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Objective To explore the risk factors for vascular invasion and its influence on prognosis of resectable gastric cancer patients by analyzing the clinicopathological features. Methods We retrospectively analyzed the data of 1077 patients with stage Ⅰ-Ⅲ gastric cancer who underwent surgical resection. According to whether vascular invasion occurred, they were divided into LVI positive group (n=672) and LVI negative group (n=405). Logistic univariate and multivariate analyses were used for the relation between clinical pathological features and LVI. Survival analysis was used to study the relation between vascular invasion and survival rate in patients with stage Ⅰ gastric cancer. Results Univariate analysis showed that tumor size, type of differentiation, depth of invasion, lymph node metastasis, TNM stage, Lauren classification, nerve invasion and the increase of CEA, CA125 and CA199 were risk factors for vascular invasion (P < 0.05). Multivariate analysis showed that poor differentiation, deep invasion, lymph node metastasis, nerve invasion and elevated CA724 were independent risk factors for vascular invasion. The 5-year survival rate of stage Ⅰ gastric cancer patients with vascular invasion was significantly lower than that without vascular invasion (P < 0.01). Conclusion Gastric cancer patients with poor differentiation, deep invasion, lymph node metastasis, nerve invasion and elevated CA724 are more prone to vascular invasion. Patients with stage I gastric cancer at risk of vascular invasion should be treated more aggressively.

In the past decade, the number of cars in China has significantly raised, but the traffic jam spree problem has brought great inconvenience to people's travel. Accurate and efficient traffic flow prediction, as the core of Intelligent Traffic System (ITS), can effectively solve the problems of traffic travel and management. The existing short-term traffic flow prediction researches mainly use the shallow model method, so they cannot fully reflect the traffic flow characteristics. Therefore, this paper proposed a short-term traffic flow prediction method based on one-dimensional convolution neural network and long short-term memory (1DCNN-LSTM). The spatial information in traffic data is obtained by 1DCNN, and then the time information in traffic data is obtained by LSTM. After that, the space-time features of the traffic flow are used as regression predictions, which are input into the Fully-Connected Layer. In the end, the corresponding prediction results of the current input are calculated. In the past, most of the researches are based on survey data or virtual data, lacking authenticity. In this paper, real data will be used for research. The data are provided by OpenITS open data platform. Finally, the proposed method is compared with other road forecasting models. The results show that the structure of 1DCNN-LSTM can further improve the prediction accuracy. [ABSTRACT FROM AUTHOR]