One year after the onset of the coronavirus disease 2019 (COVID-19) pandemic, we aimed to summarize the frequency of neurologic manifestations reported in patients with COVID-19 and to investigate ...the association of these manifestations with disease severity and mortality.
We searched PubMed, Medline, Cochrane library, ClinicalTrials.gov, and EMBASE for studies from December 31, 2019, to December 15, 2020, enrolling consecutive patients with COVID-19 presenting with neurologic manifestations. Risk of bias was examined with the Joanna Briggs Institute scale. A random-effects meta-analysis was performed, and pooled prevalence and 95% confidence intervals (CIs) were calculated for neurologic manifestations. Odds ratio (ORs) and 95% CIs were calculated to determine the association of neurologic manifestations with disease severity and mortality. Presence of heterogeneity was assessed with
, meta-regression, and subgroup analyses. Statistical analyses were conducted in R version 3.6.2.
Of 2,455 citations, 350 studies were included in this review, providing data on 145,721 patients with COVID-19, 89% of whom were hospitalized. Forty-one neurologic manifestations (24 symptoms and 17 diagnoses) were identified. Pooled prevalence of the most common neurologic symptoms included fatigue (32%), myalgia (20%), taste impairment (21%), smell impairment (19%), and headache (13%). A low risk of bias was observed in 85% of studies; studies with higher risk of bias yielded higher prevalence estimates. Stroke was the most common neurologic diagnosis (pooled prevalence 2%). In patients with COVID-19 ≥60 years of age, the pooled prevalence of acute confusion/delirium was 34%, and the presence of any neurologic manifestations in this age group was associated with mortality (OR 1.80, 95% CI 1.11-2.91).
Up to one-third of patients with COVID-19 analyzed in this review experienced at least 1 neurologic manifestation. One in 50 patients experienced stroke. In those >60 years of age, more than one-third had acute confusion/delirium; the presence of neurologic manifestations in this group was associated with nearly a doubling of mortality. Results must be interpreted with the limitations of observational studies and associated bias in mind.
PROSPERO CRD42020181867.
Epilepsy, a neurological disease characterized by recurrent seizures, is highly heterogeneous in nature. Based on the prevalence, epilepsy is classified into two types: common and rare epilepsies. ...Common epilepsies affecting nearly 95% people with epilepsy, comprise generalized epilepsy which encompass idiopathic generalized epilepsy like childhood absence epilepsy, juvenile myoclonic epilepsy, juvenile absence epilepsy and epilepsy with generalized tonic-clonic seizure on awakening and focal epilepsy like temporal lobe epilepsy and cryptogenic focal epilepsy. In 70% of the epilepsy cases, genetic factors are responsible either as single genetic variant in rare epilepsies or multiple genetic variants acting along with different environmental factors as in common epilepsies. Genetic testing and precision treatment have been developed for a few rare epilepsies and is lacking for common epilepsies due to their complex nature of inheritance. Precision medicine for common epilepsies require a panoramic approach that incorporates polygenic background and other non-genetic factors like microbiome, diet, age at disease onset, optimal time for treatment and other lifestyle factors which influence seizure threshold. This review aims to comprehensively present a state-of-art review of all the genes and their genetic variants that are associated with all common epilepsy subtypes. It also encompasses the basis of these genes in the epileptogenesis. Here, we discussed the current status of the common epilepsy genetics and address the clinical application so far on evidence-based markers in prognosis, diagnosis, and treatment management. In addition, we assessed the diagnostic predictability of a few genetic markers used for disease risk prediction in individuals. A combination of deeper endo-phenotyping including pharmaco-response data, electro-clinical imaging, and other clinical measurements along with genetics may be used to diagnose common epilepsies and this marks a step ahead in precision medicine in common epilepsies management.
Heterogeneity in epilepsy often interferes with its diagnosis as well as treatment. To examine this heterogeneity at transcriptomic level, we performed whole-genome mRNA expression profiling in whole ...blood samples from 34 patients with epilepsy (PWE) (idiopathic, n = 13; cryptogenic, n = 9; and symptomatic, n = 12) and 41 healthy controls (HC) using Illumina HT-12 Expression Beadchip v4 microarray. In silico analysis using R software identified 165 genes to be significantly differentially expressed in PWE compared to HC (fold change>1.3, p < 0.05). Hierarchical clustering of resultant DEGs segregated idiopathic epilepsy from the rest of the epilepsy classes as well as HC. The class also displayed the most differential expression pattern with the highest number of DEGs among the three epilepsy classes. Gene ontology analysis revealed several biologically relevant inflammatory and other immune-related pathways. Our study provides insight into the relevance of altered blood gene expression patterns in understanding epilepsy and its etiologic classes.
