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Objective: This study aimed to evaluate in hospital outcomes after carotid endarterectomy (CEA) according to shunt usage, particularly in patients with contralateral carotid occlusion (CCO) or recent stroke. Data from CEAs registered in the Vascular Quality Initiative database between 2012 and 2020 were analysed, excluding surgeons with < 10 CEAs registered in the database, concomitant procedures, re-interventions, and incomplete data., Methods: Based on their rate of shunt use, participating surgeons were divided in three groups: non-shunters (< 5%), selective shunters (5 - 95%), and routine shunters (> 95%). Primary outcomes of in hospital stroke, death, and stroke and death rate (SDR) were analysed in symptomatic and asymptomatic patients., Results: A total of 113 202 patients met the study criteria, of whom 31 147 were symptomatic and 82 055 were asymptomatic. Of the 1 645 surgeons included, 12.1% were non-shunters, 63.6% were selective shunters, and 24.3% were routine shunters, with 10 557, 71 160, and 31 579 procedures in each group, respectively. In the univariable analysis, in hospital stroke (2.0% vs. 1.9% vs. 1.6%; p = .17), death (0.5% vs. 0.4% vs. 0.4%; p = .71), and SDR (2.2% vs. 2.1% vs. 1.8%; p = .23) were not statistically significantly different among the three groups in the symptomatic cohort. The asymptomatic cohort also did not show a statistically significant difference for in hospital stroke (0.9% vs. 1.0% vs. 0.9%; p = .55), death (0.2% vs. 0.2% vs. 0.2%; p = .64), and SDR (1.0% vs. 1.1% vs. 1.0%; p = .43). The multivariable model did not show a statistically significant difference for the primary outcomes between the three shunting cohorts. On subgroup analysis, the SDRs were not statistically significantly different for patients with CCO (3.3% vs. 2.5% vs. 2.4%; p = .64) and those presenting with a recent stroke (2.9% vs. 3.4% vs. 3.1%; p = .60)., Conclusion: No statistically significant differences were found between three shunting strategies for in hospital SDR, including in patients with CCO or recent stroke., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

This report describes the case of a frail 36-year-old patient who underwent an endovascular treatment of a right subclavian artery pseudoaneurysm (SAP) associated with an arteriovenous fistula secondary to a traumatic central venous catheter insertion. The deployment of a covered stent from the innominate to the right common carotid artery combined with coiling of the SAP and the internal mammary artery was performed. Two additional covered stents were deployed from the vertebral artery to the distal subclavian artery to preserve right upper extremity circulation. This case highlights the feasibility of an endovascular treatment of a complex SAP in a candidate unsuitable for open surgery., Competing Interests: None., (© 2024 The Authors.)

Analysis of " omics " data is often a long and segmented process, encompassing multiple stages from initial data collection to processing, quality control and visualization. The cross-modal nature of recent genomic analyses renders this process challenging to both automate and standardize; consequently, users often resort to manual interventions that compromise data reliability and reproducibility. This in turn can produce multiple versions of datasets across storage systems. As a result, scientists can lose significant time and resources trying to execute and monitor their analytical workflows and encounter difficulties sharing versioned data. In 2015, the Ludmer Centre for Neuroinformatics and Mental Health at McGill University brought together expertise from the Douglas Mental Health University Institute, the Lady Davis Institute and the Montreal Neurological Institute (MNI) to form a genetics/epigenetics working group. The objectives of this working group are to: (i) design an automated and seamless process for (epi)genetic data that consolidates heterogeneous datasets into the LORIS open-source data platform; (ii) streamline data analysis; (iii) integrate results with provenance information; and (iv) facilitate structured and versioned sharing of pipelines for optimized reproducibility using high-performance computing (HPC) environments via the CBRAIN processing portal. This article outlines the resulting generalizable "omics" framework and its benefits, specifically, the ability to: (i) integrate multiple types of biological and multi-modal datasets (imaging, clinical, demographics and behavioral); (ii) automate the process of launching analysis pipelines on HPC platforms; (iii) remove the bioinformatic barriers that are inherent to this process; (iv) ensure standardization and transparent sharing of processing pipelines to improve computational consistency; (v) store results in a queryable web interface; (vi) offer visualization tools to better view the data; and (vii) provide the mechanisms to ensure usability and reproducibility. This framework for workflows facilitates brain research discovery by reducing human error through automation of analysis pipelines and seamless linking of multimodal data, allowing investigators to focus on research instead of data handling.

Data sharing is becoming more of a requirement as technologies mature and as global research and communications diversify. As a result, researchers are looking for practical solutions, not only to enhance scientific collaborations, but also to acquire larger amounts of data, and to access specialized datasets. In many cases, the realities of data acquisition present a significant burden, therefore gaining access to public datasets allows for more robust analyses and broadly enriched data exploration. To answer this demand, the Montreal Neurological Institute has announced its commitment to Open Science, harnessing the power of making both clinical and research data available to the world (Owens, 2016a,b). As such, the LORIS and CBRAIN (Das et al., 2016) platforms have been tasked with the technical challenges specific to the institutional-level implementation of open data sharing, including: Comprehensive linking of multimodal data (phenotypic, clinical, neuroimaging, biobanking, and genomics, etc.)Secure database encryption, specifically designed for institutional and multi-project data sharing, ensuring subject confidentiality (using multi-tiered identifiers).Querying capabilities with multiple levels of single study and institutional permissions, allowing public data sharing for all consented and de-identified subject data.Configurable pipelines and flags to facilitate acquisition and analysis, as well as access to High Performance Computing clusters for rapid data processing and sharing of software tools.Robust Workflows and Quality Control mechanisms ensuring transparency and consistency in best practices.Long term storage (and web access) of data, reducing loss of institutional data assets.Enhanced web-based visualization of imaging, genomic, and phenotypic data, allowing for real-time viewing and manipulation of data from anywhere in the world.Numerous modules for data filtering, summary statistics, and personalized and configurable dashboards. Implementing the vision of Open Science at the Montreal Neurological Institute will be a concerted undertaking that seeks to facilitate data sharing for the global research community. Our goal is to utilize the years of experience in multi-site collaborative research infrastructure to implement the technical requirements to achieve this level of public data sharing in a practical yet robust manner, in support of accelerating scientific discovery.