Publications by authors named "O S Cohen"

The broad spectrum of clinical manifestations caused by peripheral arterial disease [PAD] and the morphologic heterogeneity of associated atherosclerotic lesions present a considerable management challenge. Endovascular interventions are recognized an effective treatment for PAD. Within this revascularization strategy the role of atherectomy debulking modalities continue to evolve.

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Aims: To compare impact of pump treatment and continuous glucose monitoring (CGM) with predictive low glucose suspend (SmartGuard) or user initiated CGM (iscCGM) on sleep and hypoglycemia fear in children with type 1 Diabetes and parents.

Methods: Secondary analysis of data from 5 weeks pump treatment with iscCGM (A) or SmartGuard (B) open label, single center, randomized cross-over study was performed. At baseline and end of treatment arms, sleep and fear of hypoglycemia were evaluated using ActiGraph and questionnaires.

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High-harmonic generation (HHG) is a nonlinear process in which a material sample is irradiated by intense laser pulses, causing the emission of high harmonics of incident light. HHG has historically been explained by theories employing a classical electromagnetic field, successfully capturing its spectral and temporal characteristics. However, recent research indicates that quantum-optical effects naturally exist or can be artificially induced in HHG, such as entanglement between emitted harmonics.

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Life histories of oviparous species dictate high metabolic investment in the process of gonadal development leading to ovulation. In vertebrates, these two distinct processes are controlled by the gonadotropins follicle-stimulating hormone (FSH) and luteinizing hormone (LH), respectively. While it was suggested that a common secretagogue, gonadotropin-releasing hormone (GnRH), oversees both functions, the generation of loss-of-function fish challenged this view.

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Dynamic contrast-enhanced (DCE) MRI is an important imaging tool for evaluating tumor vascularity that can lead to improved characterization of tumor extent and heterogeneity, and for early assessment of treatment response. However, clinical adoption of quantitative DCE-MRI remains limited due to challenges in acquisition and quantification performance, and lack of automated tools. This study presents an end-to-end deep learning pipeline that exploits a novel deep reconstruction network called DCE-Movienet with a previously developed deep quantification network called DCE-Qnet for fast and quantitative DCE-MRI.

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