•The multifactorial nature of epilepsy has until now limited the use of genetic identifiers.•We performed mRNA profiling on idiopathic, cryptogenic and symptomatic patients.•Inflammation and other immune-related pathways were altered in patients with epilepsy.•Patients with idiopathic epilepsy were most distinct compared to other epilepsy subtypes as well as healthy subjects.
The release of paused RNA polymerase II into productive elongation is highly regulated, especially at genes that affect human development and disease. To exert control over this rate-limiting step, ...we designed sequence-specific synthetic transcription elongation factors (Syn-TEFs). These molecules are composed of programmable DNA-binding ligands flexibly tethered to a small molecule that engages the transcription elongation machinery. By limiting activity to targeted loci, Syn-TEFs convert constituent modules from broad-spectrum inhibitors of transcription into gene-specific stimulators. Here we present Syn-TEF1, a molecule that actively enables transcription across repressive GAA repeats that silence frataxin expression in Friedreich’s ataxia, a terminal neurodegenerative disease with no effective therapy. The modular design of Syn-TEF1 defines a general framework for developing a class of molecules that license transcription elongation at targeted genomic loci.
Spinocerebellar ataxia type 12 (SCA12) is a rare neurodegenerative disorder caused by CAG repeat expansion in the PPP2R2B gene. Previously, the causal length of CAG repeats ascribed to SCA12 was more ...than 51; however, a few reports have also described unusual occurrence of CAG repeat length 36-51 repeats among patients of different geographical population, with atypical clinical association. From our systematic search for SCA12 in a genetic screening programme, we have identified a large number of SCA12 cases. In this study, we specifically describe the clinical behaviour of 18 patients who harbour CAG repeats in the range of 43-50 and compare their clinical behaviour with patients carrying typical pathogenic threshold length of 51 CAG repeats. Unsurprisingly, we observed that the clinical characteristics were similar to those of typical SCA12 phenotype, with large variability in the age at onset. Radiologically, we observed a variable degree of cerebro-cerebellar degeneration along with white matter changes that do not correlate with the disease severity. We define a new pathogenic threshold of CAG-43 to be pathogenic for SCA12 diagnosis and also describe the clinical profiles of two biallelic CAG expansion carriers. We also propose that SCA12 might not be that restricted in terms of occurrence in other geographical or ethnic populations, as it was previously presumed to be.
Early prognostication of patient outcomes in intracerebral hemorrhage (ICH) is critical for patient care. We aim to investigate protein biomarkers' role in prognosticating outcomes in ICH patients. ...We assessed 22 protein biomarkers using targeted proteomics in serum samples obtained from the ICH patient dataset (N = 150). We defined poor outcomes as modified Rankin scale score of 3-6. We incorporated clinical variables and protein biomarkers in regression models and random forest-based machine learning algorithms to predict poor outcomes and mortality. We report Odds Ratio (OR) or Hazard Ratio (HR) with 95% Confidence Interval (CI). We used five-fold cross-validation and bootstrapping for internal validation of prediction models. We included 149 patients for 90-day and 144 patients with ICH for 180-day outcome analyses. In multivariable logistic regression, UCH-L1 (adjusted OR 9.23; 95%CI 2.41-35.33), alpha-2-macroglobulin (aOR 5.57; 95%CI 1.26-24.59), and Serpin-A11 (aOR 9.33; 95%CI 1.09-79.94) were independent predictors of 90-day poor outcome; MMP-2 (aOR 6.32; 95%CI 1.82-21.90) was independent predictor of 180-day poor outcome. In multivariable Cox regression models, IGFBP-3 (aHR 2.08; 95%CI 1.24-3.48) predicted 90-day and MMP-9 (aOR 1.98; 95%CI 1.19-3.32) predicted 180-day mortality. Machine learning identified additional predictors, including haptoglobin for poor outcomes and UCH-L1, APO-C1, and MMP-2 for mortality prediction. Overall, random forest models outperformed regression models for predicting 180-day poor outcomes (AUC 0.89), and 90-day (AUC 0.81) and 180-day mortality (AUC 0.81). Serum biomarkers independently predicted short-term poor outcomes and mortality after ICH. Further research utilizing a multi-omics platform and temporal profiling is needed to explore additional biomarkers and refine predictive models for ICH prognosis.
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