Cerebral microbleeds (CMBs) as visualized on gradient echo T2* or susceptibility-weighted magnetic resonance imaging (MRI) are considered markers of bleeding-prone cerebral vessel microangiopathies and constitute both significant and independent predictors of future cerebral bleeding.^1-3 Several clinical studies have associated CMB presence with hemorrhagic stroke and hemorrhagic complications following antithrombotic medications.⁴

Myelin basic protein (MBP) represents a candidate autoantigen in multiple sclerosis (MS). We isolated MBP from normal and MS human white matter and purified six components (charge isomers) to compare the post-translational modifications on each. The sites and extent of methylation, deimination, and phosphorylation were documented for all tryptic peptides by mass spectrometry. We found that mono and dimethylated arginine 107 was increased in MS samples; deimination of arginine occurred at a number of sites and was elevated in MS; phosphorylation was observed in 10 peptides in normal samples but was greatly reduced or absent in most peptides from MS samples. Data obtained with MBP isolated from fresh brain obtained from a spontaneously demyelinating mouse model supported the view that the changes observed in human brain were probably related to pathogenesis of demyelination, i.e. we found decreased phosphorylation and decreased amounts of glycogen synthesis kinase in brain homogenates using specific antibodies. This study represents the first to define post-translational modifications in demyelinating disease and suggest an important role in pathogenesis.

Background and Purpose—: Shortening door-to-needle time may lead to inadvertent intravenous thrombolysis (IVT) administration in stroke mimics (SMs). We sought to determine the safety of IVT in SMs using prospective, single-center data and by conducting a comprehensive meta-analysis of reported case-series. Methods—: We prospectively analyzed consecutive IVT-treated patients during a 5-year period at a tertiary care stroke center. A systematic review and meta-analysis of case-series reporting safety of IVT in SMs and confirmed acute ischemic stroke were conducted. Symptomatic intracerebral hemorrhage was defined as imaging evidence of ICH with an National Institutes of Health Stroke scale increase of ≥4 points. Favorable functional outcome at hospital discharge was defined as a modified Rankin Scale score of 0 to 1. Results—: Of 516 consecutive IVT patients at our tertiary care center (50% men; mean age, 60±14 years; median National Institutes of Health Stroke scale, 11; range, 3–22), SMs comprised 75 cases. Symptomatic intracerebral hemorrhage occurred in 1 patient, whereas we documented no cases of orolingual edema or major extracranial hemorrhagic complications. In meta-analysis of 9 studies (8942 IVT-treated patients), the pooled rates of symptomatic intracerebral hemorrhage and orolingual edema among 392 patients with SM treated with IVT were 0.5% (95% confidence interval, 0%–2%) and 0.3% (95% confidence interval, 0%–2%), respectively. Patients with SM were found to have a significantly lower risk for symptomatic intracerebral hemorrhage compared with patients with acute ischemic stroke (risk ratio=0.33; 95% confidence interval, 0.14–0.77; P =0.010), with no evidence of heterogeneity or publication bias. Favorable functional outcome was almost 3-fold higher in patients with SM in comparison with patients with acute ischemic stroke (risk ratio=2.78; 95% confidence interval, 2.07–3.73; P <0.00001). Conclusions—: Our prospective, single-center experience coupled with the findings of the comprehensive meta-analysis underscores the safety of IVT in SM.

Laboratory data from Electronic Health Records (EHR) are often used in prediction models where estimation bias and model performance from missingness can be mitigated using imputation methods. We demonstrate the utility of imputation in two real-world EHR-derived cohorts of ischemic stroke from Geisinger and of heart failure from Sutter Health to: (1) characterize the patterns of missingness in laboratory variables; (2) simulate two missing mechanisms, arbitrary and monotone; (3) compare cross-sectional and multi-level multivariate missing imputation algorithms applied to laboratory data; (4) assess whether incorporation of latent information, derived from comorbidity data, can improve the performance of the algorithms. The latter was based on a case study of hemoglobin A1c under a univariate missing imputation framework. Overall, the pattern of missingness in EHR laboratory variables wasnot at randomand was highly associated with patients’ comorbidity data; and the multi-level imputation algorithm showed smaller imputation error than the cross-sectional method.