Objective To investigate Hospital-acquired infections in patients with liver cirrhosis caused by relevant factors.Methods From Jul.2006 to Jan.2011 the clinical data of 476 cases of hospital-acquired infections in patients with decompensate cirrhosis were retrospectively analyzed by case-control study for the effective factors.Results By logistic regression analysis,17 factors are found to affect Hospital-acquired infections ; 16 risk factors:Occupation in manual labor ( OR =4.119,95 ％ CI:2.631-6.325 ) ; Age ( OR =3.014,95 ％ CI:1.163-7.136) ; The cirrhosis disease history ( OR =1.761,95 ％ CI:1.439-2.130) ; Length of stay in hospital (OR =17.354,95 ％ CI:2.539-101.304) ; Interventional procedures ( OR =5.379,95％ CI:2.354-17.594) ;Peotein intake ( OR =3.201,95％ CI:1.539-4.528) ; Alcohol drinking history ( OR =3.158,95％CI:2.274-7.153 ) ; Development of complication ( OR =8.367,95 ％ CI:2.023-11.736 ) ; ALB ( OR =4.613,95％ CI:2.157-9.936 ) ; PCR-HBV DNA quantitative ( OR =3.628,95％ CI:2.245-7.129 ) ; WBC ( OR =3.758,95％ CI:2.276-7.018 ) ; CHE ( OR =3.148,95％ CI:2.202-6.038 ) ; TC ( OR =3.210,95％ CI:2.102-5.107) ;TBIL(OR =2.748,95％ CI:1.283-3.153) ; Antiviral agents (OR =0.257,95％ CI:0.145-0.382 ) ; Preventive application of antibiotics ( OR =3.147,95％ CI:2.236-7.182 ) ; PTA ( OR =2.798,95％CI:1.293-4.182) ;Liver function of Child B and C (OR =4.164,95％ CI:2.236-6.761 ).Conclusion Age,length of stay in hospital,interventional procedures,alcohol drinking history,development of complication,ALB,PCR-HBVDNA quantitative,WBC,TC,Preventive application of antibiotics,liver function of Child are risk factors.Use of anti-virus drug are protective factors.

Objective:To construct a clinical-radiomics model based on MRI, and to explore its predictive value for biochemical recurrence (BCR) after radical prostatectomy in prostate cancer patients.Methods:A total of 212 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Soochow University from January 2015 to December 2018 and had complete follow-up data were retrospectively analyzed. The random toolkit of Python language was used to randomly sample the patients at a ratio of 7∶3 without replacement, and they were divided into a training set (149 cases) and a test set (63 cases). The endpoint of follow-up was BCR or at least 3 years. BCR occurred in 50 patients in the training group and 21 patients in the test group. The imaging features of the main lesion area in the preoperative T 2WI, diffusion-weighted imaging and apparent diffusion coefficient map of patients in the training set were extracted, and the unsupervised K means clustering algorithm was used to screen the features. The selected features were fitted by a multivariate Cox regression model, and the radiomics model was constructed. Univariate Cox regression analyses were used to screen the main clinical risk factors associated with BCR, and the clinical-radiomics model was constructed combined with RadScore. In the test set, the time-dependent receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of the radiomics model, clinical-radiomics model and prostate cancer risk assessment after radical resection (CAPRA-S) score for the occurrence of BCR. Harrell consistency index (C-index) was used to evaluate the model to predict BCR consistency. The calibration curve was used to evaluate the degree of variation of the model. The decision curve was used to evaluate the clinical application value of the prediction model. Results:A total of 26 radiomics features were screened to establish the radiomics model. The univariate Cox showed that the preoperative clinical features included preoperative prostate-specific antigen level (HR=1.006, 95%CI 1.002-1.009, P=0.001), Gleason score of biopsy (HR=1.422, 95%CI 1.153-1.753, P=0.001), clinical T stage (HR=1.501, 95%CI 1.238-1.822, P<0.001). The multivariate