ABSTRACT
Hepatic cystic echinococcosis is a chronic parasitic disease caused by the infection with the larvae of Echinococcus granulosus in human or animal liver tissues. As a chronic active infectious disease, tuberculous empyema mainly invades the pleural space and then causes visceral and parietal pleura thickening. It is rare to present comorbidity for hepatic cystic echinococcosis and tuberculous empyema. This case report presents a case of hepatic cystic echinococcosis complicated with tuberculous empyema misdiagnosed as hepatic and pulmonary cystic echinococcosis, aiming to improve clinicians’ ability to distinguish this disorder.
ABSTRACT
Dendritic cells (DCs), a type of antigen-presenting cells (APC), are recognized as an important regulator of immune response and immune tolerance, and play a critical role in the host innate immunity and adaptive immunity. Previous studies have shown that the long-term parasization of Echinococcus in the host is strongly associated with the host immune tolerance induced by DCs. This review summarizes the research progress of the role of DCs in host immune tolerance caused Echinococcus infection, aiming to provide the theoretical basis and insights into the management and immunotherapy of Echinococcus infections.
ABSTRACT
This paper presents a new method for automatic sleep stage classification which is based on the EEG permutation entropy. The EEG permutation entropy has notable distinction in each stage of sleep and manifests the trend of regular transforming. So it can be used as features of sleep EEG in each stage. Nearest neighbor is employed as the pattern recognition method to classify the stages of sleep. Experiments are conducted on 750 sleep EEG samples and the mean identification rate can be up to 79.6%.
Subject(s)
Humans , Classification , Methods , Electroencephalography , Methods , Entropy , Pattern Recognition, Automated , Signal Processing, Computer-Assisted , Sleep Stages , PhysiologyABSTRACT
Objective To find a useful index for real-time detecting of speech endpoint and improving the performance of speech processing under low SNR by analyzing fluctuation complexity of speech signals. Method The influence of state space partition method, window size and partition numbers on detecting performance was analyzed. The comparison experiments of speech signals corresponding to different SNR and noise type was designed using the measure of complexity behaviors based on the information gain.Result It was found that fluctuation complexity was more effective in detecting low-SNR speech than spectral entropy. Conclusion Fluctuation complexity is a valid feature to make speech/non-speech decision for the low SNR cases. The presented method can achieve robust performance and has a good real-time behavior.