Emotional differences based on comments on doctor-patient disputes with varying levels of severity

BACKGROUND The risks associated with negative doctor-patient relationships have seriously hindered the healthy development of medical and healthcare and aroused wide-spread concern in society.The number of public comments on doctor-patient relationship risk events reflects the degree to which the public pays attention to such events.Thirty incidents of doctor-patient disputes were collected from Weibo and TikTok,and 3655 related comments were extracted.The number of comment sentiment words was extracted,and the comment sentiment value was calculated.The Kruskal-Wallis H test was used to compare differences between each variable group at different levels of incidence.Spearman’s correlation analysis was used to examine associations between variables.Regression analysis was used to explore factors influencing scores of comments on incidents.RESULTS The study results showed that public comments on media reports of doctor-patient disputes at all levels are mainly dominated by“good”and“disgust”emotional states.There was a significant difference in the comment scores and the number of partial emotion words between comments on varying levels of severity of doctor-patient disputes.The comment score was positively correlated with the number of emotion words related to positive,good,and happy)and negatively correlated with the number of emotion words related to negative,anger,disgust,fear,and sadness.CONCLUSION The number of emotion words related to negative,anger,disgust,fear,and sadness directly influences comment scores,and the severity of the incident level indirectly influences comment scores.

KCNQ1 rs2237895 gene polymorphism increases susceptibility to type 2 diabetes mellitus in Asian populations

BACKGROUND The association of single nucleotide polymorphism of KCNQ1 gene rs2237895 with type 2 diabetes mellitus(T2DM)is currently controversial.It is unknown whether this association can be gene realized across different populations.AIM To determine the association of KCNQ1 rs2237895 with T2DM and provide reliable evidence for genetic susceptibility to T2DM.METHODS We searched PubMed,Embase,Web of Science,Cochrane Library,Medline,Baidu Academic,China National Knowledge Infrastructure,China Biomedical Literature Database,and Wanfang to investigate the association between KCNQ1 gene rs2237895 and the risk of T2DM up to January 12,2022.Review Manager 5.4 was used to analyze the association of the KCNQ1 gene rs2237895 polymorphism with T2DM and to evaluate the publication bias of the selected literature.RESULTS Twelve case–control studies(including 11273 cases and 11654 controls)met our inclusion criteria.In the full population,allelic model[odds ratio(OR):1.19;95%confidence interval(95%CI):1.09–1.29;P<0.0001],recessive model(OR:1.20;95%CI:1.11–1.29;P<0.0001),dominant model(OR:1.27.95%CI:1.14–1.42;P<0.0001),and codominant model(OR:1.36;95%CI:1.15–1.60;P=0.0003)(OR:1.22;95%CI:1.10–1.36;P=0.0002)indicated that the KCNQ1 gene rs2237895 polymorphism was significantly correlated with susceptibility to T2DM.In stratified analysis,this association was confirmed in Asian populations:allelic model(OR:1.25;95%CI:1.13–1.37;P<0.0001),recessive model(OR:1.29;95%CI:1.11–1.49;P=0.0007),dominant model(OR:1.35;95%CI:1.20–1.52;P<0.0001),codominant model(OR:1.49;95%CI:1.22–1.81;P<0.0001)(OR:1.26;95%CI:1.16–1.36;P<0.0001).In non-Asian populations,this association was not significant:Allelic model(OR:1.06,95%CI:0.98–1.14;P=0.12),recessive model(OR:1.04;95%CI:0.75–1.42;P=0.83),dominant model(OR:1.06;95%CI:0.98–1.15;P=0.15),codominant model(OR:1.08;95%CI:0.82–1.42;P=0.60.OR:1.15;95%CI:0.95–1.39;P=0.14).CONCLUSION KCNQ1 gene rs2237895 was significantly associated with susceptibility to T2DM in an Asian population.Carriers of the C allele had a higher risk of T2DM.This association was not significant in non-Asian populations.

A Review on High-Dimensional Frequentist Model Averaging

Model averaging has attracted increasing attention in recent years for the analysis of high-dimensional data. By weighting several competing statistical models suitably, model averaging attempts to achieve stable and improved prediction. To obtain a better understanding of the available model averaging methods, their properties and the relationships between them, this paper is devoted to make a review on some recent progresses in high-dimensional model averaging from the frequentist perspective. Some future research topics are also discussed.