【摘要】 目的 总结皮肤扩张器用于巨痣整形的护理措施。 方法 2008年4月-2009年11月对28例皮肤扩张器置入治疗巨痣整形的护理措施进行总结和分析，重点加强了心理护理、健康教育及注水期间的护理。 结果 术后患者皮肤色泽正常，外观满意出院。随诊1年，效果佳。 结论 加强心理护理，有针对性的健康教育，重视注水期间护理措施的实施，对皮肤扩张器置入术用于巨痣整形患者至关重要。【Abstract】Objective To summarize the nursing interventions of skin expander plasty of giant nevus. Methods From April 2008 to November 2009, 28 cases of giant nevus were admitted for skin expander surgery.The nursing interventions，especially the mental nursing, health education and nursing care during the infusion period were summarized and analyzed. Results The results were satisfactory including the color and the appearance by one-year follow-up. Conclusion It is important to emphasize the mental nursing, health education and nursing care during the infusion period for the patient undergoing giant nevus plasty treated with skin expander.
ObjectiveTo systematically review the efficacy and safety of new anti-epileptic drugs in the treatment of epilepsy. MethodsPubMed, EMbase, The Cochrane Library, CNKI and WanFang Data databases were electronically searched to collect randomized controlled trials (RCTs) of new anti-epileptic drugs rufinamide, zonisamide, and perampanel in the treatment of epilepsy from January 2006 to May 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by using RevMan 5.3 and Stata 16.0 software. ResultsA total of 16 RCTs involving 4 382 patients were included. The results of meta-analysis showed that the effective rate (RR=1.66, 95%CI 1.45 to 1.89, P<0.000 01) and seizure-free rate (RR=2.82, 95%CI 2.01 to 3.96, P<0.000 01) in new anti-epileptic drugs group were higher than those in the control group, while it did not increase the serious adverse events (RR=0.95, 95%CI 0.72 to 1.27, P=0.75). ConclusionCurrent evidence shows that new anti-epileptic drugs have trends of better effectiveness, and their safety is satisfactory. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify above conclusion.
Existing classification methods for myositis ultrasound images have problems of poor classification performance or high computational cost. Motivated by this difficulty, a lightweight neural network based on a soft threshold attention mechanism is proposed to cater for a better IIMs classification. The proposed network was constructed by alternately using depthwise separable convolution (DSC) and conventional convolution (CConv). Moreover, a soft threshold attention mechanism was leveraged to enhance the extraction capabilities of key features. Compared with the current dual-branch feature fusion myositis classification network with the highest classification accuracy, the classification accuracy of the network proposed in this paper increased by 5.9%, reaching 96.1%, and its computational complexity was only 0.25% of the existing method. The obtained results support that the proposed method can provide physicians with more accurate classification results at a lower computational cost, thereby greatly assisting them in their clinical diagnosis.