Increased blood pressure variability and the risk of probable dementia or mild cognitive impairment: A post hoc analysis of the SPRINT MIND trial

Background Increased systolic blood pressure variability (BPV) is associated with stroke, cardiovascular disease, and dementia and mild cognitive impairment. However, prior studies assessing the relationship between BPV and dementia or mild cognitive impairment had infrequent measurement of blood pressure or suboptimal blood pressure control. Methods and Results We performed a post hoc analysis of the SPRINT (Systolic Blood Pressure Intervention Trial) MIND (Memory and Cognition in Decreased Hypertension) trial. The primary outcome was probable dementia during follow-up. We defined our exposure period, during which blood pressures were collected, as the first 600 days of the trial, and outcomes were ascertained during the subsequent follow-up. BPV was measured as tertiles of systolic blood pressure standard deviation. We fit Cox proportional hazards models to our outcome. We included 8379 patients. The mean follow-up was 3.2±1.4 years, during which 316 (3.8%) patients developed dementia. The mean number of blood pressure measurements was 7.8, and in the tertiles of BPV, the SD was 6.3±1.6, 10.3±1.1, and 16.3±3.6 mm Hg, respectively. The rate of dementia was 2.4%, 3.6%, and 5.4% by ascending tertile, respectively

Yersinia V–Antigen Exploits Toll-like Receptor 2 and CD14 for Interleukin 10–mediated Immunosuppression

A characteristic of the three human-pathogenic Yersinia spp. (the plague agent Yersinia pestis and the enteropathogenic Yersinia pseudotuberculosis and Yersinia enterocolitica) is the expression of the virulence (V)-antigen (LcrV). LcrV is a released protein which is involved in contact-induced secretion of yersinia antihost proteins and in evasion of the host's innate immune response. Here we report that recombinant LcrV signals in a CD14- and toll-like receptor 2 (TLR2)-dependent fashion leading to immunosuppression by interleukin 10 induction. The impact of this immunosuppressive effect for yersinia pathogenesis is underlined by the observation that TLR2-deficient mice are less susceptible to oral Y. enterocolitica infection than isogenic wild-type animals. In summary, these data demonstrate a new ligand specificity of TLR2, as LcrV is the first known secreted and nonlipidated virulence-associated protein of a Gram-negative bacterium using TLR2 for cell activation. We conclude that yersiniae might exploit host innate pattern recognition molecules and defense mechanisms to evade the host immune response

Medical Image Imputation from Image Collections

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago .Comment: Accepted at IEEE Transactions on Medical Imaging (\c{opyright} 2018 IEEE

Principles of precision medicine in stroke

The era of precision medicine has arrived and conveys tremendous potential, particularly for stroke neurology. The diagnosis of stroke, its underlying aetiology, theranostic strategies, recurrence risk and path to recovery are populated by a series of highly individualised questions. Moreover, the phenotypic complexity of a clinical diagnosis of stroke makes a simple genetic risk assessment only partially informative on an individual basis. The guiding principles of precision medicine in stroke underscore the need to identify, value, organise and analyse the multitude of variables obtained from each individual to generate a precise approach to optimise cerebrovascular health. Existing data may be leveraged with novel technologies, informatics and practical clinical paradigms to apply these principles in stroke and realise the promise of precision medicine. Importantly, precision medicine in stroke will only be realised once efforts to collect, value and synthesise the wealth of data collected in clinical trials and routine care starts. Stroke theranostics, the ultimate vision of synchronising tailored therapeutic strategies based on specific diagnostic data, demand cerebrovascular expertise on big data approaches to clinically relevant paradigms. This review considers such challenges and delineates the principles on a roadmap for rational application of precision medicine to stroke and cerebrovascular health

The TeleStroke Mimic (TM)‐Score: A Prediction Rule for Identifying Stroke Mimics Evaluated in a Telestroke Network

