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Objective·To explore the prognosis effects of tumor-stroma ratio(TSR), perineural invasion (PNI) and lymphovascular invasion (LVI) on patients with colorectal cancer (CRC). Methods·Data of 948 patients diagnosed with CRC in Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine from January 2014 to December 2018 were retrospectively analyzed, and according to pathological risk factors TSR, PNI and LVI, patients were divided into high risk group (n=81) and low risk group (n=867). After 1-1 matching with SPSS software, 67 patients of the high risk group and 67 patients of the low risk group were obtained. The overall survival (OS) time was compared between patients in the high risk group and low risk group. The results were validated by using tissue microarray (TMA) dataset of 106 CRC patients from our research group. The clinical characteristics of the high risk group and low risk group were compared. Kaplan-Meier curve was used for survival analysis, and COX regression model was used to analyze the prognosis factors. Results·Patients in the high risk group had a worse prognosis with a short median OS (27.7 months) than patients in the low risk group (31.1 months, P=0.000). The worse OS in patients with high risk group was validated in matched data and TMA dataset. Conclusion·CRC with positive perineural lymphovascular invasion combined with high TSR is a type of CRC with poor prognosis, which can effectively make up for the defect of tumor-node-metastasis (TNM) stage in prognosis and can be used to predict postoperative disease development of CRC patients and guide treatment. [ABSTRACT FROM AUTHOR]

The positioning scheme based on mobile cellular network technology is one of the important technical approaches to provide network optimization, emergency rescue, police patrol and location services. The traditional positioning scheme based on cell base station location information has low positioning accuracy and large positioning error, so it cannot meet the requirements of some positioning applications. The scheme based on fingerprint location can greatly improve the location accuracy, save computational cost and enhance the usability based on the coarse location scheme of the cell and become the hotspot of the research. Rasterization and non-rasterization of outdoor fingerprint location scheme based on machine learning were studied and analyzed to meet the business requirements of outdoor fingerprint location. By means of parameter weighting, data fitting and other methods, large-scale fingerprint data were cleaned to improve the effectiveness of data sources. Through the realization of sub-modules such as demarcating research area, rasterizing, constructing fingerprint database, training model, correcting model, non-rasterizing, rough positioning coupling, matching parameter and training parameter, the operation efficiency and positioning accuracy of the algorithm were analyzed and optimized, and the key indexes affecting the algorithm performance were determined. Then, the performance of two fingerprint-based localization schemewas analyzed based on the simulation results. Finally, the typical scenarios of the fingerprint location scheme based on machine learning in practical application were presented. [ABSTRACT FROM AUTHOR]