ObjectiveTo screen compounds or drugs can affect the hypoxia induced-gene expression of retinal vascular endothelial cell based on gene expression microarrays and connectivity map (CMAP) technology. MethodsTotally 326 up-regulated and down-regulated genes of hypoxic human embryonic retinal microvascular endothelial cells minduced by cobalt chloride in the previous study were converted into query signature format documents. Gene profile of the disease characteristics was then compared with that of control in CMAP website database, positive and negative compounds related to retinopathy of prematurity (ROP) were finally screened out. Results44 and 18 compounds or drugs have positive and negative relationship with ROP respectively by searching CMAP database with differentially expressed genes. Ciclopirox, cobalt chloride, gossypol and withaferin A have positive relationship with ROP. Cyclic adenosine monophosphate, harmalol, naringin and probenecid have a negative effect on ROP. ConclusionsCiclopirox, cobalt chloride, gossypol and withaferin A have a positive effect on ROP. However, cyclic adenosine monophosphate, harmalol, naringin and probenecid have a negative effect.
The drug-target protein interaction prediction can be used for the discovery of new drug effects. Recent studies often focus on the prediction of an independent matrix filling algorithm, which apply a single algorithm to predict the drug-target protein interaction. The single-model matrix-filling algorithms have low accuracy, so it is difficult to obtain satisfactory results in the prediction of drug-target protein interaction. AdaBoost algorithm is a strong multiple classifier combination framework, which is proved by the past researches in classification applications. The drug-target interaction prediction is a matrix filling problem. Therefore, we need to adjust the matrix filling problem to a classification problem before predicting the interaction among drug-target protein. We make full use of the AdaBoost algorithm framework to integrate several weak classifiers to improve performance and make accurate prediction of drug-target protein interaction. Experimental results based on the metric datasets show that our algorithm outperforms the other state-of-the-art approaches and classical methods in accuracy. Our algorithm can overcome the limitations of the single algorithm based on machine learning method, exploit the hidden factors better and improve the accuracy of prediction effectively.
Aiming at the problem of scaffold degradation in bone tissue engineering, we studied the feasibility that controlls bone defect repair effect with the inhomogeneous structure of scaffold. The prediction model of bone defect repair which contains governing equations for bone formation and scaffold degradation was constructed on the basis of analyzing the process and main influence factors of bone repair in bone tissue engineering. The process of bone defect repair and bone structure after repairing can be predicted by combining the model with finite element method (FEM). Bone defect repair effects with homogenous and inhomogeneous scaffold were simulated respectively by using the above method. The simulation results illustrated that repair effect could be impacted by scaffold structure obviously and it can also be controlled via the inhomogeneous structure of scaffold with some feasibility.
ObjectiveTo predict as well as bioinformatically analyze the target genes of has-miR-451. MethodsmiRBase, miRanda, TargetScan and PicTar were used to predict the target genes of hsa-miRNA-451. The functions of the target genes were demonstrated by Gene Ontology and pathway enrichment analysis. P < 0.05 was set as statistically significant. Results18 target spots of hsa-miRNA-451 were predicted by 3 databases or prediction software at least. The functions of the target genes were enriched in proliferation and development of epithelial cells and regulation of kinase activity (P < 0.05). Pathway analysis showed that transforming growth factor-beta signaling pathway, mitogen-activated protein kinase signaling pathway, epidermal growth factor signaling pathway, Wnt signaling pathway and mammalian target of rapamycin signaling pathway were significantly enriched (P < 0.05). Conclusionhsa-miRNA-451 might be involved in various signaling pathways related to proliferation and development of epithelial cells.
