目的 探讨损伤控制外科（DCS）理念在肝脏破裂救治中的作用。方法 收集2009年1月至2012年5月期间我院急诊外科收治的62例外伤致肝脏破裂患者的临床资料，比较DCS理念指导前（传统组）与DCS理念指导后（DCS组）急诊肝脏破裂救治的疗效。结果 DCS组的保守治疗率明显高于传统组 〔26.47% （9/34）比7.14% （2/28），P＜0.05〕，2组间保守治疗成功率比较差异无统计学意义〔100% （9/9）比100% （2/2），P＞0.05〕；DCS组的死亡率及术后并发症发生率较传统组明显降低〔死亡率：4.00% （1/25）比19.23% （5/26），P＜0.05；并发症发生率：32.00% （8/25）比61.54% （16/26），P＜0.05〕；2组手术患者住院时间、出血量、输血量、手术时间及住院费用比较差异均无统计学意义（P＞0.05）。结论 DCS理念指导下制定出的新的抢救措施，能够明显降低肝脏破裂的死亡率及术后并发症的发生率。
ObjectiveTo summarize the research progress of microRNA-200 (miR-200) family in triple-negative breast cancer (TNBC).MethodsRelevant literatures at home and abroad were systematically retrieved and read to review the research progress of miR-200 family in TNBC in recent years.ResultsThe miR-200 family played an important role in the proliferation, invasion, and metastasis of TNBC, as well as the resistance to treatment. It could also be used as potential therapeutic targets and biological predictors. Different miR-200 family members and differential expression mediated various targeting effects, which may be related to differences in signaling pathways and cellular environment.ConclusionsmiR-200 family plays a key regulatory role in the occurrence and development of TNBC, and it is expected to provide new ideas for the treatment and prognosis evaluation of TNBC. However, its mechanism of action still needs further study.
Studies have shown that the clinical manifestation of patients with neuropsychiatric disorders might be related to the abnormal connectivity of brain functions. Psychogenic non-epileptic seizures (PNES) are different from the conventional epileptic seizures due to the lack of the expected electroencephalographically epileptic changes in central nervous system, but are related to the presence of significant psychological factors. Diagnosis of PNES remains challenging. We found in the present work that the connectivity between the frontal and parieto-occipital in PNES was weaker than that of the controls by using network analysis based on electroencephalogram (EEG) signals. In addition, PNES were recognized by using the network properties as linear discriminant nalysis (LDA) input and classification accuracy was 85%. This study may provide a feasible tool for clinical diagnosis of PNES.