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Objective To construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.Methods A case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022.With the positive/negative ratio of 1 ∶3,928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases.These 1243 patients were randomly divided into the modeling group(n=869)and the validation group(n=374)in a ratio of 7∶3 using the R language setting code.Preoperative clinical indicators(basic information,medical history,relevant scale score,surgical information and results of laboratory tests)and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms,k-nearest neighbor(KNN),support vector machine linear(SVM),logic regression(LR)and extreme gradient boosting(XGBoost).In the validation group,receiver operating characteristic(ROC)curve,precision-recall curve(P-R),decision curve analysis(DCA)curve,Kappa value,sensitivity,specificity,Brier score,and F1 score were applied to evaluate the efficacy of model.Results The model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm.The area under the receiver operating characteristic curve(AUROC)was 0.916(95%CI:0.883～0.949),area under the precision-recall curve(AUPRC)was 0.841,Brier score was 0.097,and sensitivity and specificity was 78.95%and 87.10%,respectively.Conclusion The postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.
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Objective:To investigate the relationship between bedtime procrastination and daytime sleepiness in college students,and to explore the mediating effect of sleep quality and the moderating effect of gender in the a-bove-mentioned relationship.Methods:A total of 2 823 college students(808 males,2 015 females)from two uni-versities were selected.They were assessed with the Bedtime Procrastination Scale(BPS),Epworth Sleeping Scale(ESS,ESS score≥11 indicated daytime sleepiness)and Pittsburgh Sleep Quality Index(PSQI).Process model 4 was used to test the mediating effect of sleep quality and model 14 was used to test the moderating effect of gender.Results:There were 1 214(43.0％)college students suffering from daytime sleepiness.The scores of ESS in college students were positively associated with the BPS scores(β=0.16).The total scores of PSQI partially mediated the effect of BPS scores on college students'ESS scores,and the value of mediating effect was 39.9％.The association between PSQI scores and ESS scores was moderated by gender(β=-0.13).Conclusion:Daytime sleepiness is associated with bedtime procrastination and sleep quality in college students,and the association be-tween sleep quality and daytime sleepiness is moderated by gender.
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Coronavirus disease 2019 (COVID-19) continuously proceeds despite the application of a variety of vaccines. It is still urgent to find effective ways to treat COVID-19. Recent studies indicate that NRP1, an important receptor of the natural peptide tuftsin, facilitates SARS-CoV-2 infection. Importantly, tuftsin is a natural human molecule released from IgG. Here, we found 91 overlapping genes between tuftsin targets and COVID-19-associated genes. Bioinformatics analyses indicated that tuftsin could also target ACE2 and exert some immune-related functions to treat COVID-19. Using surface plasmon resonance (SPR) analysis, we confirmed that tuftsin can bind ACE2 and NRP1 directly. Moreover, tuftsin effectively impairs the binding of SARS-CoV-2 S1 to ACE2. Thus, tuftsin is an attractive drug against COVID-19. And tuftsin as natural immunostimulating peptide in human, we speculate that tuftsin may has crucial roles in asymptomatic carriers or mild cases of COVID-19.
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Objective:To promote the development of private pension institutions and social welfare in China. Methods:33 private pension institutions and 165 elderly people are followed with questionnaire. Results:The number of institutions, beds and occupancy in Hefei has increased annually since 2001. 94% of pension institutions are in the mid-range level of 500~1000 yuan;27. 27% of pension institutions belong to self-constructed housing, 69. 7%rented housing, 3. 03% modificated housing. 21. 21% of pension institutions’ investments scale is more than 10 million yuan. Conclusion:Private pension institutions face some problems, such as structural imbalance in terms of supply and demand, shortage of funds, and lack of human resources. Suggestion:We should reasonably adjust the level and function of institutions, increase the support for funds, and cultivate professional teams.
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Along with the degree of population ageing worse off continuously, the demand for medical service is growing. The current system of pension service and medical service are discrete and separate, and integrating pen-sion service with medical service is the best choice for China. However, there are some problems with the model for integrating pension service with medical service. These problems include the lack of funding, negative participation and imperfect methods of service delivery; high toll levels and single, rigid service contents; and bull management and policies hard to enforce. China should improve the service delivery model, identify service content according to the demand for service object, and improve the mechanism of government management.