Background/Aims: Mitochondrial permeability transition has a critical role in ischemia/reperfusion (I/R)-induced kidney injury. It is thought that mitochondrial permeability transition occurs after the opening of the permeability transition pore, a channel which putatively consists of a voltage-dependent anion channel, adenine nucleotide translocator and cyclophilin D (CypD). Much evidence shows that CypD plays an important role in I/R-induced injury. Methods: To evaluate the role of CypD following I/R renal injury, we tested the hypothesis that knockdown of CypD gene by RNA interference (RNAi) protects rat from I/R-induced renal injury. Results: Our data show that knockdown of CypD by RNAi protects normal rat kidney cell line from hypoxia-induced necrotic death. Infection of lentivirus expressing CypD RNAi sequence produces a significant reduction of CypD at both mRNA and protein levels. Both pathologic and biochemical analyses show that knockdown of CypD by RNAi protects rat kidney from I/R-induced renal injury. Conclusion: Our study provides the evidence that CypD may be a potential target for protecting I/R-induced renal injury.

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Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. Although recent literature has already achieved competitive results, these approaches usually fail when dealing with complex documents with noisy OCR results or mutative layouts. This paper proposes Generative Multi-modal Network (GMN) for real-world scenarios to address these problems, which is a robust multimodal generation method without predefined label categories. With the carefully designed spatial encoder and modal-aware mask module, GMN can deal with complex documents that are hard to serialized into sequential order. Moreover, GMN tolerates errors in OCR results and requires no character-level annotation, which is vital because fine-grained annotation of numerous documents is laborious and even requires annotators with specialized domain knowledge. Extensive experiments show that GMN achieves new state-of-the-art performance on several public DIE datasets and surpasses other methods by a large margin, especially in realistic scenes.

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