Raf kinase inhibitor protein (RKIP) protects against host immunological responses in nematodes and Whether RKIP functions in innate immune responses in mammals remains unknown. In this article, we report that RKIP preferentially regulates the TLR3-mediated immune response in macrophages. RKIP deficiency or silencing significantly decreases polyinosinic:polycytidylic acid [Poly(I:C)]-induced IFN-β, IL-6, and TNF-α production without affecting the counterpart induced by LPS or CpG. Compared with their wild-type counterparts, RKIP-deficient mice produce less IFN-β, IL-6, and TNF-α in serum and display decreased lethality upon peritoneal Poly(I:C) plus d-galactosamine injection. Mechanistically, RKIP interacts with TBK1 and promotes the Poly(I:C)-induced TANK-binding kinase 1/IRF3 activation. Simultaneously, RKIP enhances the Poly(I:C)-induced interaction between TGF-β-activated kinase 1 and MAPK kinase 3 (MKK3), thus promoting MKK3/6 and p38 activation. We further demonstrated that Poly(I:C) treatment, but not LPS treatment, induces RKIP phosphorylation at S109. This action is required for RKIP to promote TANK-binding kinase 1 activation, as well as the interaction between TGF-β-activated kinase 1 and MKK3, which lead to activation of the downstream IRF3 and p38, respectively. Therefore, RKIP acts as a positive-feedback regulator of the TLR3-induced inflammatory response and may be a potential therapeutic target for inflammatory disease.
: Subarachnoid hemorrhage (SAH) is a type of hemorrhagic stroke associated with high mortality and morbidity. The blood-brain-barrier (BBB) is a structure consisting primarily of cerebral microvascular endothelial cells, end feet of astrocytes, extracellular matrix, and pericytes. Post-SAH pathophysiology included early brain injury and delayed cerebral ischemia. BBB disruption was a critical mechanism of early brain injury, and was associated with other pathophysiological events. These pathophysiological events may propel the development of secondary brain injury, known as delayed cerebral ischemia. Imaging advancements to measure BBB after SAH primarily focused on exploring innovative methods to predict clinical outcome, delayed cerebral ischemia, and delayed infarction related to delayed cerebral ischemia in acute periods. These predictions are based on detecting abnormal changes in BBB permeability. The parameters of BBB permeability are described by changes in computed tomography (CT) perfusion and magnetic resonance imaging (MRI). Kep seems to be a stable and sensitive indicator in CT perfusion, whereas Ktrans is a reliable parameter for dynamic contrast-enhanced MRI. Future prediction models that utilize both the volume of BBB disruption and stable parameters of BBB may be a promising direction to develop practical clinical tools. These tools could provide greater accuracy in predicting clinical outcome and risk of deterioration. Therapeutic interventional exploration targeting BBB disruption is also promising, considering the extended duration of post-SAH BBB disruption.
Higher resolution building mapping from lower resolution remote sensing images is in great demand due to the lack of higher resolution data access, especially in the context of disaster assessment. High resolution building layout map is crucial for emergency rescue after the disaster. The emergency response time would be reduced if detailed building footprints were delineated from more easily available low-resolution data. To achieve this goal, we propose a super-resolution semantic segmentation network called ESPC NASUnet, which consists of a feature super-resolution module and a semantic segmentation module. To the best of our knowledge, this is the first work to systematically explore a deep learning-based approach to generate semantic maps with higher spatial resolution from lower spatial resolution remote sensing images in an end-toend fashion. The experimental results for two datasets suggest that the proposed network is the best among four different end-to-end architectures in terms of both pixel-level metrics and object-level metrics. In terms of pixel-level F1-score, the improvements are greater than 0.068 and 0.055. Regarding the object-level F1-score, the disparities between ESPC NASUnet and other end-to-end methods are more than 0.083 and 0.161 in the two datasets, respectively. Compared with stage-wise methods, our end-to-end network is less impacted by low-resolution input images. Finally, the proposed network produces building semantic maps comparable to those generated by semantic segmentation networks trained with high-resolution images and the ground truth utilizing the two datasets.
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