To reveal the anti-inflammation mechanism of koumine,we determined effects of different concentrations of koumine(100,200,400 mg/L) on the secretion of NO,IL-1β,IL-6 and TNF-a by nitrate reductase and ELISA as well as the mRNA of iNOS,IL-1β,IL-6 and TNF-α.Western blot was used to detect iNOS protein expression.The results showed that 100,200,400 mg/L of koumine can significantly inhibit the secretion of NO,IL-1β,IL-6 and TNF-α of RAW264.7 cell (P＜0.01).Koumine can dose-dependently down-regulate the mRNA of iNOS,IL-1β,IL-6 and TNF-α,meanwhile koumine can also significantly inhibit the protein expression of iNOS.The results indicated that koumine may play an anti-inflammation activity by mean of the reduction of NO and mediator of inflammation,down-regulating the mRNA expression of iNOS,IL-1β,IL-6 and TNF-a,decreasing protein expressions of iNOS.

Shortcomings have been inherited in the traditional influenza early warning system, often expressed through the scope, accuracy on prediction and real-time performance of the monitor related programs. With the new round of scientific and technological revolution and the increasingly maturity of modern information system, related technology on influenza early warning has become the focus of research in this field, based on big data analysis technology. Using the traditional influenza surveillance and early warning system as reference, this paper summarizes the progress of influenza early warning research, based on the Internet, influencing factors, time and space trends, and risk assessment etc., to summarize the trends on the advantages, shortcomings and future development of big data, used in the early warning system on influenza.

Objective:To establish a prediction model of acute gastrointestinal injury (AGI) above grade II in elderly patients with severe pneumonia, and to evaluate and validate the model internally.Methods:A retrospective analysis was performed on 268 patients aged >65 years with severe pneumonia admitted to the Second People′s Hospital of Hefei from June 2019 to May 2022 (207 cases in the training set and 61 cases in the verification set). Sixteen indicators, including age, sex, underlying disease, pneumonia Severity index (PSI) score, dosage of sedative and analgesic drugs, and mechanical ventilation time of all patients were collected. After logistic regression analysis in the training set, a model was established to predict AGI above grade Ⅱ in elderly patients with severe pneumonia. Receiver operating characteristic (ROC) curve was drawed and correction curve was used to evaluate the reliability of the model. The model was internally validated by validation set data.Results:Among 207 patients with severe pneumonia in the training set, 50 patients developed AGI above grade Ⅱ during treatment. The prediction model was established by logistic regression analysis as follows: When L=Sequential Organ Failure Assessment (SOFA)×0.181+ PSI score×0.066+ propofol dosage×0.607+ reifentanil dosage×1.187, L>19.288, it can be considered that patients with severe pneumonia have a 93.24% chance of developing grade Ⅱ or above AGI. The ROC curve showed that the model was well differentiated, AUC=0.960. H-L test indicated (χ 2=7.39, P=0.496>0.05) the model fit was good. The sensitivity and specificity of the model were 82.00% and 96.82% respectively. AUC=94.58% (sensitivity 81.25%, specificity 93.33%), H-L test indicated ( χ 2=4.51, P=0.808>0.05) the prediction accuracy was 90.16%. Conclusions:The prediction model for AGI after severe pneumonia in elderly patients can be used clinically to help predict the occurrence of AGI in elderly patients with multiple injuries.