[Development of Vital Signal Monitoring System Based on Accelerometer].

OBJECTIVE: Reduce the number of false alarms and measurement time caused by movement interference by the sync waveform of the movement. METHODS: Vital signal monitoring system based on motion sensor was developed, which collected and processed the vital signals continuously, optimized the features and results of vital signals and transmitted the vital signal results and alarms to the interface. RESULTS: The system was tested in many departments, such as digestive department, cardiology department, internal medicine department, hepatobiliary surgery department and emergency department, and the total collection time was 1 940 h. The number of false electrocardiograph (ECG) alarms decreased by 82.8%, and the proportion of correct alarms increased by 28%. The average measurement time of non-invasive blood pressure (NIBP) decreased by 16.1 s. The total number of false respiratory rate measurement decreased by 71.9%. CONCLUSIONS: False alarms and measurement failures can be avoided by the vital signal monitoring system based on accelerometer to reduce the alarm fatigue in clinic.

The pathogenesis of liver cancer and the therapeutic potential of bioactive substances.

Liver cancer is the third most common cause of cancer-related deaths in the world and has become an urgent problem for global public health. Bioactive substances are widely used for the treatment of liver cancer due to their widespread availability and reduced side effects. This review summarizes the main pathogenic factors involved in the development of liver cancer, including metabolic fatty liver disease, viral infection, and alcoholic cirrhosis, and focuses on the mechanism of action of bioactive components such as polysaccharides, alkaloids, phenols, peptides, and active bacteria/fungi. In addition, we also summarize transformation methods, combined therapy and modification of bioactive substances to improve the treatment efficiency against liver cancer, highlighting new ideas in this field.

A New Approach to Segment Both Main and Peripheral Retinal Vessels Based on Gray-Voting and Gaussian Mixture Model.

Vessel segmentation in retinal fundus images is a preliminary step to clinical diagnosis for some systemic diseases and some eye diseases. The performances of existing methods for segmenting small vessels which are usually of more importance than the main vessels in a clinical diagnosis are not satisfactory in clinical use. In this paper, we present a method for both main and peripheral vessel segmentation. A local gray-level change enhancement algorithm called gray-voting is used to enhance the small vessels, while a two-dimensional Gabor wavelet is used to extract the main vessels. We fuse the gray-voting results with the 2D-Gabor filter results as pre-processing outcome. A Gaussian mixture model is then used to extract vessel clusters from the pre-processing outcome, while small vessels fragments are obtained using another gray-voting process, which complements the vessel cluster extraction already performed. At the last step, we eliminate the fragments that do not belong to the vessels based on the shape of the fragments. We evaluated the approach with two publicly available DRIVE (Staal et al., 2004) and STARE (Hoover et at., 2000) datasets with manually segmented results. For the STARE dataset, when using the second manually segmented results which include much more small vessels than the first manually segmented results as the "gold standard," this approach achieved an average sensitivity, accuracy and specificity of 65.0%, 92.1% and 97.0%, respectively. The sensitivities of this approach were much higher than those of the other existing methods, with comparable specificities; these results thus demonstrated that this approach was sensitive to detection of small vessels.