Objective To investigate whether the expression of metal matrix protease-9 (MMp-9) changes with altitude in severe acute pancreatitis (SAP) lung injury by establishing SAP model in rats.The related indexes of pancreas and lung injury and MMp-9 were measured.Methods 280 SPF male Wistar rats were randomly assigned to different altitude groups to establish SAP model,then the serum amylase (AMY) content of each group were detected.The degree of pancreatic injury and pancreatic pathological score were observed under light microscope.The expression level of MMP-9 in lung tissues was measured and analyzed by statistical methods.Results (1) At the same altitude,the expression of MMP-9 in each time group increased with time,with statistically significant difference (P ≤ 0.05).(2) AMY value,pathological score of pancreas and expression of MMP-9 in lung tissue increased with the altitude in the same time group at different altitudes,with statistically significant difference (P ≤ 0.05).Conclusions The higher the altitude,the more severe the damage of SAP and lung,and the higher the expression level of MMP-9.

Artificial intelligence (AI) has important significance and great promise in the pathological diagnosis,imaging diagnosis,prognosis prediction,and molecular subtyping of prostate cancer (PCa). This review focuses on the progress of AI for the diagnosis and molecular classification of PCa,and briefly introduces the application of AI in the pathological diagnosis of needle biopsy and Gleason grading,pathological diagnosis and grading after prostatectomy,and prognosis prediction of PCa patients based on pathological sections. For the pathological diagnosis of needle biopsy and Gleason grading,AI has already comparable to general pathologists;for the pathological diagnosis and grading after prostatectomy,AI can accurately grade and classify tumors;and for the prognosis prediction of PCa patients,AI can directly extract relevant prognostic information from pathological tissue sections for prognosis prediction. In addition,AI can also predict gene mutations in PCa patients and suggest the probability of gene mutation by analyzing the pathological sections.