目的 对尿液特征组分与糖尿病早期肾损害的关系进行了初步探索。 方法 对2011年12月－2012年5月间28例2型糖尿病组、33例2型糖尿病肾病组及26例健康对照组尿液中尿蛋白含量和几种常见非蛋白氮物质，包括肌酸、尿囊素、肌酐、尿酸和假尿嘧啶核苷的浓度进行测定，采用多种归一化方法对数据进行对比分析，并通过t检验减少高效液相色谱测定的变量信息，保留P＜0.05的检出峰进行主成分分析，获得分类结果。 结果 采用体积归一化方法，发现健康对照组尿液中肌酸、尿囊素和尿酸的含量与2型糖尿病组和糖尿病肾病组相比，差异均有统计学意义（P＜0.05），2型糖尿病组尿液中尿蛋白的浓度与糖尿病肾病组相比，差异有统计学意义（P＜0.05）。 结论 通过肌酸、尿囊素、尿酸和尿蛋白的联合测定为肾脏损伤程度的监测及疗效观察提供依据，为2型糖尿病患者肾功能损坏的早期预防与诊断进行初步判断提供了新的方法。
In this work, a new method of heart sound signal preprocessing is presented. First, the heart sound signals are decomposed by using multilayer wavelet transform. And then double parameters as thresholds are used in processing each layer after decomposition for denoising. Next, reconstruction of heart sound signals could be done after processing last layer. Four methods, i.e. wavelet transform, Hilbert-Huang transform (HHT), mathematical morphology, and normalized average Shannon energy, were used to extract the envelop of the heart sound signals respectively after reconstruction of heart sounds. All methods were improved in this study. We finally in our study chose 30 cases of raw heart sound signals, which were selected randomly from a database comed from The Clinical Medicine Institute of Montreal, and processed them by using the improved methods. The results were satisfactory. It showed that the extracted envelope with the original signal has a high degree of matching, whether it is a low frequency portion or high frequency portion. Most of all information of heart sound has been maintained in the envelope.
Brain aging can affect the strength of functional connectivity between brain regions. In recent years, studies have shown that functional connectivity is fluctuant over time, and can reflect more physiological and pathological information. Therefore, in the study resting state functional magnetic resonance imaging (fMRI) data of 32 elderly subjects and 36 younger subjects were selected, and the sliding window technique was used to estimate dynamic functional connectivity network. Then, the dependency of fluctuating energy difference on frequency band was studied using wavelet packet analysis, conducting the linear regression with age at the same time. Results showed that the fluctuating energy in older group was significantly higher than that in the young group in low frequency, and it was significantly lower than that in the young people in high frequency. These results suggested that the dynamic functional connectivity between networks in the elderly exist slow wave phenomenon, which may be related to the decreased reaction rate of the elderly. This article provides new ideas and methods for the research about brain aging, and promotes a theoretical basis for further understanding of the physiological significance of brain dynamic functional connectivity.
In order to eliminate the influence of motion artifacts, high-frequency noise and baseline drift on photoplethysmographic (PPG), and to obtain the accurate value of heart rate in motion state, this paper proposed a de-noising method of PPG signal based on normalized least mean square (NLMS) adaptive filtering combining ensemble empirical mode decomposition(EEMD). Firstly, the PPG signal containing noise is passed through an adaptive filter with a 3-axis acceleration sensor as a reference signal to filter out motion artifacts. Secondly, the PPG signal is decomposed by EEMD to obtain a series of intrinsic modal function (IMF) according to the frequency from high to low. The threshold range of the signal is judged by the permutation entropy (PE) criterion, thereby filtering out the high frequency noise and the baseline drift. The experimental results show that the Pearson correlation coefficient between the calculated heart rate of PPG signal and the standard heart rate based on electrocardiogram (ECG) signal is 0.731 and the average absolute error percentage is 6.10% under different motion states, which indicates that the method can accurately calculate the heart rate in moving state and is beneficial to the physiological monitoring under the state of human motion.
Heart failure is one kind of cardiovascular disease with high risk and high incidence. As an effective treatment of heart failure, artificial heart is gradually used in clinical treatment. Blood compatibility is an important parameter or index of artificial heart, and how to evaluate it through hemodynamic design and in vitro hemolysis test is a research hotspot in the industry. This paper first reviews the research progress in hemodynamic optimization and in vitro hemolysis evaluation of artificial heart, and then introduces the research achievements and progress of the team in related fields. The hemodynamic performance of the blood pump optimized in this paper can meet the needs of use. The normalized index of hemolysis obtained by in standard vitro hemolysis test is less than 0.1 g/100 L, which has good hemolysis performance in vitro. The optimization method described in this paper is suitable for most of the development of blood pump and can provide reference for related research work.
Adaptive filtering methods based on least-mean-square (LMS) error criterion have been commonly used in auscultation to reduce ambient noise. For non-Gaussian signals containing pulse components, such methods are prone to weights misalignment or even divergence. Unlike the commonly used variable step-size methods, this paper introduced linear preprocessing to address this issue. The role of linear preprocessing in improving the denoising performance of the normalized least-mean-square (NLMS) adaptive filtering algorithm was analyzed. It was shown that, the steady-state mean square weight deviation of the NLMS adaptive filter was proportional to the variance of the body sounds and inversely proportional to the variance of the ambient noise signals in the secondary channel. Preprocessing with properly set parameters could suppress the spikes of body sounds, and decrease the variance and the power spectral density of the body sounds, without significantly reducing or even with increasing the variance and the power spectral density of the ambient noise signals in the secondary channel. As a result, the preprocessing could reduce weights misalignment, and correspondingly, significantly improve the performance of ambient-noise reduction. Finally, a case of heart-sound auscultation was given to demonstrate how to design the preprocessing and how the preprocessing improved the ambient-noise reduction performance. The results can guide the design of adaptive denoising algorithms for body sound auscultation.