To assess the impact on stroke outcome of statin use in the acute phase after IV thrombolysis.
Multicenter study on prospectively collected data of 2,072 stroke patients treated with IV thrombolysis. ...Outcome measures of efficacy were neurologic improvement (NIH Stroke Scale NIHSS ≤ 4 points from baseline or NIHSS = 0) and major neurologic improvement (NIHSS ≤ 8 points from baseline or NIHSS = 0) at 7 days and favorable (modified Rankin Scale mRS ≤ 2) and excellent functional outcome (mRS ≤ 1) at 3 months. Outcome measures of safety were 7-day neurologic deterioration (NIHSS ≥ 4 points from baseline or death), symptomatic intracerebral hemorrhage type 2 with NIHSS ≥ 4 points from baseline or death within 36 hours, and 3-month death.
Adjusted multivariate analysis showed that statin use in the acute phase was associated with neurologic improvement (odds ratio OR 1.68, 95% confidence interval CI 1.26-2.25; p < 0.001), major neurologic improvement (OR 1.43, 95% CI 1.11-1.85; p = 0.006), favorable functional outcome (OR 1.63, 95% CI 1.18-2.26; p = 0.003), and a reduced risk of neurologic deterioration (OR: 0.31, 95% CI 0.19-0.53; p < 0.001) and death (OR 0.48, 95% CI 0.28-0.82; p = 0.007).
Statin use in the acute phase of stroke after IV thrombolysis may positively influence short- and long-term outcome.
In computer vision, stereoscopy allows the three-dimensional reconstruction of a scene using two 2D images taken from two slightly different points of view, to extract spatial information on the ...depth of the scene in the form of a map of disparities. In stereophotogrammetry, the disparity map is essential in extracting the digital terrain model (DTM) and thus obtaining a 3D spatial mapping, which is necessary for a better analysis of planetary surfaces. However, the entire reconstruction process performed with the stereo-matching algorithm can be time consuming and can generate many artifacts. Coupled with the lack of adequate stereo coverage, it can pose a significant obstacle to 3D planetary mapping. Recently, many deep learning architectures have been proposed for monocular depth estimation, which aspires to predict the third dimension given a single 2D image, with considerable advantages thanks to the simplification of the reconstruction problem, leading to a significant increase in interest in deep models for the generation of super-resolution images and DTM estimation. In this paper, we combine these last two concepts into a single end-to-end model and introduce a new generative adversarial network solution that estimates the DTM at 4× resolution from a single monocular image, called SRDiNet (super-resolution depth image network). Furthermore, we introduce a sub-network able to apply a refinement using interpolated input images to better enhance the fine details of the final product, and we demonstrate the effectiveness of its benefits through three different versions of the proposal: SRDiNet with GAN approach, SRDiNet without adversarial network, and SRDiNet without the refinement learned network plus GAN approach. The results of Oxia Planum (the landing site of the European Space Agency’s Rosalind Franklin ExoMars rover 2023) are reported, applying the best model along all Oxia Planum tiles and releasing a 3D product enhanced by 4×.
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