Objective To explore the influencing factors of readmission within 90 days in frail patients with chronic obstructive pulmonary disease(COPD),construct an early warning model and evaluate its discrimination and effectiveness.Methods A total of 831 COPD patients with frailty were included and divided into the readmission group(290 cases)and the control group(541 cases)based on whether they were readmitted within 90 days after discharge.The electronic medical record data were collected and compared between the two groups of patients.Multivariate Logistic regression analysis was used to screen independent influencing factors for readmission within 90 days in COPD weak patients.A predictive model was established.The column chart was drawn.The discriminability of the predictive model was evaluated through receiver operating characteristic(ROC)curves.The clinical practicality and the predictive model calibration were evaluated through decision curves and calibration curves.Results The body mass index(BMI)of the readmission group was lower than that of the non readmission group,and the proportion of acute exacerbation of COPD hospitalizations≥2 times in the past year,the Edmonton frailty scale(REFS)score and the Chalson comorbidity index(CCI)were higher in the readmission group than those of the non readmission group(P＜0.05).Multivariate Logistic regression analysis showed that BMI(OR=0.721,95%CI:0.636-0.818),the number of acute exacerbations of COPD hospitalizations in the past year(OR=3.040,95%CI:1.944-4.753),REFS score(OR=1.726,95%CI:1.486-2.005)and CCI score(OR=3.917,95%CI:3.079-4.983)were all independent influencing factors for readmission within 90 days in COPD frail patients(P＜0.05).Based on the results of multiple factor Logistic regression analysis,the column chart warning model was constructed.The AUC value of the ROC curve was 0.847(95%CI:0.820-0.874),the specificity was 87.20%and the sensitivity was 64.50%.The calibration curve of the predictive model was close to the diagonal,and the calibrationa degree was good.The decision curve suggested that the prediction model had clinical practicality.Conclusion The early warning model constructed based on the influencing factors of readmission hospitalization within 90 days in weak patients with COPD has good predictive effect and clinical practicability.

Objective To explore the influencing factors of readmission within 90 days in frail patients with chronic obstructive pulmonary disease(COPD),construct an early warning model and evaluate its discrimination and effectiveness.Methods A total of 831 COPD patients with frailty were included and divided into the readmission group(290 cases)and the control group(541 cases)based on whether they were readmitted within 90 days after discharge.The electronic medical record data were collected and compared between the two groups of patients.Multivariate Logistic regression analysis was used to screen independent influencing factors for readmission within 90 days in COPD weak patients.A predictive model was established.The column chart was drawn.The discriminability of the predictive model was evaluated through receiver operating characteristic(ROC)curves.The clinical practicality and the predictive model calibration were evaluated through decision curves and calibration curves.Results The body mass index(BMI)of the readmission group was lower than that of the non readmission group,and the proportion of acute exacerbation of COPD hospitalizations≥2 times in the past year,the Edmonton frailty scale(REFS)score and the Chalson comorbidity index(CCI)were higher in the readmission group than those of the non readmission group(P＜0.05).Multivariate Logistic regression analysis showed that BMI(OR=0.721,95%CI:0.636-0.818),the number of acute exacerbations of COPD hospitalizations in the past year(OR=3.040,95%CI:1.944-4.753),REFS score(OR=1.726,95%CI:1.486-2.005)and CCI score(OR=3.917,95%CI:3.079-4.983)were all independent influencing factors for readmission within 90 days in COPD frail patients(P＜0.05).Based on the results of multiple factor Logistic regression analysis,the column chart warning model was constructed.The AUC value of the ROC curve was 0.847(95%CI:0.820-0.874),the specificity was 87.20%and the sensitivity was 64.50%.The calibration curve of the predictive model was close to the diagonal,and the calibrationa degree was good.The decision curve suggested that the prediction model had clinical practicality.Conclusion The early warning model constructed based on the influencing factors of readmission hospitalization within 90 days in weak patients with COPD has good predictive effect and clinical practicability.

Primary ureteral plasmacytoid carcinomas is a rare tumor with high grade and poor diagnosis. Pathological and immunohistochemical staining play an extremely key role in diagnosis since there is no specific clinical and radiological evidence. The surgical removement is the first line treatment. Herein, we report a case of ureteral plasmacytoid carcinoma that was well controlled with multimodal therapy.

