Anesthesia consciousness monitoring is an important issue in basic neuroscience and clinical applications, which has received extensive attention. In this study, in order to find the indicators for monitoring the state of clinical anesthesia, a total of 14 patients undergoing general anesthesia were collected for 5 minutes resting electroencephalogram data under three states of consciousness (awake, moderate and deep anesthesia). Sparse partial least squares (SPLS) and traditional synchronized likelihood (SL) are used to calculate brain functional connectivity, and the three conscious states before and after anesthesia were distinguished by the connection features. The results show that through the whole brain network analysis, SPLS and traditional SL method have the same trend of network parameters in different states of consciousness, and the results obtained by SPLS method are statistically significant (P<0.05). The connection features obtained by the SPLS method are classified by the support vector machine, and the classification accuracy is 87.93%, which is 7.69% higher than that of the connection feature classification obtained by SL method. The results of this study show that the functional connectivity based on the SPLS method has better performance in distinguishing three kinds of consciousness states, and may provides a new idea for clinical anesthesia monitoring.
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (P < 0.05). Next, using the relationship between multiple thresholds and network parameters, the area under the curve (AUC) of each network parameter was extracted as new features (P < 0.05). Finally, support vector machine (SVM) was used to classify the two groups with the network parameters and their AUC as features. The study results show that with strength, average characteristic path length, and average clustering coefficient as features, the classification accuracy in the θ band is increased from 69% to 71%, 66% to 77%, and 50% to 68%, respectively. In the α band, the accuracy is increased from 72% to 79%, 69% to 82%, and 65% to 75%, respectively. And from overall view, when AUC of network parameters was used as a feature in the α band, the classification accuracy is improved compared to the network parameter feature. In the θ band, only the AUC of average clustering coefficient was applied to classification, and the accuracy is improved by 17.6%. The study proved that based on graph theory, the method of feature optimization of brain function network could provide some theoretical support for the computer-aided diagnosis of adolescent depression.
Objective To explore the effects of 4-phenylbutyric acid (4-PBA) on idiopathic pulmonary fibrosis (IPF) using a murine model of bleomycin (BLM)-induced pulmonary fibrosis. Methods Pulmonary fibrosis was induced in C57BL/6 mice by intratracheal injection of BLM. A total of 120 mice were randomly allocated into three groups: BLM group, BLM+4-PBA group, and control group. Pathology of lung tissue was analyzed to evaluate the degree of pulmonary fibrosis, and the survival of the mice was noted. The expression levels of the endoplasmic reticulum stress markers, activating transcription factor 6 (ATF6) and C/EBP homologous protein (CHOP), were analyzed in lung tissues from mice. Results BLM induced significant collagen deposition in the lungs of the mice, which was alleviated by 4-PBA. 4-PBA also dramatically improved the pulmonary function and increased the survival rate in the BLM+4-PBA group compared with that in the BLM group. Both the mRNA and protein expression levels of ATF6 and CHOP were significantly reduced in mouse lung tissue after 2 weeks of 4-PBA treatment. Conclusions 4-PBA treatment could alleviate BLM-induced pulmonary fibrosis in mice via the attenuation of endoplasmic reticulum stress.