ObjectiveTo analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019 and forecast its change in the next 10 years. MethodsThe Global Burden of Disease database 2019 was used to analyze the burden of digestive diseases attributed to smoking in China from 1990 to 2019. Joinpoint regression model was used to analyze the time variation trend. A time series model was used to predict the burden of digestive diseases attributable to smoking over the next 10 years. ResultsIn 2019, there were 12 900 deaths from digestive diseases attributed to smoking in China, with a DALY of 398 600 years, a crude death rate of 0.91/100 000 and a crude DALY rate of 28.02/100 000. The attributed standardized mortality rate was 0.69 per 100 000, and the standardized DALY rate was 19.79 per 100 000, which was higher than the global level. In 2019, the standardized mortality rate and DALY rate of males were higher than those of females (1.48/ 100 000 vs. 0.11/ 100 000, 38.42/ 100 000 vs. 293/100 000), and the standardized rates of males and females showed a downward trend over time. In 2019, both mortality and DALY rates from digestive diseases attributed to smoking increased with age. ARIMA predicts that over the next 10 years, the burden of disease in the digestive system caused by smoking will decrease significantly. ConclusionFrom 1990 to 2019, the burden of digestive diseases attributed to smoking showed a decreasing trend in China, and the problem of disease burden is more serious in men and the elderly population. A series of effective measures should be taken to reduce the smoking rate in key groups. The burden of digestive diseases caused by smoking will be significantly reduced in the next 10 years.
ObjectiveTo summarize the research progress of early allograft dysfunction (EAD) predictors after liver transplantation. MethodThe literatures about the studies of predictive predictors of EAD after liver transplantation in recent years were reviewed. ResultsThe EAD was closely related to the prognosis and long-term survival of patients. In recent years, there were some reports of serum uric acid, neutrophil and lymphocyte ratio, von Willebrand factor to protein C ratio, serum brain natriuretic peptide, cytokine, hyaluronic acid, soluble CD163, serum lipid, lactic acid, coagulation factor Ⅴ, serum phosphorus etc. new serum biomarkers for early detection and recognition the occurrence and development of the EAD after liver transplantation. It was possible to intervene EAD early and effectively after liver transplantation. Conclusions Early recognition and prevention of EAD after liver transplantation is particularly important. Although some new predictive indicators have been proposed to predict occurrence of EAD after liver transplantation, relevant studies are lesser and there are still many problems to be solved. Further studies will be conducted to verify clinical application value of these new indicators.
Objective To analyze the imaging features of solitary pulmonary nodules ( SPNs) , and compare the two types of lung cancer prediction models in distinguishing malignancy of SPNs.Methods A retrospective study was performed on the patients admitted to Ruijin Hospital between 2002 and 2009 with newly discovered SPNs. The patients all received pathological diagnosis. The clinical and imaging characteristics were analyzed. Then the diagnostic accuracy of two lung cancer prediction models for distinguishing malignancy of SPNs was evaluated and compared.Results A total of 90 patients were enrolled, of which 32 cases were with benign SPNs, 58 cases were with malignant SPNs. The SPNs could be identified between benign and maligant by the SPN edge features of lobulation ( P lt;0. 05) . The area under ROC curve of VA model was 0. 712 ( 95% CI 0. 606 to 0. 821) . The area under ROC curve of Mayo Clinic model was 0. 753 ( 95% CI 0. 652 to 0. 843) , which was superior to VA model. Conclusions It is meaningful for the identification of benign and maligant SPNs by the obulation sign in CT scan. We can integrate the clinical features and the lung cancer predicting models to guide clinical work.
ObjectiveTo investigate the effect of CYP2C9 and APOE on the dose of stable warfarin and model prediction in Hainan population.MethodsFrom August 2016 to July 2018, 368 patients who required heart valve replacement and agreed to take warfarin anticoagulation at the second department of cardiothoracic surgery in our hospital were enrolled, including 152 males aged 48.5–70.5 (60.03±10.18) years and 216 females aged 43.5–65.6 (54.24±11.35) years. CYP2C9 and APOE were amplified by polymerase chain reaction. The gene fragment was sequenced by the Single Nucleotide Polymorphisms (SNP) site. The patients' age, sex, weight, history of smoking and drinking, and the dose of stable warfarin were recorded. Regression analysis of these clinical data was made to construct a dose prediction model.ResultsAmong 368 patients, CYP2C9 genotype test results showed 301 patients (81.8%) with *1*1 genotype, and 67 patients (18.2%) with *1*3 type. For different CYP2C9 genotype patients, the difference was statistically significant in the dose of stable warfarin (P<0.05). The results of APOE genotype showed 93 patients (25.3%) with E2 genotype, 221 patients (60.1%) with E3 genotype, and 54 patients (14.7%) with E4 genotype; the dose of stable warfarin in patients with different APOE genotypes was statistically significant (P<0.05). Multiple regression analysis showed that patients' age, body weight, and CYP2C9 and APOE genotypes were correlated with the dose of stable warfarin. The correlation coefficient R2 was 0.572, and the prediction model was statistically significant (P<0.05).ConclusionCYP2C9 and APOE gene polymorphisms exist in Hainan population. There is significant difference in the dose of stable warfarin among different genotypes of patients. The model to predict stable warfarin can partly explain the difference of warfarin among different patients.
