【摘要】 目的 探讨子宫部位异位妊娠的临床特征和处理对策。 方法 回顾分析2002年9月-2009年9月间收治的31例子宫部位异位妊娠患者的临床资料。 结果 31例患者中，初诊确诊仅8例，误诊率74.2%。除5例因难以控制的大出血行经腹病灶清除术加子宫修补术或全子宫切除术外，其余26例患者均经氨甲喋呤（MTX）治疗加清宫术或宫腔镜下病灶清除术保守治疗成功。 结论 子宫部位异位妊娠容易误诊，超声检查是诊断的主要方法。保守治疗安全、有效，可保留生育能力。氨甲喋呤治疗加清宫术可作为治疗子宫部位异位妊娠的主要方法。【Abstract】 Objective To investigate the clinical characteristics and treatment of ectopic pregnancy in the uterus. Methods The clinical data of 31 patients diagnosed as ectopic pregnancy from September 2002 to September 2009 were analyzed retrospectively. Results During preliminary diagnosis, only eight patients were accurately diagnosed.The error rate of first diagnosis was 74.2%. Five patients suffered focal cleaning and uterus neoplasty or total hysterectomy due to uncontrollable bleeding.The other 26 patients were successfully cured by conservation treatment of methotrexate (MTX) combined with dilatation and curettage or clearance of focal lesion under hysteroscopy. Conclusion Misdiagnosis of ectopic pregnancy in the uterus is easy to make.The ultrasonography is the main method for the diagnosis of ectopic pregnancy in the uterus.Conservative treatment is proved to be safe and effective and can preserve the patients’ fertility. Administration of MTX combined with dilatation and curettage is an main therapeutic method in handling ectopic pregnancy in the uterus.
The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.
How to extract high discriminative features that help classification from complex resting-state fMRI (rs-fMRI) data is the key to improving the accuracy of brain disease recognition such as schizophrenia. In this work, we use a weighted sparse model for brain network construction, and utilize the Kendall correlation coefficient (KCC) to extract the discriminative connectivity features for schizophrenia classification, which is conducted with the linear support vector machine. Experimental results based on the rs-fMRI of 57 schizophrenia patients and 64 healthy controls show that our proposed method is more effective (i.e., achieving a significantly higher classification accuracy, 81.82%) than other competing methods. Specifically, compared with the traditional network construction methods (Pearson’s correlation and sparse representation) and the commonly used feature selection methods (two-sample t-test and Least absolute shrinkage and selection operator (Lasso)), the algorithm proposed in this paper can more effectively extract the discriminative connectivity features between the schizophrenia patients and the healthy controls, and further improve the classification accuracy. At the same time, the discriminative connectivity features extracted in the work could be used as the potential clinical biomarkers to assist the identification of schizophrenia.
Both spike and local field potential (LFP) signals are two of the most important candidate signals for neural decoding. At present there are numerous studies on their decoding performance in mammals, but the decoding performance in birds is still not clear. We analyzed the decoding performance of both signals recorded from nidopallium caudolaterale area in six pigeons during the goal-directed decision-making task using the decoding algorithm combining leave-one-out and k-nearest neighbor (LOO-kNN). And the influence of the parameters, include the number of channels, the position and size of decoding window, and the nearest neighbor k value, on the decoding performance was also studied. The results in this study have shown that the two signals can effectively decode the movement intention of pigeons during the this task, but in contrast, the decoding performance of LFP signal is higher than that of spike signal and it is less affected by the number of channels. The best decoding window is in the second half of the goal-directed decision-making process, and the optimal decoding window size of LFP signal (0.3 s) is shorter than that of spike signal (1 s). For the LOO-kNN algorithm, the accuracy is inversely proportional to the k value. The smaller the k value is, the larger the accuracy of decoding is. The results in this study will help to parse the neural information processing mechanism of brain and also have reference value for brain-computer interface.