836 research outputs found

    The clinic research of gout treatment with Ziyinruanjiantongluo decoction and small needle-knife

    Get PDF
    目的  揭示痛风“主客交”的病理机制以及滋阴软坚通络方治疗痛风的作用机理。方法  临床60例患者,随机分为两组,治疗组30例运用滋阴软坚通络方外加小针刀辅助治疗,对照组30例口服秋水仙碱片和依托考昔片。观察两组患者的主要症状改善情况、治疗前后检验指标变化、临床疗效及不良反应等方面的差异。结果  60例痛风患者中,临床疗效率方面,治疗组总有效率100%,其中治愈率33%、显效率40%、无效率0,明显优于对照组,差异具有非常显著性(P<0.01);不良反应方面,治疗组0,对照组10%,差异有显著性(P<0.05)。结论  以“主客交”理论为指导,运用其代表方“三甲散”化裁的滋阴软坚通络方,配合小针刀治疗痛风,临床疗效确切,与单纯采用秋水仙碱等西药治疗比较,有明显优势。Objective: To reveal the pathogenesis of “intercourse between Host and Guest” in gout and mechanism of Ziyinruanjiantongluo decoction. Methods: 60 patients are divided into 2 groups by random, 30 in therapeutic group (given by ziyinruanjiantongluo decoction and small needle-knife) and 30 in the controlled group (given by Colchicine tablets and Etoricoxib Tablets). Then the changes of main symptoms, Laboratory test index, clinic effect and adverse reaction are recorded in both groups. Results: The clinic effective rate is 100% in therapeutic group. In therapeutic group, the curative rate 33%, effectual rate 40% and noneffective rate 0. The results in therapeutic group is more superior than the controlled group and there is significant difference between the two groups (P<0.01). In adverse reaction, there is no adverse reaction in therapeutic group but 10% in the controlled group and there is difference between the two groups (P<0.05). Conclusion: Based on the theory of “intercourse between Host and Guest”, the treatment Ziyinruanjiantongluo decoction (addition or substraction from Sanjia power) and small needle-knife clinic effect is accurate. Comparing with the treatment Colchicine tablets and Etoricoxib Tablets, it has certain advantages

    Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution

    Full text link
    Hyperspectral image has become increasingly crucial due to its abundant spectral information. However, It has poor spatial resolution with the limitation of the current imaging mechanism. Nowadays, many convolutional neural networks have been proposed for the hyperspectral image super-resolution problem. However, convolutional neural network (CNN) based methods only consider the local information instead of the global one with the limited kernel size of receptive field in the convolution operation. In this paper, we design a network based on the transformer for fusing the low-resolution hyperspectral images and high-resolution multispectral images to obtain the high-resolution hyperspectral images. Thanks to the representing ability of the transformer, our approach is able to explore the intrinsic relationships of features globally. Furthermore, considering the LR-HSIs hold the main spectral structure, the network focuses on the spatial detail estimation releasing from the burden of reconstructing the whole data. It reduces the mapping space of the proposed network, which enhances the final performance. Various experiments and quality indexes show our approach's superiority compared with other state-of-the-art methods
    corecore