Cox showed that the RadScore was an independent predictor of BCR after radical prostatectomy (HR=51.214, 95%CI 18.226-143.908, P<0.001). The selected preoperative clinical features were combined with RadScore to construct a clinical-radiomics model. In the test set, the AUCs of the time (3 years)-dependent ROC curves of the radiomics model, the clinical-radiomics model, and the CAPRA-S score were 0.824 (95%CI 0.701-0.948), 0.841 (95%CI 0.714-0.968), and 0.662 (95%CI 0.518-0.806), respectively. The C-index of the radiomics model, clinical-radiomics model and CAPRA-S score were 0.784 (95%CI 0.660-0.891), 0.802 (95%CI 0.637-0.912) and 0.650 (95%CI 0.601-0.821), respectively. The calibration curve showed that the predicted probability and actual probability of BCR by radiomics model, clinical-radiomics model and CAPRA-S score were in good agreement (χ 2=7.64, 10.61, 6.37, P=0.465, 0.225, 0.498). The decision curve showed that the clinical net benefit of the clinical-radiomics model and the radiomics model was significantly higher than the CAPRA-S score. When the threshold probability was 0.20-0.30, 0.40-0.50, and >0.55, the clinical net benefit of the clinical radiomics model was higher than that of the radiomics model. Conclusions:The clinical-radiomics model can effectively predict the occurrence of BCR in patients with prostate cancer after radical prostate ctomy, and the prediction efficacy is better than the radiomics model and CAPRA-S score.

Objective:To investigate the predictive value of postoperative C-reactive protein for serious complications after Da Vinci robotic surgical system radical gastrectomy of gastric cancer.Methods:The retrospective case-control study was conducted. The clinicopathological data of 298 patients with advanced gastric cancer who underwent Da Vinci robotic surgical system radical gastrectomy in the 940th Hospital of Joint Logistics Support Force of Chinese People's Liberation Army from January 2017 to June 2019 were collected. There were 253 males and 45 females, aged from 24 to 86 years, with a median age of 60 years. Of the 298 patients, 275 cases underwent no serious postoperative complications and 23 cases underwent serious postoperative complications. Observation indicators: (1) serious postoperative complications; (2) analysis of risk factors for serious postoperative complications after Da Vinci robotic surgical system radical gastrectomy of gastric cancer; (3) performance evaluation of the predictive indicators. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers and comparison between groups was conducted using the chi-square test or Fisher exact probability. Comparison of ordinal data was conducted using the rank sum test. Univariate analysis was conducted using the chi-square test. Multivariate analysis was conducted using Logistic regression model. The receiver operating characteristic curve was drawn and the area under curve (AUC) was used to compare and estimate the efficiency of diagnostic criteria. The value of Youden index was used to determine the optimal cut-off point. Results:(1) Serious postoperative complications: of the 298 patients, 23 cases underwent complications classified ≥grade Ⅲa of Clavien-Dindo classifica-tion, including 10 cases with grade Ⅲa complications, 7 cases with grade Ⅲb complications, 4 cases with grade Ⅳa complications, 1 case with grade Ⅳb complications and 1 case with grade Ⅴ complications. (2) Analysis of risk factors for serious postoperative complications after Da Vinci robotic surgical system radical gastrectomy of gastric cancer. Results of univariate analysis showed that operation time, indicators of C-reactive protein concentration and neutrophil count at post-operative day 1, and indicators of C-reactive protein concentration, white blood cells count, neutrophil count and platelet count at