Case Western Reserve University

About: Case Western Reserve University is a education organization based out in Cleveland, Ohio, United States. It is known for research contribution in the topics: Population & Cancer. The organization has 54617 authors who have published 106568 publications receiving 5071613 citations. The organization is also known as: Case & Case Western.

Recommendations on Angiographic Revascularization Grading Standards for Acute Ischemic Stroke A Consensus Statement

Abstract: See related article, p 2509 Intra-arterial therapy (IAT) for acute ischemic stroke (AIS) has dramatically evolved during the past decade to include aspiration and stent-retriever devices. Recent randomized controlled trials have demonstrated the superior revascularization efficacy of stent-retrievers compared with the first-generation Merci device.1,2 Additionally, the Diffusion and Perfusion Imaging Evaluation for Understanding Stroke Evolution (DEFUSE) 2, the Mechanical Retrieval and Recanalization of Stroke Clots Using Embolectomy (MR RESCUE), and the Interventional Management of Stroke (IMS) III trials have confirmed the importance of early revascularization for achieving better clinical outcome.3–5 Despite these data, the current heterogeneity in cerebral angiographic revascularization grading (CARG) poses a major obstacle to further advances in stroke therapy. To date, several CARG scales have been used to measure the success of IAT.6–14 Even when the same scale is used in different studies, it is applied using varying operational criteria, which further confounds the interpretation of this key metric.10 The lack of a uniform grading approach limits comparison of revascularization rates across clinical trials and hinders the translation of promising, early phase angiographic results into proven, clinically effective treatments.6–14 For these reasons, it is critical that CARG scales be standardized and end points for successful revascularization be refined.6 This will lead to a greater understanding of the aspects of revascularization that are strongly predictive of clinical response. The optimal grading scale must demonstrate (1) a strong correlation with clinical outcome, (2) simplicity and feasibility of scale interpretation while ensuring characterization of relevant angiographic findings, and (3) high inter-rater reproducibility. To address these issues, a multidisciplinary panel of neurointerventionalists, neuroradiologists, and stroke neurologists with extensive experience in neuroimaging and IAT, convened at the “Consensus Meeting on Revascularization Grading Following Endovascular Therapy” with the goal …

Acid-doped polybenzimidazoles : a new polymer electrolyte

Abstract: Polybenzimidazole films doped with phosphoric acid are being investigated as potential polymer electrolytes for use in hydrogen/air and direct methanol fuel cells. In this paper, we present experimental findings on the proton conductivity, water content, and methanol vapor permeability of this material, as well as preliminary fuel cell results. The low methanol vapor permeability of these electrolytes significantly reduces the adverse effects of methanol crossover typically observed in direct methanol polymer electrolyte membrane fuel cells.

Drusen proteome analysis: An approach to the etiology of age-related macular degeneration

Abstract: Drusen are extracellular deposits that accumulate below the retinal pigment epithelium on Bruch's membrane and are risk factors for developing age-related macular degeneration (AMD). The progression of AMD might be slowed or halted if the formation of drusen could be modulated. To work toward a molecular understanding of drusen formation, we have developed a method for isolating microgram quantities of drusen and Bruch's membrane for proteome analysis. Liquid chromatography tandem MS analyses of drusen preparations from 18 normal donors and five AMD donors identified 129 proteins. Immunocytochemical studies have thus far localized ≈16% of these proteins in drusen. Tissue metalloproteinase inhibitor 3, clusterin, vitronectin, and serum albumin were the most common proteins observed in normal donor drusen whereas crystallin was detected more frequently in AMD donor drusen. Up to 65% of the proteins identified were found in drusen from both AMD and normal donors. However, oxidative protein modifications were also observed, including apparent crosslinked species of tissue metalloproteinase inhibitor 3 and vitronectin, and carboxyethyl pyrrole protein adducts. Carboxyethyl pyrrole adducts are uniquely generated from the oxidation of docosahexaenoate-containing lipids. By Western analysis they were found to be more abundant in AMD than in normal Bruch's membrane and were found associated with drusen proteins. Carboxymethyl lysine, another oxidative modification, was also detected in drusen. These data strongly support the hypothesis that oxidative injury contributes to the pathogenesis of AMD and suggest that oxidative protein modifications may have a critical role in drusen formation.

Applications of machine learning in drug discovery and development.

Abstract: Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development. Machine learning has been applied to numerous stages in the drug discovery pipeline. Here, Vamathevan and colleagues discuss the most useful techniques and how machine learning can promote data-driven decision making in drug discovery and development. They highlight major hurdles in the field, such as the required data characteristics for applying machine learning, which will need to be solved as machine learning matures.