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A pandemic due to novel coronavirus arose in mid-December 2019 in Wuhan, China, and in 3 months’ time swept the world. The disease has been referred to as COVID-19, and the causative agent has been labelled SARS-CoV-2 due to its genetic similarities to the virus (SARS-CoV-1) responsible for the severe acute respiratory syndrome (SARS) epidemic nearly 20 years earlier. The spike proteins of both viruses dictate tissue tropism using the angiotensin-converting enzyme type 2 (ACE-2) receptor to bind to cells. The ACE-2 receptor can be found in nervous system tissue and endothelial cells among the tissues of many other organs. Neurological complications have been observed with COVID-19. Myalgia and headache are relatively common, but serious neurological disease appears to be rare. No part of the neuraxis is spared. The neurological disorders occurring with COVID-19 may have many pathophysiological underpinnings. Some appear to be the consequence of direct viral invasion of the nervous system tissue, others arise as a postviral autoimmune process, and still others are the result of metabolic and systemic complications due to the associated critical illness. This review addresses the preliminary observations regarding the neurological disorders reported with COVID-19 to date and describes some of the disorders that are anticipated from prior experience with similar coronaviruses.

We assessed the performance of metagenomic next-generation sequencing (mNGS) in the diagnosis of infectious encephalitis and meningitis. This was a prospective multicenter study. Cerebrospinal fluid samples from patients with viral encephalitis and/or meningitis, tuberculous meningitis, bacterial meningitis, fungal meningitis, and non-central nervous system (CNS) infections were subjected to mNGS. In total, 213 patients with infectious and non-infectious CNS diseases were finally enrolled from November 2016 to May 2019; the mNGS-positive detection rate of definite CNS infections was 57.0%. At a species-specific read number (SSRN) ≥2, mNGS performance in the diagnosis of definite viral encephalitis and/or meningitis was optimal (area under the curve [AUC] = 0.659, 95% confidence interval [CI] = 0.566-0.751); the positivity rate was 42.6%. At a genus-specific read number ≥1, mNGS performance in the diagnosis of tuberculous meningitis (definite or probable) was optimal (AUC=0.619, 95% CI=0.516-0.721); the positivity rate was 27.3%. At SSRNs ≥5 or 10, the diagnostic performance was optimal for definite bacterial meningitis (AUC=0.846, 95% CI = 0.711-0.981); the sensitivity was 73.3%. The sensitivities of mNGS (at SSRN ≥2) in the diagnosis of cryptococcal meningitis and cerebral aspergillosis were 76.92 and 80%, respectively. mNGS of cerebrospinal fluid effectively identifies pathogens causing infectious CNS diseases. mNGS should be used in conjunction with conventional microbiological testing. Chinese Clinical Trial Registry, ChiCTR1800020442.

Pseudorabies virus (PRV) primarily infects swine but can infect cattle, dogs, and cats. Several studies have reported that PRV can cross the specie barrier and induce human encephalitis, but a definitive diagnosis of human PRV encephalitis is debatable due to the lack of PRV DNA detection. Here, we report a case of human PRV encephalitis diagnosed by the next-generation sequencing (NGS) of PRV sequences in the cerebrospinal fluid (CSF) of a patient. A male pork vendor developed fever and seizures for 6 days. NGS results showed PRV sequences in his CSF and blood. Sanger sequencing showed that PRV DNA in the CSF and PRV antibodies in both the CSF and blood were positive. MRI results revealed multiple inflammatory lesions in the bilateral hemisphere. Based on the clinical and laboratory data, we diagnosed the patient with PRV encephalitis. This case suggests that PRV can infect humans, causing severe viral encephalitis. People at risk of PRV infection should improve their self-protection awareness.

