Efficacy and safety of salvianolate and enoxaparin in the prevention of perioperative deep venous thrombosis in gastrointestinal surgery.

OBJECTIVE: In this study, the efficacy and safety of salvianolate were compared with enoxaparin in the prevention of perioperative deep vein thrombosis in gastrointestinal surgery. METHODS: From October 2017 to September 2019, 563 patients who underwent gastrointestinal surgery were collected. Based on the inclusion and exclusion criteria, 119 patients were divided into two groups: enoxaparin group (n = 65) and salvianolate group (n = 54). Comparisons were made regarding the outcomes: prothrombin time (PT), prothrombin activity (PTA), international normalized ratio (INR), activated partial thromboplastin time (APTT), fibrinogen (FIB), thrombin time (TT), D-dimer level (D-D), platelet count (PLT), hematokrit (HCT), and incidence of deep vein thrombosis (DVT). RESULTS: The main outcomes showed no significance between enoxaparin group and salvianolate group (p > .05). The incidence of DVT in salvianolate group was 1.85%, significantly lower than that in enoxaparin group (12.3%) (p < .05). No serious adverse reactions occurred in the two groups during treatment. CONCLUSION: Compared with enoxaparin, salvianolate has an advantage in the prevention of perioperative thrombosis in gastrointestinal surgery with a lower incidence of DVT.

Efficient VR and AR Navigation Through Multiperspective Occlusion Management.

Immersive navigation in virtual reality (VR) and augmented reality (AR) leverages physical locomotion through pose tracking of the head-mounted display. While this navigation modality is intuitive, regions of interest in the scene may suffer from occlusion and require significant viewpoint translation. Moreover, limited physical space and user mobility need to be taken into consideration. Some regions of interest may require viewpoints that are physically unreachable without less intuitive methods such as walking in-place or redirected walking. We propose a novel approach for increasing navigation efficiency in VR and AR using multiperspective visualization. Our approach samples occluded regions of interest from additional perspectives, which are integrated seamlessly into the user's perspective. This approach improves navigation efficiency by bringing simultaneously into view multiple regions of interest, allowing the user to explore more while moving less. We have conducted a user study that shows that our method brings significant performance improvement in VR and AR environments, on tasks that include tracking, matching, searching, and ambushing objects of interest.

Multiperspective Focus+Context Visualization.

Occlusions are a severe bottleneck for the visualization of large and complex datasets. Conventional images only show dataset elements to which there is a direct line of sight, which significantly limits the information bandwidth of the visualization. Multiperspective visualization is a powerful approach for alleviating occlusions to show more than what is visible from a single viewpoint. However, constructing and rendering multiperspective visualizations is challenging. We present a framework for designing multiperspective focus+context visualizations with great flexibility by manipulating the underlying camera model. The focus region viewpoint is adapted to alleviate occlusions. The framework supports multiperspective visualization in three scenarios. In a first scenario, the viewpoint is altered independently for individual image regions to avoid occlusions. In a second scenario, conventional input images are connected into a multiperspective image. In a third scenario, one or several data subsets of interest (i.e., targets) are visualized where they would be seen in the absence of occluders, as the user navigates or the targets move. The multiperspective images are rendered at interactive rates, leveraging the camera model's fast projection operation. We demonstrate the framework on terrain, urban, and molecular biology geometric datasets, as well as on volume rendered density datasets.