Objective To study the biological activities ofthe nerve regeneration conditioned fluid (NRCF), especially to further separateand identify the protein bands of the relative molecular mass of (232-440)×103. Methods The silicone nerve regeneration chambers were implanted between the cut ends of the sciatic nerve in 6 New Zealand white rabbits (weight, 1.8-2.5 kg). The proteins in NRCF were separated by the native-polycrylamide gel electrophoresis (Native-PAGE), the protein bands of the relative molecular mass of (232-440)×103 were analyzed by the Shotgun technique, liquid chromatography, and mass spectrometry. Results The Native-PAGE result showed that there was 1 protein band of the relative molecular mass over 669×103, (232-440)×103 and (140-232)×103,respectively, and 6 bands of the relative molecular mass of (67-140)×103.Besides, 54 proteins were identified with at least 2 distinct peptides in 1 protein band of the relative molecular mass of (232-440)×103, including 4 unnamed protein products, mainly at the isoelectric points of 5.5-8.0 and of the relative molecular mass of (10-40)×103. Based on their functions in the protein database, allthe identified proteins in this study were classified into the following 5 groups: conjugated protein (43%), transport protein (30%), enzyme (6%), signal transducer (4%), and molecular function-unknown protein (17%). At the subcellular localization of the identified proteins, there was mainly a secreted protein (63%), and the remaining proteins were localized in the membrane and cytoplasm. Conclusion Native-PAGE and the Shotgun technique can effectively separate and identify proteins from NRCF, and can identify the components of the protein band of the relative molecular mass of (232-440)×103 and provide basicinformation on the unnamed protein products in NRCF.
For the near-infrared (NIR) spectral analysis of the concentration of blood glucose, the calibration accuracy can be affected because of the existing of outlier samples. In this research, a Monte-Carlo cross validation (MCCV) method is constructed for eliminating outlier samples. The human blood plasma experiment in vitro and the human body experiment in vivo were introduced to evaluate the MCCV method for its application effect in NIR spectral analysis of blood glucose. And the uninformative sample elimination method based on modified uninformative variable elimination (MUVE-USE) was employed in this study for the comparison with MCCV. The results indicated that, like the MUVE-USE method, the outlier samples elimination method based on MCCV could be used to eliminate the outlier samples which came from gross errors (such as bad sample) or system errors (such as baseline drift). In addition, the outlier samples from the random errors of uncertain causes which affect model accuracy can be eliminated simultaneously by MCCV. The elimination of multiple outlier samples is beneficial to the improvement of prediction accuracy of calibration model.
Cardiovascular disease (CVD) is one of the leading causes of death worldwide. Heart sound classification plays a key role in the early detection of CVD. The difference between normal and abnormal heart sounds is not obvious. In this paper, in order to improve the accuracy of the heart sound classification model, we propose a heart sound feature extraction method based on bispectral analysis and combine it with convolutional neural network (CNN) to classify heart sounds. The model can effectively suppress Gaussian noise by using bispectral analysis and can effectively extract the features of heart sound signals without relying on the accurate segmentation of heart sound signals. At the same time, the model combines with the strong classification performance of convolutional neural network and finally achieves the accurate classification of heart sound. According to the experimental results, the proposed algorithm achieves 0.910, 0.884 and 0.940 in terms of accuracy, sensitivity and specificity under the same data and experimental conditions, respectively. Compared with other heart sound classification algorithms, the proposed algorithm shows a significant improvement and strong robustness and generalization ability, so it is expected to be applied to the auxiliary detection of congenital heart disease.