ABSTRACT Background Glial fibrillary acidic protein‐immunoglobulin G (GFAP‐IgG) positivity is associated with autoimmune GFAP astrocytopathy (GFAP‐A), but also with other autoimmune encephalitides and viral infections. We attempted to elucidate the characteristics of GFAP‐A in relation to other GFAP‐IgG‐positive encephalitides and constructed a differential diagnosis model. Methods 141 GFAP‐IgG‐positive cases were identified, including 52 astrocytopathy (GFAP‐A group), 48 autoimmune encephalitis (AE‐G), and 41 viral encephalitis (VE‐G). Multivariate logistic regression was employed to create a diagnostic model, with validation using an external cohort. Result Compared to the AE‐G group, the GFAP‐A patients showed more onset age ≥ 50 years, headache, fever, consciousness disturbance, MRI radial vascular enhancement, cerebrospinal fluid (CSF) antibody titer grade ≥ 4, and CSF proteins ≥ 700 mg/L, but less female sex, limb numbness, visual disturbances, and CSF chloride ≤ 120 mmol/L. Among these, CSF antibody titer grade ≥ 4, CSF protein ≥ 700 mg/L, and absence of visual disturbances were independent risk factors for GFAP‐A diagnosis. Compared to the VE‐G group, the GFAP‐A patients showed more course ≥ 14 days, onset age ≥ 50 years, limb weakness, serum potassium ≤ 3.9 mmol/L, CSF antibody titer grade ≥ 4, CSF leukocytes ≤ 46*10, MRI radial vascular enhancement, MRI involvement of brainstem, and MRI involvement of spinal cord, but less headache, fever, nausea, and vomiting. Among these, serum potassium ≤ 3.9 mmol/L, MRI spinal cord involvement, and absence of nausea and vomiting were independent risk factors for GFAP‐A diagnosis. Conclusions Based on critical clinical indicators identified, we constructed a differential diagnosis model for GFAP‐A. To optimize the diagnostic process for GFAP‐A, this study analyzed and compared the clinical features of 141 cases of GFAP‐IgG‐positive encephalitis and constructed a differential diagnostic model to distinguish GFAP‐A from autoimmune encephalitis and viral encephalitis.

Metagenomic next-generation sequencing (NGS) was used to diagnose an unusual and fatal case of progressive encephalitis in an immunocompromised adult presenting at disease onset as bilateral hearing loss. The sequencing and confirmatory studies revealed neuroinvasive infection of the brain by an astrovirus belonging to a recently discovered VA/HMO clade.

Dengue is the second most common mosquito-borne disease affecting human beings. In 2009, WHO endorsed new guidelines that, for the first time, consider neurological manifestations in the clinical case classification for severe dengue. Dengue can manifest with a wide range of neurological features, which have been noted—depending on the clinical setting—in 0·5–21% of patients with dengue admitted to hospital. Furthermore, dengue was identified in 4–47% of admissions with encephalitis-like illness in endemic areas. Neurological complications can be categorised into dengue encephalopathy (eg, caused by hepatic failure or metabolic disorders), encephalitis (caused by direct virus invasion), neuromuscular complications (eg, Guillain-Barré syndrome or transient muscle dysfunctions), and neuro-ophthalmic involvement. However, overlap of these categories is possible. In endemic countries and after travel to these regions, dengue should be considered in patients presenting with fever and acute neurological manifestations.

Background. An 18-month-old boy developed encephalopathy, for which extensive investigation failed to identify an etiology, 6 weeks after stem cell transplant. To exclude a potential infectious cause, we performed high-throughput RNA sequencing on brain biopsy. Methods. RNA-Seq was performed on an Illumina Miseq, generating 20 million paired-end reads. Nonhost data were checked for similarity to known organisms using BLASTx. The full viral genome was sequenced by primer walking. Results. We identified an astrovirus, HAstV-VA1/HMO-C-UK1(a), which was highly divergent from human astrovirus (HAstV 1–8) genotypes, but closely related to VA1/HMO-C astroviruses, including one recovered from a case of fatal encephalitis in an immunosuppressed child. The virus was detected in stool and serum, with highest levels in brain and cerebrospinal fluid (CSF). Immunohistochemistry of the brain biopsy showed positive neuronal staining. A survey of 680 stool and 349 CSF samples identified a related virus in the stool of another immunosuppressed child. Conclusions. The discovery of HAstV-VA1/HMO-C-UK1(a) as the cause of encephalitis in this case provides further evidence that VA1/HMO-C viruses, unlike HAstV 1–8, are neuropathic, particularly in immunocompromised patients, and should be considered in the differential diagnosis of encephalopathy. With a turnaround from sample receipt to result of <1 week, we confirm that RNA-Seq presents a valuable diagnostic tool in unexplained encephalitis.

