MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis.

PURPOSE: The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification. METHODS: In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning. RESULTS: We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.

Pre-anesthetic clinic internship: new teaching method of pre-anesthesia evaluation for undergraduates.

BACKGROUND: This study aimed to observe the effect of internship in a pre-anesthetic clinic on the teaching quality of pre-anesthesia evaluation for undergraduates. METHODS: A total of 120 undergraduates from July 2017 to July 2018 in the anesthesia department of our hospital were randomly divided into two groups: pre-anesthetic clinic internship teaching group (n = 60) and traditional teaching group (n = 60). The knowledge in the pre-anesthesia evaluation teaching chapters was evaluated between the two groups of undergraduates. RESULTS: There were no significant differences in the demographic information between the two groups. The scores in the case analysis and theoretical knowledge test in the pre-anesthetic clinic internship teaching group were significantly higher than those in the traditional teaching group. In addition, the students' satisfaction with the curriculum design was significantly higher in the pre-anesthetic clinic internship teaching group than in the traditional teaching group. CONCLUSION: Pre-anesthetic clinic internships can improve the quality of pre-anesthesia assessment teaching for undergraduates.

Neural Remodeling of the Left Atrium in Rats by Rosuvastatin Following Acute Myocardial Infarction.

OBJECTIVE: This study aims to investigate the effect of rosuvastatin on sympathetic neural remodeling of the left atrium (LA) in rats after myocardial infarction (MI). METHODS: Rats were randomly divided into a three groups: sham group, statin group, and MI group. The mRNA expression levels of the growth-associated protein-43 (GAP43) and nerve growth factor (NGF) were measured by RT-PCR. Immunohistochemistry was used to detect the distribution and density of GAP43- and NGF-positive nerves. The expression levels of these proteins were quantified by Western blot. RESULTS: Compared with the sham group, the average optical density (AOD) values of GAP43 and nerve growth factor (NGF)-positive substances in the LA in the statin and MI groups were significantly higher (P<0.01), but the AOD values in the statin group were lower than of those in the MI group (P<0.01). Furthermore, the AOD values of GAP43 and NGF positive nerves in the left stellate ganglion in the statin and MI groups were significantly higher (P<0.01), but the AOD values in the statin group were lower, when compared with the MI group (P<0.01). CONCLUSION: Rosuvastatin could effectively improve the sympathetic neural remodeling of LA in MI rats.