postoperative day 3 and pathological stage were related factors affecting serious complications for advanced gastric cancer after Da Vinci robotic surgical system radical gastrectomy ( χ2=7.671, 4.504, 5.045, 48.293, 9.575, 15.436, 13.731, 9.537, P<0.05). Results of multivariate analysis showed that the operation time ≥250 minutes, the concentration of C-reactive protein at postoperative day 3 ≥16.65 mg/dL, the neutrophil count at postoperative day 3 ≥8.167×10 9/L, the platelet count at postoperative day 3 ≥218×10 9/L and the pathological stage of tumor as stage Ⅱ and stage Ⅲ were independent risk factors affecting serious complications for advanced gastric cancer after Da Vinci robotic surgical system radical gastrectomy ( odds ratio=3.721, 16.084, 6.056, 6.893, 12.455, 95% confidence interval: 1.032-13.421, 4.657-55.547, 1.073-34.163, 1.798-26.423, 1.338-115.930, P<0.05). (3) Performance evaluation of the predictive indicators: the C-reactive protein concentration at postoperative day 3 was a high-performance predictor with the AUC as 0.851 (95% c onfidence interval: 0.780-0.921, P<0.05) and neutrophil count and platelet count at postoperative day 3 were low-performance predictors with the AUC as 0.659 and 0.666 (95% confidence interval: 0.570-0.748 and 0.581-0.750, P<0.05). Conclusion:The C-reactive protein concentration ≥16.65 mg/dL at postoperative day 3 is a high performance predictive indicator for serious complications after Da Vinci robotic surgical system radical gastrectomy of gastric cancer.

Objective:To investigate the expression and clinical significance of cAMP response element-binding protein 3-like 1 (CREB3L1) in gastric cancer.Methods:A total of 97 patients who received surgical resection of gastric cancer in Lanzhou University Second Hospital from Jan. 2019 to Dec. 2020 were selected as the study subjects. Immunohistochemistry was used to detect the expression level of CREB3L1 in gastric cancer tissues and matched paracancer tissues. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the expression level of CREB3L1 in gastric cancer and adjacent tissues. Statistical methods were used to analyze the relationship between the expression level of CREB3L1 in gastric cancer tissues and the degree of differentiation of tumor cells, tumor size, depth of invasion, TNM staging and other clinicopathological data, and Logistic regression analysis was used to study the risk factors of gastric cancer. To explore the clinical significance of CREB3L1 expression level in gastric cancer.Results:Immunohistochemical results showed that CREB3L1 protein was mainly expressed in the nucleus. The positive rate in gastric cancer tissues was 17.5% (17 cases), which was lower than that in normal adjacent tissues 84.5% (82 cases), and the difference was statistically significant ( χ2=87.15, P<0.001). qRT-PCR was used to detect the expression of CREB3L1 in gastric cancer and adjacent tissues. The results showed that the expression level of CREB3L1 was significantly higher in adjacent tissues than in cancer cells. The results were statistically significant ( P<0.05). The positive expression rate of CREB3L1 was decreased in the cancer tissues of gastric cancer patients, and its expression level was correlated with the degree of tumor differentiation, tumor size, invasion depth and TNM stage ( P<0.05), but not with Lauren classification and tumor location ( P>0.05). Logistic regression analysis showed that the positive expression level of CREB3L1 was correlated with the degree of tumor differentiation in gastric cancer patients ( P<0.05). Conclusion:The expression of CREB3L1 is decreased in gastric cancer, which is related to the degree of tumor differentiation, tumor size, invasion depth and TNM stage, which is of great value in early and accurate diagnosis of benign and malignant gastric cancer.

