Viral encephalitis: a practical review on diagnostic approach and treatment

Temporal transformer-spatial graph convolutional network: an intelligent classification model for anti N-methyl-D-aspartate receptor encephalitis based on electroencephalogram signal

Encephalitis is a disease typically caused by viral infections or autoimmunity. The most common type of autoimmune encephalitis is anti-N-methyl-D-aspartate receptor (NMDAR) antibody-mediated, known as anti-NMDA receptor encephalitis, which is a rare disease. Specific EEG patterns, including "extreme delta brush" (EDB), have been reported in patients with anti-NMDA receptor encephalitis. The aim of this study was to develop an intelligent diagnostic model for encephalitis based on EEG signals. A total of 131 Participants were selected based on reasonable inclusion criteria and divided into three groups: health control (35 participants), viral encephalitis (58 participants), and anti NMDAR receptor encephalitis (55 participants). Due to the low prevalence of anti-NMDAR receptor encephalitis, it took several years to collect participants' EEG signals while they were in an awake state. EEG signals were collected and analyzed following the international 10-20 system layout. We proposed a model called Temporal Transformer-Spatial Graph Convolutional Network (TT-SGCN), which consists of a Preprocess Module, a Temporal Transformer Module (TTM), and a Spatial Graph Convolutional Module (SGCM). The raw EEG signal was preprocessed according to traditional procedures, including filtering, averaging, and Independent Component Analysis (ICA) method. The EEG signal was then segmented and transformed using short-time Fourier transform (STFT) to produce concatenated power density (CPD) maps, which served as inputs for the proposed model. TTM extracted the time-frequency features of each channel, and SGCM fused these features using graph convolutional methods based on the location of electrodes. The model was evaluated in two experiments: classification of the three groups and pairwise classification among the three groups. The model was trained using two stages and achieved the performance, with an accuracy of 82.23%, recall of 80.75%, precision of 82.51%, and F1 score of 81.23% in the classification of the three groups. The proposed model has the potential to become an intelligent auxiliary diagnostic tool for encephalitis.

Neurological sequelae after encephalitis associated with herpes simplex virus in children: systematic review and meta-analysis

BACKGROUND

Encephalitis is an inflammation of the cerebral parenchyma manifested by acute symptoms such as fever, headaches, and other neurological disorders. Its etiology is mostly viral, with herpes simplex virus being a frequent etiological agent in children. The development of neurological sequelae is a serious outcome associated with this infection.

OBJECTIVE

To assess the general prevalence and types of neurological sequelae in children after a case of acute viral encephalitis caused by HSV.

METHODS

This systematic review and meta-analysis was developed following the PRISMA guidelines. The literature search was carried out in the MEDLINE, Embase, SciELO, LILACS, Cochrane, CINAHL, PsycINFO, and Web of Science databases. Studies were included of children with confirmed HSV infection and that presented a description of neurological sequelae associated with that infection. For the meta-analysis of general prevalence and of the types of neurological sequelae a random effects model was used.

RESULTS

Of the 2827 articles chosen in the initial search, nine studies were included in the systematic review and meta-analysis. The general prevalence of neurological sequelae was 50.7% (95% CI 39.2-62.2). The most frequent sequelae were related to mental disability, with a 42.1% prevalence (95% CI 30-55.2); on the other hand, the least frequent sequelae were those related with visual impairment, with a 5.9% prevalence (95% CI 2.2-14.6). The included studies presented regular quality and substantial heterogeneity.

CONCLUSION

Even with antiviral therapy, half of patients will develop some type of disability.

An Intelligent Healthcare Cyber Physical Framework for Encephalitis Diagnosis Based on Information Fusion and Soft-Computing Techniques

