Objective To investigate the effects of disulfiram (DS) combined with Cu on the human Burkitt lymphoma cell xenografts in nude mice.Methods Burkitt lymphoma xenograft was established by subcutaneous injection of Raji cell into nude mice after 2 Gy whole body X-irradiation (1×107 Raji cells were resuspended in 200 μl saline).18 bearing tumor mice were randomly divided into control group,DS group and DS/Cu group.During the experiment,the effects of DS/Cu on the nude mice with tumors were examined,including the tumor volumes,weights and the growth curves of xenograft tumor.Histopathological examination of tumor tissue was observed with optical microscape.The protein expression levels of p-JNK and c-jun were also detected by Western blot.Results Subsequent tumor size and weight in DS or DS/Cu-treated animals were (67.71±2.15) mm3,(33.35±7.74) mm3 and (43.35±4.22) mg,(18.05±2.88) mg.One-way ANOVA analysis indicated that the tumor size and weight in DS or DS/Cu-treated animals were reduced significantly relative to tumors in vehicle-treated animals (F =27.579,P =0.000;F =16.369,P =0.000).Furthermore,multiple comparisons revealed that the DS or DS/Cu-treated animals had significantly reduced tumor size and weight compared with control animals (all P ＜ 0.05).There were significant differences in tumor size and weight between DS or DS/Cu-treated animals (both P ＜ 0.05).Tumor inhibition rates in DS or DS/Cu group were 63.48 ％ and 80.24 ％,respectively.An increase of apoptosis changes in the xenograft tumor cells in DS or DS/Cu treated mice were more significant.Westem blot showed that the p-JNK and c-jun protein expressions in the tumors were improved after the DS or DS/Cu treatment,more obvious in DS/Cu treatment.Conclusion DS/Cu can inhibit the growth of xenografts,and one possible mechanism may involve the regulation of JNK signal pathway.

Objective:To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients.Methods:Based on the patients’ admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model.Results:In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94 %, peripheral white blood cells count >8.0×10 9, change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7 %, and the missed diagnosis rate was 38.3 %. The prediction sensitivity of the model based on the test set was 75.0 %, and the missed diagnosis rate was 25.0 %. Conclusions:Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model’s prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.