PurposeInvestigation of undiagnosed cases of infectious neurological diseases, especially in the paediatric population, remains a challenge. This study aimed to enhance understanding of viruses in CSF from children with clinically diagnosed meningitis and/or encephalitis (M/ME) of unknown aetiology using shotgun sequencing enhanced by hybrid capture (HCSS).MethodsA single-centre prospective study was conducted at Sant Joan de Déu University Hospital, Barcelona, involving 40 M/ME episodes of unknown aetiology, recruited from May 2021 to July 2022. All participants had previously tested negative with the FilmArray Meningitis/Encephalitis Panel.HCSS was used to detect viral nucleic acid in the patients’ CSF. Sequencing was performed on Illumina NovaSeq platform. Raw sequence data were analysed using CZ ID metagenomics and PikaVirus bioinformatics pipelines.ResultsForty episodes of M/ME of unknown aetiology in 39 children were analysed by HCSS. A significant viral detection in 30 CSF samples was obtained, including six parechovirus A, three enterovirus ACD, four polyomavirus 5, three HHV-7, two BKV, one HSV-1, one VZV, two CMV, one EBV, one influenza A virus, one rhinovirus, and 13 HERV-K113 detections. Of these, one sample with BKV, three with HHV-7, one with EBV, and all HERV-K113 were confirmed by specific PCR. The requirement for Intensive Care Unit admission was associated with HCSS detections.ConclusionThis study highlights HCSS as a powerful tool for the investigation of undiagnosed cases of M/ME. Data generated must be carefully analysed and reasonable precautions must be taken before establishing association of clinical features with unexpected or novel virus findings.

Background A score to differentiate autoimmune (AE) and viral encephalitis (VE) early upon admission has recently been developed but needed external validation. The objective of this study was to evaluate the performance of the score in a larger and more diagnostically diverse patient cohort. Methods We conducted a retrospective nationwide and population-based cohort study including all adults with encephalitis of definite viral (2015–2022) or autoimmune aetiology (2009–2022) in Denmark. Variables included in the score-model were extracted from patient records and individual risk scores were assessed. The performance of the score was assessed by receiver-operating characteristics (ROC) curve analyses and calculation of the area under the curve (AUC). Results A total of 496 patients with encephalitis [AE n  = 90, VE n  = 287 and presumed infectious encephalitis (PIE) n  = 119] were included in the study. The score was highly accurate in predicting cases of AE reaching an AUC of 0.94 (95% CI 0.92–0.97). Having a score ≥ 3 predicted AE with a PPV of 87% and an NPV of 91%. The risk score was found to perform well across aetiological subgroups and applied to the PIE cohort resulted in an AUC of 0.88 (95% CI 0.84–0.93). Conclusion The excellent performance of the score as reported in the development study was confirmed in this significantly larger and more diverse cohort of patients with encephalitis in Denmark. These results should prompt further prospective testing with wider inclusion criteria.

Background Distinguishing between viral encephalitis (VE) and autoimmune limbic encephalitis (ALE) presents a clinical challenge due to the overlap in symptoms. We aimed to develop and validate a diagnostic prediction model to differentiate VE and ALE. Methods A prospective observational multicentre cohort study, which continuously enrolled patients diagnosed with either ALE or VE from October 2011 to April 2023. The demographic data, clinical features, and laboratory test results were collected and subjected to logistic regression analyses. The model was displayed as a web-based nomogram and then modified into a scored prediction tool. Model performance was assessed in both derivation and external validation cohorts. Results A total of 2423 individuals were recruited, and 1001 (496 VE, 505 ALE) patients were included. Based on the derivation cohort (389 VE, 388 ALE), the model was developed with eight variables including age at onset, acuity, fever, headache, nausea/vomiting, psychiatric or memory complaints, status epilepticus, and CSF white blood cell count. The model showed good discrimination and calibration in both derivation (AUC 0.890; 0.868–0.913) and external validation (107 VE, 117 ALE, AUC 0.872; 0.827–0.917) cohorts. The scored prediction tool had a total point that ranged from − 4 to 10 also showing good discrimination and calibration in both derivation (AUC 0.885, 0.863–0.908) and external validation (AUC 0.868, 0.823–0.913) cohorts. Conclusions The prediction model provides a reliable and user-friendly tool for differentiating between the VE and ALE, which would benefit early diagnosis and appropriate treatment and alleviate economic burdens on both patients and society.