Viral encephalitis is a contagious disease that causes life insecurity and is considered one of the major health concerns worldwide. It causes inflammation of the brain and, if left untreated, can have persistent effects on the central nervous system. Conspicuously, this paper proposes an intelligent cyber-physical healthcare framework based on the IoT-fog-cloud collaborative network, employing soft-computing technology and information fusion. The proposed framework uses IoT-based sensors, electronic medical records, and user devices for data acquisition. The fog layer, composed of numerous nodes, processes the most specific encephalitis symptom-related data to classify possible encephalitis cases in real time to issue an alarm when a significant health emergency occurs. Furthermore, the cloud layer involves a multi-step data processing scheme for in-depth data analysis. First, data obtained across multiple data generation sources are fused to obtain a more consistent, accurate, and reliable feature set. Data preprocessing and feature selection techniques are applied to the fused data for dimensionality reduction over the cloud computing platform. An adaptive neuro-fuzzy inference system is applied in the cloud to determine the risk of a disease and classify the results into one of four categories: no risk, probable risk, low risk, and acute risk. Moreover, the alerts are generated and sent to the stakeholders based on the risk factor. Finally, the computed results are stored in the cloud database for future use. For validation purposes, various experiments are performed using real-time datasets. The analysis results performed on the fog and cloud layers show higher performance than the existing models. Future research will focus on the resource allocation in the cloud layer while considering various security aspects to improve the utility of the proposed work.

Central neuroinflammation in Covid-19: a systematic review of 182 cases with encephalitis, acute disseminated encephalomyelitis, and necrotizing encephalopathies

Growing evidence demonstrates the association of encephalitis, meningoencephalitis or encephalomyelitis, with SARS-CoV-2 infection. This study aims to determine the profile and possible mechanisms behind CNS inflammatory diseases in the context of Covid-19. We conducted a systematic review of case reports on Covid-19-related encephalitis, meningoencephalitis, acute necrotizing encephalitis, and acute disseminated encephalomyelitis in adults, published before January 2021. A total of 182 cases (encephalitis = 109, meningoencephalitis = 26, acute disseminated encephalomyelitis = 35, acute necrotizing (hemorrhagic) encephalitis = 12) were included. While cerebrospinal fluid (CSF) pleocytosis and increased protein level was present in less than 50%, magnetic resonance imaging (MRI) and electroencephalogram (EEG) were abnormal in 78 and 93.2% of all cases, respectively. Viral particles were detected in cerebrospinal fluid of only 13 patients and autoantibodies were present in seven patients. All patients presented with altered mental status, either in the form of impaired consciousness or psychological/cognitive decline. Seizure, cranial nerve signs, motor, and reflex abnormalities were among associated symptoms. Covid-19-associated encephalitis presents with a distinctive profile requiring thorough diagnosis and thereby a comprehensive knowledge of the disease. The clinical profile of brain inflammation in Covid-19 exhibits majority of abnormal imaging and electroencephalography findings with mild/moderate pleocytosis or proteinorrhachia as prevalent as normal cerebrospinal fluid (CSF). Oligoclonal bands and autoantibody assessments are useful in further evaluating neuro-covid patients, as supported by our pooled evidence. Despite the possibility that direct viral invasion cannot be easily estimated, it is still more likely that immune-mediated or autoimmune reactions play a more important role in SARS-CoV-2 neuroinflammation.

Differential Diagnosis and Hospital Emergency Management for Fastlane Treatment of Central Nervous System Infection Under the COVID-19 Epidemic in Changsha, China

Importance: Corona virus disease 2019 (COVID-19) has long latent period, strong infectivity, and non-specific symptoms and signs in the upper respiratory tract. Some initial neurological symptoms appear, including dizziness, headache, seizures, slurred speech, disturbance of consciousness, and limb paralysis among a few COVID-19 patients, which share similar manifestations with central nervous system (CNS) infection. Improving the diagnostic efficiency of suspected CNS infection patients on the basis of preventing and controlling COVID-19 plays a key role in preventing nosocomial and cross infections. This study intends to formulate a hospital emergency management system of fastlane treatment of CNS infection for epidemic prevention and control, aiming at providing references and guidelines for the government and medical institutions to improve the efficiency of treating CNS infection patients in the clinical practice during COVID-19. Observations: This study formulated a framework of a fastlane treatment of CNS infection based on the cooperation of resources and experience, aiming at the key and difficult problems faced by the hospital emergency management system during the COVID-19 outbreak in Changsha, China. The main problem of formulating the hospital emergency management system is efficiently identifying whether CNS infection was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The framework improves the efficiency of diagnosing and treating CNS infections by standardizing the diagnosis and treatment process of patients in emergency observation and strengthening the management of inpatient wards, aiming at assisting medical staff during clinical practice. Conclusions and Relevance: The hospital emergency management system of a fastlane treatment of CNS infection for epidemic prevention and control of the COVID-19 outbreak is a professional and multisystem project, which needs the cooperation of various resources and the experience of clinical leadership.