. In the human-machine tests, the accuracy of CNN1 is similar to the results from experts, with higher accuracy than doctors in other levels. Moreover, the overall accuracy of CNN2 is 10% higher than that of experts, with a time consumption of only one-third of that consumed by an expert. Using the CAD software saves 90% working time of cytologists. Conclusion: A deep learning method has been developed to assist the LM diagnosis with high accuracy and low time consumption effectively. Thanks to labeled data and step-by-step training, our proposed method can successfully classify cancer cells in the CSF to assist LM diagnosis early. In addition, this unique research can predict cancer's primary source of LM, which relies on cytomorphologic features without immunohistochemistry. Our results show that deep learning can be widely used in medical images to classify cerebrospinal fluid cells. For complex cancer classification tasks, the accuracy of the proposed method is significantly higher than that of specialist doctors, and its performance is better than that of junior doctors and interns. The application of CNNs and CAD software may ultimately aid in expediting the diagnosis and overcoming the shortage of experienced cytologists, thereby facilitating earlier treatment and improving the prognosis of LM.