Treatment of depression may be inadequately prioritized in the management of intractable epilepsy.

Prenatal nutritional deficiency may play a role in the origin of some cases of schizophrenia.

Lesion location and stroke subtype are strong determinants of ES risk, even after adjusting for stroke severity. ES does not predict 30-day mortality. SE occurs in more than one-quarter of patients with ES.

Background and Purpose This study evaluated the use of an artificial intelligence (AI) platform on mobile devices in measuring and increasing medication adherence in stroke patients on anticoagulation therapy. The introduction of direct oral anticoagulants (DOACs), while reducing the need for monitoring, have also placed pressure on patients to self-manage. Suboptimal adherence goes undetected as routine laboratory tests are not reliable indicators of adherence, placing patients at increased risk of stroke and bleeding. Methods A randomized, parallel-group, 12-week study was conducted in adults (n = 28) with recently diagnosed ischemic stroke receiving any anticoagulation. Patients were randomized to daily monitoring by the AI Platform (intervention) or to no daily monitoring (control). The AI application visually identified the patient, the medication and confirmed ingestion. Adherence was measured by pill counts and plasma sampling in both groups. Results For all patients (n = 28), mean (standard deviation [SD]) age was 57 (13.2) years and 53.6% were female. Mean (SD) cumulative adherence based on the AI Platform was 90.5% (7.5%). Plasma drug concentration levels indicated that adherence was 100% (15 of 15) and 50% (6 of 12) in the intervention and control groups, respectively. Conclusions Patients, some with little experience using a smartphone, successfully used the technology and demonstrated a 50% improvement in adherence based on plasma drug concentration levels. For patients receiving DOACs, absolute improvement increased to 67%. Real-time monitoring has the potential to increase adherence and change behavior, particularly in patients on DOAC therapy. Clinical Trial Registration-URL: http://www.clinicaltrials.gov. Unique identifier: NCT02599259.

ICH is a heterogeneous disease with deep and lobar subtypes distinguishable on an epidemiologic basis. The different patterns of these two subtypes in our race-ethnically diverse population lend credence to the notion that ICH should no longer be treated as a single entity.

Summary Introduction The discovery of disease-associated loci through genome-wide association studies (GWAS) is the leading approach to the identification of novel biological pathways for human disease. To date, GWAS have had been limited by relatively small sample sizes and yielded relatively few loci associated with ischemic stroke The National Institute of Neurological Disorders Stroke Genetics Network (NINDS-SiGN) is an international consortium that has taken a systematic approach to phenotyping and produced the largest ischemic stroke GWAS to date. Methods In order to identify genetic loci associated with ischemic stroke, we performed a two-stage genome-wide association study. The first stage consisted of 16,851 cases with state-of-the-art phenotyping and 32,473 stroke-free controls. Cases were aged 16 to 104 years, recruited between 1989 and 2012, and subtyped by centrally trained and certified investigators using the web-based protocol, Causative Classification of Stroke (CCS). We constructed case-control strata by identify samples genotyped on (nearly) identical arrays and of similar genetic ancestral background. Data was cleaned and imputed using dense imputation reference panels generated from whole-genome sequence data. Genome-wide testing was performed within each stratum for each available phenotype, and summary level results were combined using inverse variance-weighted fixed effects meta-analysis. The second stage consisted of in silico look-ups of 1,372 SNPs in 20,941 cases and 364,736 stroke-free controls, with cases previously subtyped using the TOAST classification system according to local standards. The two stages were then jointly analyzed in a final meta-analysis. Findings We identified a novel locus at 1p13.2 near TSPAN2 associated with large artery atherosclerosis (LAA)-related stroke (stage I OR for the G allele at rs12122341 = 1·21, p = 4.50 × 10−8; stage II OR = 1·19, p = 1·30 × 10−9). We also confirmed four loci robustly associated with ischemic stroke and reported in prior studies, including PITX2 and ZFHX3 for cardioembolic stroke, and HDAC9 for LAA stroke. The 12q24 locus near ALDH2, originally associated with all ischemic stroke but not with any specific subtype, exceeded genome-wide significance in the meta-analysis of small artery stroke. Other loci, including NINJ2, were not confirmed. Interpretation Our results identify a novel LAA-stroke susceptibility gene and now indicate that all loci implicated by GWAS to date are subtype specific. Follow-up studies will be necessary to determine whether the locus near TSPAN2 yields a novel therapeutic approach to stroke prevention. Given the subtype-specificity of these associations, the rich phenotyping available in SiGN is likely to prove vital for further genetic discovery in ischemic stroke. Funding National Institute of Neurological Disorders and Stroke (NINDS), National Institutes of Health (NIH).

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Objective:The SARS-Cov2 virus is protean in its manifestations, affecting nearly every organ system. However, nervous system involvement and its impact on disease outcome are poorly characterized. The objective of the study is to determine if neurological syndromes are associated with increased risk of inpatient mortality.Methods:581 hospitalized patients with confirmed SARS-Cov2 infection, neurological involvement and brain-imaging were compared to hospitalized non-neurological COVID-19 patients. Four patterns of neurological manifestations were identified –acute stroke, new or recrudescent seizures, altered mentation with normal imaging, and neuro-COVID-19 complex. Factors present on admission were analyzed as potential predictors of in-hospital mortality, including sociodemographic variables, pre-existing comorbidities, vital-signs, laboratory values, and pattern of neurological manifestations. Significant predictors were incorporated into a disease-severity score. Patients with neurological manifestations were matched with patients of the same age and disease severity to assess the risk of death.Results:4711 patients with confirmed SARS-Cov2 infection were admitted to one medical system in New York City during a 6-week period. Of these, 581 (12%) had neurological issues of sufficient concern to warrant neuro-imaging. These patients were compared to 1743 non-neurological COVID-19 patients matched for age and disease-severity admitted during the same period. Patients with altered mentation (n=258, p =0.04, OR 1.39, CI 1.04 – 1.86) or radiologically confirmed stroke (n=55, p = 0.001, OR 3.1, CI 1.65-5.92) had a higher risk of mortality than age and severity-matched controls.Conclusions:The incidence of altered mentation or stroke on admission predicts a modest but significantly higher risk of in-hospital mortality independent of disease severity. While other biomarker factors also predict mortality, measures to identify and treat such patients may be important in reducing overall mortality of COVID-19.