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Objective:To investigate the effect of Hong's One Stitch Method in pancreaticoduodenectomy(PD).Methods:A total of 40 patients who underwent PD in our hospital from Jan 2021 to Dec 2022 were divided into two groups according to random number table method,with 20 patients in each group.The control group was treated with end to end pancreatojejunal anastomosis,and the observation group was treated with"Hong's One Stitch Method".The perioperative indicators,complications,secondary surgery,mortality and quality of life were compared between the two groups.Results:The pancreatoenteroanastomosis time,operation time and hospitalization time in the observation group were shorter than those in the control group,and the incidence of pancreatic fistula was lower than that in the control group(P＜0.05).There were no significant differences in intraoperative blood loss,pancreatic biochemical leakage,bile fistula,hemorrhage,localized abdominal infection,gastric emptying obstruction,pulmonary infection,secondary surgery and mortality between the two groups(P＞0.05).The mental health score,emotional function score,social function score,energy score,general health status score,body pain score,and physiological function score in the observation group were higher than those in the control group(P＜0.05).Conclusion:In PD surgery,the application of"Hong's One Stitch Method"to perform pancreatoenterostomy is beneficial to shorten the pancreatoenterostomy time,operation time and hospitalization time,accelerate the postoperative recovery,reduce the incidence of pancreatic fistula,and improve the quality of life of patients.

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The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.

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Traditional depression research based on electroencephalogram (EEG) regards electrodes as isolated nodes and ignores the correlation between them. So it is difficult to discover abnormal brain topology alters in patients with depression. To resolve this problem, this paper proposes a framework for depression recognition based on brain function network (BFN). To avoid the volume conductor effect, the phase lag index is used to construct BFN. BFN indexes closely related to the characteristics of "small world" and specific brain regions of minimum spanning tree were selected based on the information complementarity of weighted and binary BFN and then potential biomarkers of depression recognition are found based on the progressive index analysis strategy. The resting state EEG data of 48 subjects was used to verify this scheme. The results showed that the synchronization between groups was significantly changed in the left temporal, right parietal occipital and right frontal, the shortest path length and clustering coefficient of weighted BFN, the leaf scores of left temporal and right frontal and the diameter of right parietal occipital of binary BFN were correlated with patient health questionnaire 9-items (PHQ-9), and the highest recognition rate was 94.11%. In addition, the study found that compared with healthy controls, the information processing ability of patients with depression reduced significantly. The results of this study provide a new idea for the construction and analysis of BFN and a new method for exploring the potential markers of depression recognition.

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