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Aortoenteric fistula is one of the uncommon emergencies and is challenging to navigate for diagnostic testing. Here, we present a clinical case of an aortoduodenal fistula with primary etiology. A 73-year-old female patient with a history of hypertension was admitted to the hospital because of a 1-day history of melena. Ultrasound showed an abdominal aortic aneurysm sized (33 × 46) mm and a hematoma on the wall of the aorta. The patient underwent a gastrointestinal endoscopy with no bleeding point detected. However, the patient suddenly fell into a hemorrhagic shock on day 3 of admission. We rapidly performed fluid resuscitation, blood transfusion, a second gastrointestinal endoscopy, and a computed tomography scan of the abdomen with contrast injection that revealed a fistula from the abdominal aorta into the second segment of the duodenum. The patient was indicated for urgent endovascular aortic repair. Although this technique was successful with 3 abdominal aortic stents, the patient died due to multiorgan failure. Delayed diagnosis is the root cause of primary aortoduodenal fistula treatment failure, so it is important for clinicians to keep aortoduodenal fistula in mind as a possible cause of gastrointestinal bleeding in any patient.

Virtual reality (VR) has been adopted in various fields such as entertainment, education, healthcare, and the military, due to its ability to provide an immersive experience to users. However, 360° images, one of the main components in VR systems, have bulky sizes and thus require effective transmitting and rendering solutions. One of the potential solutions is to use foveated technologies, that take advantage of the foveation feature of the human eyes. Foveated technologies can significantly reduce the data required for transmission and computation complexity in rendering. However, understanding the impact of foveated 360° images on human quality perception is still limited. This paper addresses the above problems by proposing an accurate machine-learning-based quality assessment model for foveated 360° images. The proposed model is proven to outperform the three cutting-edge machine-learning-based models, which apply deep learning techniques and 25 traditional-metric-based models (or analytical-function-based-models), which utilize analytical functions. It is also expected that our model helps to evaluate and improve 360° content streaming and rendering solutions to further reduce data sizes while ensuring user experience. Also, this model could be used as a building block to construct quality assessment methods for 360° videos, that are reserved for our future work. The source code is available at https://github.com/telagment/FoVGCN.

Study of structural inhomogeneities in zeolites is important but limited by conventional techniques. Here the authors employ in situ free-electron-laser-based time-resolved coherent X-ray diffraction imaging to visualize the effect of these inhomogeneities during catalytic deoxygenation of NOx.