Optical detection of the Pictor A jet and tidal tail : evidence against an IC/CMB jet

Date of Acceptance: 12/06/2015New images of the FR II radio galaxy Pictor A from the Hubble Space Telescope reveal a previously undiscovered tidal tail, as well as a number of jet knots coinciding with a known X-ray and radio jet. The tidal tail is approximately 5″ wide (3 kpc projected), starting 18″ (12 kpc) from the center of Pictor A, and extends more than 90″ (60 kpc). The knots are part of a jet observed to be about 4′ (160 kpc) long, extending to a bright hotspot. These images are the first optical detections of this jet, and by extracting knot flux densities through three filters, we set constraints on emission models. While the radio and optical flux densities are usually explained by synchrotron emission, there are several emission mechanisms that might be used to explain the X-ray flux densities. Our data rule out Doppler-boosted inverse Compton scattering as a source of the high-energy emission. Instead, we find that the observed emission can be well described by synchrotron emission from electrons with a low-energy index (p ∼ 2) that dominates the radio band, while a high-energy index (p ∼ 3) is needed for the X-ray band and the transition occurs in the optical/infrared band. This model is consistent with a continuous electron injection scenario.Peer reviewedFinal Accepted Versio

Genetic overlap between diagnostic subtypes of ischemic stroke

Background and Purpose: Despite moderate heritability, the phenotypic heterogeneity of ischemic stroke has hampered gene discovery, motivating analyses of diagnostic subtypes with reduced sample sizes. We assessed evidence for a shared genetic basis among the 3 major subtypes: large artery atherosclerosis (LAA), cardioembolism, and small vessel disease (SVD), to inform potential cross-subtype analyses. Methods: Analyses used genome-wide summary data for 12 389 ischemic stroke cases (including 2167 LAA, 2405 cardioembolism, and 1854 SVD) and 62 004 controls from the Metastroke consortium. For 4561 cases and 7094 controls, individual-level genotype data were also available. Genetic correlations between subtypes were estimated using linear mixed models and polygenic profile scores. Meta-analysis of a combined LAA-SVD phenotype (4021 cases and 51 976 controls) was performed to identify shared risk alleles. Results: High genetic correlation was identified between LAA and SVD using linear mixed models (rg=0.96, SE=0.47, P=9×10-4) and profile scores (rg=0.72; 95% confid

Large-scale analyses of common and rare variants identify 12 new loci associated with atrial fibrillation

Atrial fibrillation affects more than 33 million people worldwide and increases the risk of stroke, heart failure, and death. Fourteen genetic loci have been associated with atrial fibrillation in European and Asian ancestry groups. To further define the genetic basis of atrial fibrillation, we performed large-scale, trans-ancestry meta-analyses of common and rare variant association studies. The genome-wide association studies (GWAS) included 17,931 individuals with atrial fibrillation and 115,142 referents; the exome-wide association studies (ExWAS) and rare variant association studies (RVAS) involved 22,346 cases and 132,086 referents. We identified 12 new genetic loci that exceeded genome-wide significance, implicating genes involved in cardiac electrical and structural remodeling. Our results nearly double the number of known genetic loci for atrial fibrillation, provide insights into the molecular basis of atrial fibrillation, and may facilitate the identification of new potential targets for drug discovery

Multi-ethnic genome-wide association study for atrial fibrillation

Atrial fibrillation (AF) affects more than 33 million individuals worldwide and has a complex heritability. We conducted the largest meta-analysis of genome-wide association studies (GWAS) for AF to date, consisting of more than half a million individuals, including 65,446 with AF. In total, we identified 97 loci significantly associated with AF, including 67 that were novel in a combined-ancestry analysis, and 3 that were novel in a European-specific analysis. We sought to identify AF-associated genes at the GWAS loci by performing RNA-sequencing and expression quantitative trait locus analyses in 101 left atrial samples, the most relevant tissue for AF. We also performed transcriptome-wide analyses that identified 57 AF-associated genes, 42 of which overlap with GWAS loci. The identified loci implicate genes enriched within cardiac developmental, electrophysiological, contractile and structural pathways. These results extend our understanding of the biological pathways underlying AF and may facilitate the development of therapeutics for AF

Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes

AbstractObjectiveWe sought to assess whether genetic risk factors for atrial fibrillation can explain cardioembolic stroke risk.MethodsWe evaluated genetic correlations between a prior genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously-validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors.ResultsWe observed strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson’s r=0.77 and 0.76, respectively, across SNPs with p &lt; 4.4 × 10−4 in the prior AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio (OR) per standard deviation (sd) = 1.40, p = 1.45×10−48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per sd = 1.07, p = 0.004), but no other primary stroke subtypes (all p &gt; 0.1).ConclusionsGenetic risk for AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.</jats:sec

Stroke genetics informs drug discovery and risk prediction across ancestries

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

Urologic Surgery with Multisystem Comorbidities: A Case Report.