ObjectiveTo analyze the influencing factors of acute exacerbation readmission in elderly patients with chronic obstructive pulmonary disease (COPD) within 30 days, construct and validate the risk prediction model.MethodsA total of 1120 elderly patients with COPD in the respiratory department of 13 general hospitals in Ningxia from April 2019 to August 2020 were selected by convenience sampling method and followed up until 30 days after discharge. According to the time of filling in the questionnaire, 784 patients who entered the study first served as the modeling group, and 336 patients who entered the study later served as the validation group to verify the prediction effect of the model.ResultsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors were the influencing factors of patients’ readmission to hospital. The risk prediction model was constructed: Z=–8.225–0.310×assignment of education level+0.564×assignment of smoking status+0.873×assignment of number of acute exacerbations of COPD hospitalizations in the past 1 year+0.779×assignment of regular use of medication+0.617×assignment of rehabilitation and exercise +0.970×assignment of nutritional status+assignment of seasonal factors [1.170×spring (0, 1)+0.793×autumn (0, 1)+1.488×winter (0, 1)]. The area under ROC curve was 0.746, the sensitivity was 75.90%, and the specificity was 64.30%. Hosmer-Lemeshow test showed that P=0.278. Results of model validation showed that the sensitivity, the specificity and the accuracy were 69.44%, 85.71% and 81.56%, respectively.ConclusionsEducation level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors are the influencing factors of patients’ readmission to hospital. The risk prediction model is constructed based on these factor. This model has good prediction effect, can provide reference for the medical staff to take preventive treatment and nursing measures for high-risk patients.
Objective To investigate the factors influencing the occurrence of postoperative pulmonary complications (PPCs) in liver transplant recipients and to construct Nomogram model to identify high-risk patients. Methods The clinical data of 189 recipients who underwent liver transplantation at the General Hospital of Eastern Theater Command from November 1, 2019 to November 1, 2022 were retrospective collected, and divided into PPCs group (n=61) and non-PPCs group (n=128) based on the occurrence of PPCs. Univariate and multivariate logistic regression analyses were used to determine the risk factors for PPCs, and the predictive effect of the Nomogram model was evaluated by receiver operator characteristic curve (ROC) and calibration curve. Results Sixty-one of 189 liver transplant patients developed PPCs, with an incidence of 32.28%. Univariate analysis results showed that PPCs were significantly associated with age, smoking, Child-Pugh score, combined chronic obstructive pulmonary disease (COPD), combined diabetes mellitus, prognostic nutritional index (PNI), time to surgery, amount of bleeding during surgery, and whether or not to diuretic intraoperatively (P<0.05). Multivariate logistic regression analysis showed that age [OR=1.092, 95%CI (1.034, 1.153), P=0.002], Child-Pugh score [OR=1.575, 95%CI (1.215, 2.041), P=0.001], combined COPD [OR=4.578, 95%CI (1.832, 11.442), P=0.001], combined diabetes mellitus [OR=2.548, 95%CI (1.024, 6.342), P=0.044], preoperative platelet count (PLT) [OR=1.076, 95%CI (1.017, 1.138), P=0.011], and operative time [OR=1.061, 95%CI (1.012, 1.113), P=0.014] were independent risk factors for PPCs. The prediction model for PPCs which constructed by using the above six independent risk factors in Nomogram had an area under the ROC curve of 0.806. Hosmer and Lemeshow goodness of fit test (P=0.129), calibration curve, and decision curve analysis showed good agreement with Nomogram model. Conclusion The Nomogram model constructed based on age, Child-Pugh score, combined COPD, combined diabetes mellitus, preoperative PLT, and time of surgery can better identify patients at high risk of developing PPCs after liver transplantation.