Background: Up to 30% of acute stroke evaluations are deemed stroke mimics (SM). As telestroke consultation expands across the world, increasing numbers of SM patients are likely being evaluated via Telestroke. We developed a model to prospectively identify ischemic SMs during Telestroke evaluation. Methods and Results: We analyzed 829 consecutive patients from January 2004 to April 2013 in our internal New England–based Partners TeleStroke Network for a derivation cohort, and 332 cases for internal validation. External validation was performed on 226 cases from January 2008 to August 2012 in the Partners National TeleStroke Network. A predictive score was developed using stepwise logistic regression, and its performance was assessed using receiver‐operating characteristic (ROC) curve analysis. There were 23% SM in the derivation, 24% in the internal, and 22% in external validation cohorts based on final clinical diagnosis. Compared to those with ischemic cerebrovascular disease (iCVD), SM had lower mean age, fewer vascular risk factors, more frequent prior seizure, and a different profile of presenting symptoms. The TeleStroke Mimic Score (TM‐Score) was based on factors independently associated with SM status including age, medical history (atrial fibrillation, hypertension, seizures), facial weakness, and National Institutes of Health Stroke Scale >14. The TM‐Score performed well on ROC curve analysis (derivation cohort AUC=0.75, internal validation AUC=0.71, external validation AUC=0.77). Conclusions: SMs differ substantially from their iCVD counterparts in their vascular risk profiles and other characteristics. Decision‐support tools based on predictive models, such as our TM Score, may help clinicians consider alternate diagnosis and potentially detect SMs during complex, time‐critical telestroke evaluations

Medical Image Imputation From Image Collections

We present an algorithm for creating high-resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable the application of existing algorithms that were originally developed for high-resolution research scans to significantly undersampled scans. We introduce a generative model that captures a fine-scale anatomical structure across subjects in clinical image collections and derives an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms the state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago

Common NOTCH3 Variants and Cerebral Small-Vessel Disease.

BACKGROUND AND PURPOSE: The most common monogenic cause of cerebral small-vessel disease is cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, caused by NOTCH3 gene mutations. It has been hypothesized that more common variants in NOTCH3 may also contribute to the risk of sporadic small-vessel disease. Previously, 4 common variants (rs10404382, rs1043994, rs10423702, and rs1043997) were found to be associated with the presence of white matter hyperintensity in hypertensive community-dwelling elderly. METHODS: We investigated the association of common single nucleotide polymorphisms (SNPs) in NOTCH3 in 1350 patients with MRI-confirmed lacunar stroke and 7397 controls, by meta-analysis of genome-wide association study data sets. In addition, we investigated the association of common SNPs in NOTCH3 with MRI white matter hyperintensity volumes in 3670 white patients with ischemic stroke. In each analysis, we considered all SNPs within the NOTCH3 gene, and within 50-kb upstream and downstream of the coding region. A total of 381 SNPs from the 1000 genome population with a mean allele frequency>0.01 were included in the analysis. A significance level of P<0.0015 was used, adjusted for the effective number of independent SNPs in the region using the Galwey method. RESULTS: We found no association of any common variants in NOTCH3 (including rs10404382, rs1043994, rs10423702, and rs1043997) with lacunar stroke or white matter hyperintensity volume. We repeated our analysis stratified for hypertension but again found no association. CONCLUSIONS: Our study does not support a role for common NOTCH3 variation in the risk of sporadic small-vessel disease.Collection of the UK Young Lacunar Stroke DNA Study (DNA lacunar) was primarily supported by the Wellcome Trust (WT072952) with additional support from the Stroke Association (TSA 2010/01). Genotyping of the DNA lacunar samples, and Dr Traylor, was supported by a Stroke Association Grant (TSA 2013/01). Funding for the genotyping at Massachusetts General Hospital was provided by the Massachusetts General Hospital- Deane Institute for the Integrative Study of Atrial Fibrillation and Stroke and the National Institute of Neurological Disorders and Stroke (U01 NS069208). Dr Rutten-Jacobs was supported by a project grant from the Stroke Association/British Heart Foundation grant (TSA BHF 2010/01). Dr Adib-Samii was supported by a Medical Research Council (United Kingdom) training fellowship. Drs Markus and Bevan were supported by the National Institute for Health Research Cambridge University Hospitals Comprehensive Biomedical Research Centre. Dr Markus was supported by a National Institute for Health Research Senior Investigator award. Dr Thijs was supported by a Clinical Investigator Grant from the scientific research fund, Fonds Wetenschappelijk Onderzoek Flanders. Dr Rost was supported by a National Institute of Neurological Disorders and Stroke grant (R01 NS082285-01).This is the final published version. It first appeared at http://stroke.ahajournals.org/content/46/6/1482.long