To establish the experts consensus on the management of delirium in critically ill patients.A special committee was set up by 15 experts from the Chinese Critical Hypothermia-Sedation Therapy Study Group.Each statement was assessed based on the GRADE (Grading of Recommendations Assessment,Development,and Evaluation) principle.Then the Delphi method was adopted by 36 experts to reassess all the statements.(1) Delirium is not only a mental change,but also a clinical syndrome with multiple pathophysiological changes.(2) Delirium is a form of disturbance of consciousness and a manifestation of abnormal brain function.(3) Pain is a common cause of delirium in critically ill patients.Analgesia can reduce the occurrence and development of delirium.(4) Anxiety or depression are important factors for delirium in critically ill patients.(5) The correlation between sedative and analgesic drugs and delirium is uncertain.(6) Pay attention to the relationship between delirium and withdrawal reactions.(7) Pay attention to the relationship between delirium and drug dependence/ withdrawal reactions.(8) Sleep disruption can induce delirium.(9) We should be vigilant against potential risk factors for persistent or recurrent delirium.(10) Critically illness related delirium can affect the diagnosis and treatment of primary diseases,and can also be alleviated with the improvement of primary diseases.(11) Acute change of consciousness and attention deficit are necessary for delirium diagnosis.(12) The combined assessment of confusion assessment method for the intensive care unit and intensive care delirium screening checklist can improve the sensitivity of delirium,especially subclinical delirium.(13) Early identification and intervention of subclinical delirium can reduce its risk of clinical delirium.(14) Daily assessment is helpful for early detection of delirium.(15) Hopoactive delirium and mixed delirium are common and should be emphasized.(16) Delirium may be accompanied by changes in electroencephalogram.Bedside electroencephalogram monitoring should be used in the ICU if conditions warrant.(17) Pay attention to differential diagnosis of delirium and dementia/depression.(18) Pay attention to the role of rapid delirium screening method in delirium management.(19) Assessment of the severity of delirium is an essential part of the diagnosis of delirium.(20) The key to the management of delirium is etiological treatment.(21) Improving environmental factors and making patient comfort can help reduce delirium.(22) Early exercise can reduce the incidence of delirium and shorten the duration of delirium.(23) Communication with patients should be emphasized and strengthened.Family members participation can help reduce the incidence of delirium and promote the recovery of delirium.(24) Pay attention to the role of sleep management in the prevention and treatment of delirium.(25) Dexmedetomidine can shorten the duration of hyperactive delirium or prevent delirium.(26) When using antipsychotics to treat delirium,we should be alert to its effect on the heart rhythm.(27) Delirium management should pay attention to brain functional exercise.(28) Compared with non-critically illness related delirium,the relief of critically illness related delirium will not accomplished at one stroke.(29) Multiple management strategies such as ABCDEF,eCASH and ESCAPE are helpful to prevent and treat delirium and improve the prognosis of critically ill patients.(30) Shortening the duration of delirium can reduce the occurrence of long-term cognitive impairment.(31) Multidisciplinary cooperation and continuous quality improvement can improve delirium management.Consensus can promote delirium management in critically ill patients,optimize analgesia and sedation therapy,and even affect prognosis.

The dysregulation of transcription factors is widely associated with tumorigenesis. As the most well-defined transcription factor in multiple types of cancer, c-Myc can transform cells by transactivating various downstream genes. Given that there is no effective way to directly inhibit c-Myc, c-Myc targeting strategies hold great potential for cancer therapy. In this study, we found that WSB1, which has a highly positive correlation with c-Myc in 10 cancer cell lines and clinical samples, is a direct target gene of c-Myc, and can positively regulate c-Myc expression, which forms a feedforward circuit promoting cancer development. RNA sequencing results from Bel-7402 cells confirmed that WSB1 promoted c-Myc expression through the β-catenin pathway. Mechanistically, WSB1 affected β-catenin destruction complex-PPP2CA assembly and E3 ubiquitin ligase adaptor β-TRCP recruitment, which inhibited the ubiquitination of β-catenin and transactivated c-Myc. Of interest, the effect of WSB1 on c-Myc was independent of its E3 ligase activity. Moreover, overexpressing WSB1 in the Bel-7402 xenograft model could further strengthen the tumor-driven effect of c-Myc overexpression. Thus, our findings revealed a novel mechanism involved in tumorigenesis in which the WSB1/c-Myc feedforward circuit played an essential role, highlighting a potential c-Myc intervention strategy in cancer treatment.