ObjectiveTo review the research progress on etiology and pathogenesis of spina bifida. MethodsBy consulting relevant domestic and foreign research literature on spina bifida, the classification, epidemic trend, pathogenesis, etiology, prevention and treatment of it were analyzed and summarized. ResultsSpina bifida, a common phenotype of neural tube defects, is classified based on the degree and pattern of malformation associated with neuroectodermal involvement and is due to the disturbance of neural tube closure 28 days before embryonic development. The prevalence of spina bifida varies greatly among different ethnic groups and regions, and its etiology is complex. Currently, some spina bifida patients can be prevented by folic acid supplements, and with the improvement of treatment technology, the short-term and long-term survival rate of children with spina bifida has improved. ConclusionThe research on the pathogenesis of spina bifida will be based on the refined individual information on exposure, genetics, and complex phenotype, and will provide a theoretical basis for improving prevention and treatment strategies through multidisciplinary cooperation.
Objective To investigate the changing tendency of mitral valve coaptation area and coaptation index of moderate mitral regurgitation （MR） in a dog experiment，and provide evidence for predicting long-term surgical results. Methods Real-time three-dimensional transesophogeal echocardiography （RT-3D-TEE） images were obtained in 15 dogs via Philips IE33 echocardiography system，and animal experiment model was established. RT-3D-TEE images were taken by gradually narrowing the ascending aorta and increasing left ventricular pressure till moderate MR. Original data were analyzed using Philips Qlab 7.0 three-dimensional quantification software，and mitral valve coaptation area and coaptation index were calculated. Specimen coaptation index of the mitral leaflets was calculated after the animal experiment. Cutoff values of coaptation index and left ventricular pressure were calculated by receiver operating characteristic （ROC） curve. Results There was statistical difference in coaptation area （198±50）mm2 vs. （123±36）mm2，P＜0.05） and coaptationindex （0.25±0.06 vs. 0.13±0.03，P＜0.05） between non-MR state and MR status of the 15 dogs. The area under the ROC curve of coaptation index and moderate MR was 0.879±0.019 with 95% CI 0.843 to 0.916，and the cutoff value was 0.213（P＜0.05）. The area under the ROC curve of left ventricular pressure and moderate MR was 0.882±0.021 swith 95% CI 0.840 to 0.923，and the cutoff value was 225 （P＜0.05）. There was no statistical difference between specimen mitral valve area and early-diastolic mitral leaflet area，specimen coaptation area and coaptation area，specimen coaptation index and coaptation index （P＞0.05）. Early-diastolic mitral leaflet area was significantly correlated with specimen mitral valve area （r=0.937，P＜0.05）. Coaptation area was significantly correlated with specimen coaptation area （r=0.917，P＜0.05）. Coaptation index was significantly correlated with specimen coaptation index （r=0.946，P＜0.05）. The correlation of coaptation index and specimen coaptation index was higher than those of coaptation area and specimen coaptation area，and earlydiastolic mitral leaflet area and specimen mitral valve area. Conclusions Both coaptation area and coaptation index significantly decrease in MR status. Coaptation index can more precisely reflect MR degree，and provide reference for prognosis of mitral valve repair. RT-3D TEE can accurately measure mitral valve coaptation area and coaptation index.
目的 探讨甲状腺疾病患者血清游离三碘甲状腺原氨酸(FT3)、游离甲状腺素(FT4)、促甲状腺激素(TSH)、甲状腺球蛋白(TG)含量水平变化与临床意义。 方法 采用电化学发光法对2009年1月-6月诊断的725例甲状腺疾病患者的FT3、FT4、TSH、TG进行检测，对各类甲状腺疾病的激素含量进行统计分析。 结果 血清中此类激素的水平与甲状腺的功能状态呈平行关系。 结论 在不同甲状腺疾病患者血清FT3、FT4、TSH、TG变化各有特点，有助于临床鉴别诊断。
Both feature representation and classifier performance are important factors that determine the performance of computer-aided diagnosis (CAD) systems. In order to improve the performance of ultrasound-based CAD for breast cancers, a novel multiple empirical kernel mapping (MEKM) exclusivity regularized machine (ERM) ensemble classifier algorithm based on self-paced learning (SPL) is proposed, which simultaneously promotes the performance of both feature representation and the classifier. The proposed algorithm first generates multiple groups of features by MEKM to enhance the ability of feature representation, which also work as the kernel transform in multiple support vector machines embedded in ERM. The SPL strategy is then adopted to adaptively select samples from easy to hard so as to gradually train the ERM classifier model with improved performance. This algorithm is verified on a B-mode ultrasound dataset and an elastography ultrasound dataset, respectively. The results show that the classification accuracy, sensitivity and specificity on B-mode ultrasound are (86.36±6.45)%, (88.15±7.12)%, and (84.52±9.38)%, respectively, and the classification accuracy, sensitivity and specificity on elastography ultrasound are (85.97±3.75)%, (85.93±6.09)%, and (86.03±5.88)%, respectively. It indicates that the proposed algorithm can effectively improve the performance of ultrasound-based CAD for breast cancers with the potential for application.