BACKGROUND Originally implemented for colorectal surgery, enhanced perioperative protocols have been incorporated into many surgical fields in an effort to improve outcomes. The cornerstone of many strategies includes patient education, liberalized oral intake on the day of surgery, no routine bowel prep, targeted multimodal analgesia, cautious use of IV hydration, early extubation, avoidance of NG tubes and/or surgical drains, and encouraging early postoperative ambulation. CASE REPORT We report on the successful outcome of a single patient with a rare autosomal dominant disorder (hereditary hemorrhagic telangiectasia) with multisystem involvement including pulmonary, cardiac, hematologic, gastrointestinal, renal, oncologic, and hepatic comorbidities, scheduled for open nephrectomy. CONCLUSIONS Prospective and retrospective studies are needed to specifically elucidate the role of similar management in higher-risk surgical candidates

Novel grading system for CADASIL severity: A multicenter cross-sectional study

Background: Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited progressive cerebral microangiopathy with considerable phenotypic variability. The purpose of this study was to describe the generalizability of a recently proposed grading system of CADASIL across multiple centers in the United States. Methods: Electronic medical records (EMR) of an initial neurological assessment of adult patients with confirmed CADASIL were reviewed across 5 tertiary referral medical centers with expertise in CADASIL. Demographic, vascular risk factors, and neuroimaging data were abstracted from EMR. Patients were categorized into groups according to the proposed CADASIL grading system: Grade 0 (asymptomatic), Grade 1 (migraine only), Grade 2 (stroke, TIA, or MCI), Grade 3 (gait assistance or dementia), and Grade 4 (bedbound or end-stage). Inter-rater reliability (IRR) of grading was tested in a subset of cases. Results: We identified 138 patients with a mean age of 50.9 ± 13.1 years, and 57.2% were female. The IRR was acceptable over 33 cases (κ=0.855, SD 0.078, p<0.001) with 81.8% being concordant. There were 15 patients (10.9%) with Grade 0, 50 (36.2%) with Grade 1, 61 (44.2%) with Grade 2, 12 (8.7%) with Grade 3, and none with Grade 4. Patients with a lower severity grade (grade 0 vs 3) tended to be younger (49.5 vs. 61.9 years) and had a lower prevalence of hypertension (50% vs. 20%, p = 0.027) and diabetes mellitus (0% vs. 25%, p = 0.018). A higher severity grade was associated with an increased number of vascular risk factors (p = 0.02) and independently associated with hypertension and diabetes (p<0.05). Comparing Grade 0 vs. 3, cortical thickness tended to be greater (2.06 vs. 1.87 mm; p = 0.06) and white matter hyperintensity volume tended to be lower (54.7 vs. 72.5 ml; p = 0.73), but the differences did not reach significance. Conclusion: The CADASIL severity grading system is a pragmatic, reliable system for characterizing CADASIL phenotype that does not require testing beyond that done in standard clinical practice. Higher severity grades tended to have a higher vascular risk factor burden. This system offers a simple method of categorizing CADASIL patients which may help to describe populations in observational and interventional studies

Big Data Approaches to Phenotyping Acute Ischemic Stroke Using Automated Lesion Segmentation of Multi-Center Magnetic Resonance Imaging Data

Background and Purpose- We evaluated deep learning algorithms' segmentation of acute ischemic lesions on heterogeneous multi-center clinical diffusion-weighted magnetic resonance imaging (MRI) data sets and explored the potential role of this tool for phenotyping acute ischemic stroke. Methods- Ischemic stroke data sets from the MRI-GENIE (MRI-Genetics Interface Exploration) repository consisting of 12 international genetic research centers were retrospectively analyzed using an automated deep learning segmentation algorithm consisting of an ensemble of 3-dimensional convolutional neural networks. Three ensembles were trained using data from the following: (1) 267 patients from an independent single-center cohort, (2) 267 patients from MRI-GENIE, and (3) mixture of (1) and (2). The algorithms' performances were compared against manual outlines from a separate 383 patient subset from MRI-GENIE. Univariable and multivariable logistic regression with respect to demographics, stroke subtypes, and vascular risk factors were performed to identify phenotypes associated with large acute diffusion-weighted MRI volumes and greater stroke severity in 2770 MRI-GENIE patients. Stroke topography was investigated. Results- The ensemble consisting of a mixture of MRI-GENIE and single-center convolutional neural networks performed best. Subset analysis comparing automated and manual lesion volumes in 383 patients found excellent correlation (ρ=0.92; P<0.0001). Median (interquartile range) diffusion-weighted MRI lesion volumes from 2770 patients were 3.7 cm3 (0.9-16.6 cm3). Patients with small artery occlusion stroke subtype had smaller lesion volumes ( P<0.0001) and different topography compared with other stroke subtypes. Conclusions- Automated accurate clinical diffusion-weighted MRI lesion segmentation using deep learning algorithms trained with multi-center and diverse data is feasible. Both lesion volume and topography can provide insight into stroke subtypes with sufficient sample size from big heterogeneous multi-center clinical imaging phenotype data sets