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The Stroke Preclinical Assessment Network (SPAN) is a multicenter preclinical trial platform using rodent models of transient focal cerebral ischemia to address translational failure in experimental stroke. In addition to centralized randomization and blinding and large samples, SPAN aimed to introduce heterogeneity to simulate the heterogeneity embodied in clinical trials for robust conclusions. Here, we report the heterogeneity introduced by allowing the 6 SPAN laboratories to vary most of the biological and experimental model variables and the impact of this heterogeneity on middle cerebral artery occlusion (MCAo) performance. We included the modified intention-to-treat population of the control mouse cohort of the first SPAN trial (n=421) and examined the biological and procedural independent variables and their covariance. We then determined their impact on the dependent variables cerebral blood flow drop during MCAo, time to achieve MCAo, and total anesthesia duration using multivariable analyses. We found heterogeneity in biological and procedural independent variables introduced mainly by the site. Consequently, all dependent variables also showed heterogeneity among the sites. Multivariable analyses with the site as a random effect variable revealed filament choice as an independent predictor of cerebral blood flow drop after MCAo. Comorbidity, sex, use of laser Doppler flow to monitor cerebral blood flow, days after trial onset, and maintaining anesthesia throughout the MCAo emerged as independent predictors of time to MCAo. Total anesthesia duration was predicted by most independent variables. We present with high granularity the heterogeneity introduced by the biological and model selections by the testing sites in the first trial of cerebroprotection in rodent transient filament MCAo by SPAN. Rather than trying to homogenize all variables across all sites, we embraced the heterogeneity to better approximate clinical trials. Awareness of the heterogeneity, its sources, and how it impacts the study performance may further improve the study design and statistical modeling for future multicenter preclinical trials.

BackgroundGenome-wide Association Studies (GWAS) have reshaped our understanding of the genetic bases of complex diseases in general and neurodegenerative diseases in particular. Despite being a common disorder, dementia with Lewy bodies (DLB), which, together with Parkinson’s disease dementia (PDD), comprise the umbrella term Lewy body dementias (LBD), is far from being well-characterized genetically. This is primarily due to a lack of familial cases and difficulty recruiting large, deeply characterized cohorts, given the high rate of misdiagnosis. By performing the largest GWAS in DLB, we aimed to identify novel risk loci to gain a better understanding of this disease’s pathobiology.MethodsHere, we conducted the largest meta-analysis of genome-wide association studies performed in LBD, using a total of 5,119 cases and 20,988 controls, from five independent datasets, aggregating all previously published DLB genome-wide association results to date, as well as two previously undescribed cohorts. Additionally, we performed a sex stratified GWAS using the discovery datasets. We updated the heritability estimates for DLB and, to fine map these estimates, we used local heritability analysis. We calculated genetic correlation estimates between DLB and a range of other diseases and traits to identify potential pleiotropy. We also performed gene-set analysis to identify genes with excess burden of rare variability and pathway analysis. Lastly, we used the UK Biobank data to perform a PheWas using individuals at the extremes of genetic risk for DLB.FindingsBetween November 2018 and September 2022 we analyzed 8.6 million single nucleotide polymorphisms in 3293 DLB cases, 1826 LBD cases and 20,988 controls, as well as phenotypes from the UK Biobank dataset. Despite more than doubling the sample size from the previous GWAS in DLB, we did not identify significant loci in addition to those previously reported atGBA, SNCA, STX1B, andAPOE. However, the sex-stratified analysis revealed that theGBAandSNCAsignals are mainly driven by males, suggesting a sex-specific genetic architecture of disease. Using only clinical and neuropathologically diagnosed cases, we highlight four loci surpassing the significance threshold. Using the largest cohort of DLB we update our heritability estimates to 13% and fine map these results highlighting regions of the genome with high heritability but no genome-wide significant result so far.InterpretationThese data provide the most comprehensive analysis of genetic variability in DLB to date. The fact that no novel risk loci have been identified after doubling the cohort size indicates the potentially significant role of rare variants in the genetic architecture of DLB and stresses the urgent need for larger, well-characterized cohorts of this disease for genetic studies. The sex-stratified analysis shows that males and females have different signatures of genetic risk for DLB. These results have widespread implications for clinical practice and clinical trials’ design in DLB.

This tutorial will introduce you to the basics of Windows PowerShell, the standard command-line interface for Windows computers. If you are a Mac or Linux user, you should check out the Bash introduction instead. If you are already familiar with using Bash, you may be able to get started with PowerShell just by looking at the table at the end of this lesson. The tutorial is divided into two main sections. In the first section, “Getting Started,” you will learn to do basic desktop tasks like creating and opening files and folders using PowerShell. In the second section, “Doing More,” you will get a glimpse of some of the features that make work on the command line particularly efficient, and learn enough of the basics to be able to explore further on your own. You will also get set up to run Python scripts from the command line. This tutorial was written for PowerShell 5.0. If you are using an earlier version, you may encounter a few small syntax differences which you should be able to overcome with a little help from a search engine.

Parkinson's disease (PD) is a common neurodegenerative disorder with no known cure. The etiology of PD is likely due, in part, to combinations of genetic susceptibilities and environmental factors. In rare familial cases, PD is due to genetic mutations. A number of new genetic and toxin models of PD and advances in older models are yielding important new information about the pathogenesis of PD. This has prompted us to critically review the current animal models for PD and discuss how these models may yield fresh insights into the pathogenesis of PD, as well as new therapeutic opportunities.