Objective: To evaluate the risk factors of postoperative upgrade to pT₃a of cT₁ renal cell carcinoma, and to establish a nomogram prediction model to improve the ability of predicting locally advanced renal cell carcinoma and provide a reference for clinical surgical decision-making. Methods: Clinical data of 1 376 patients with cT₁ (diameter ≤ 7 cm) renal tumor hospitalized for surgery from January 2010 to December 2016 were retrospectively analyzed. There were 979 males and 397 females, with the mean age of (57.65±10.92) years. The mean body mass index (BMI) was (25.47±3.27) kg/m² and the average tumor size was (4.02±1.52) cm. There were 711 tumors on the left and 665 on the right. There were 363 cases with clinical symptoms, 567 patients with smoking history , 732 cases with history of chronic disease. There were 289 cases with tumor necrosis, 636 cases with tumor protrusion, 822 cases with irregular tumor , and 738 cases with renal sinus compression. Partial nephrectomy and radical nephrectomy were performed in 396 cases and 980 cases respectively. Mann-whitney U test and chi-square test were used for univariate analysis, logistic regression analysis was used for multivariate analysis to analyze the predictors of upgrading, R software was used to construct the nomogram predictive model, C-index was used to evaluate the model discrimination, and calibration curve method was used to evaluate the consistency of the model. Results: Postoperative pathology of total 1 376 cases showed that there were 1 195 cases of clear cell carcinoma of kidney, 48 cases of papillary cell carcinoma, 57 cases of chromophobe cell carcinoma, and 76 cases of other types. Among the 1 376 patients with cT₁ renal tumor, 75 patients were upgraded to pT_3a, accounting for 5.5% of all patients. Univariate analysis showed that the patients who upgraded to pT_3a were older [(63.08±10.17) years old and (57.34±10.88) years old], and the tumor length and diameter were larger [(5.24±1.35) cm and (3.95±1.51) cm]. Patients with clinical symptoms [46.7% (35/75) vs. 25.2%(328/1 301)], patients with CT indication of tumor necrosis [40.0%(30/25 975) vs. 19.9% (259/1 301)], patients with irregular tumor contour [73.3%(55/76 775) vs. 59.0%(767/1 301)], and patients with radical nephrectomy were higher [(92.1% (70/91 075) vs. 70%(910/1 301)]. All the differences were statistically significant (P<0.01). Multivariate analysis showed that the independent predictors of upgrading were age (OR=1.046, P<0.001), larger tumor (OR=1.504, P<0.001), clinically symptom (OR=2.153, P=0.004), irregular tumor profile (OR=2.466, P=0.002), and tumor necrosis on CT (OR=2.588, P<0.001). The C-index was 0.808, the calibration curve of forecasting curve with the standard curve fit was good, and the prediction of renal cancer are better in predict consistency. Conclusions Based on the five preoperative predictors, including age, tumor size, clinical presence or absence of symptoms, tumor profile, and whether or not the tumor necrosis indicated by CT, this study developed a nomogram of cT₁ renal cancer upgrade to pT_3a. This nomogram has a good statistical significance, and this model can provide prognosis consultation for patients and provide reference for doctors to make decisions before treatment.

Objective To investigate the role of miR-203 in radiation-induced thymic lymphoma (RITL).Methods A 60Co irradiator was used for total-body irradiation.MicroRNAs(miRNAs) level was assayed by qRT-PCR.Cell proliferation was assayed by MTT assay.Cell apoptosis was examined by fluorescence activated cell sorter (FACS).Dual luciferase reporter assay system was used to detect the 3'UTR reporter.Results MiR-203 was down-regulated in RITL tissues.Overexpression of miR-203 strongly inhibited the proliferation of both NIH3T3 cells and EL4 cells and vice versa.MiR-203 inhibited cells proliferation and induced apoptosis via TANK-binding kinase (TBK1),SLUG (SNAI2) and Cyclin D1 (CCND1).Conclusions Radiation down-regulated the level of miR-203 in thymic,which promoted radiation-induced thymic lymphoma by targeting TBK1,SNAI2 and CCND1.