Objective Shuanghuanglian injection (SHLI) is a typical Chinese medicine injection which has been used for more than 20 years. This study aims to investigate the compatibility of SHLI with western intravenous agents. Methods An extensive literature search was executed to identify all experimental tests and clinical reports on the topics. Data on the types of infusion solutions and western medical injections, usage and dosage, outcome measures for physical and chemical properties, and changes of drug effect were searched for. The included data was analyzed and described by the category of included western drugs. Compatibility was tested through integrating the evaluations of physical properties, chemical composition, drug metabolism, and safety. Results Sixty articles (38 experimental tests and 22 clinical reports) were included after screening. Fifty–three western medical injections were identified from all the included articles. Most of the research was about the compatibility of SHLI with different types of antibiotics, including β-lactam, aminoglycoside, and quinolone etc. Thirty-one western medical injections were not recommended to be combined with SHLI. The quality of the experimental test design was low because of unbalanced evaluation indicators: more attention to physical changes than drug metabolism, effect, and safety. Conclusions A broad incompatibility exists in the combination of SHLI with western medical injections. Some results are still uncertain, but the combinations should still be avoided until researched clearly. The compatibility and interaction of Chinese herbal injections and western medicines is still a weak area. The pharmaceutical sector should strengthen post-market research to update evidence and improve its distribution.
Globally, the incidence of diabetes has grown rapidly. The prevalence of diabetes in China was 11.6% in 2010. Diabetes has become a huge challenge for public health. The cause of diabetes is not yet completely clear. Potential risk factors include genetic factors, environmental factors, and interactions between the two. Genome-wide association studies have found a series of genetic variants that are closely related to diabetes. Several environmental factors, such as excessive energy intake and lack of physical activity, have been associated with increased risk of diabetes. In the meanwhile, genetic and environmental factors could modify each other’s effect on diabetes risk. With the advent of molecular techniques, post-genomics research, gut microbiota, and trans-omics have provided novel perspectives for the study of diabetes risk factors.
ObjectiveTo explore the association of elongase of very long chain fatty acids family member 6 (ELOVL6) gene with increased risk of large-artery atherosclerosis stroke (LAA) in Han Chinese population in Chengdu.MethodsHan Chinese populations in Chengdu, Sichuan were chosen for this study using the case-control design between January 2015 and December 2017. The genotypes and haplotypes of six single nucleotides polymorphisms (SNPs) of ELOVL6 gene (rs3813825, rs17041272, rs4141123, rs9997926, rs6824447, and rs12504538) were analyzed in different genetic models in entire samples, and gene-enviromental interaction analyses were also carried out to get an insight of the risk factors for LAA. At the same time, we also analyzed the gene expression profile in peripheral blood mononuclear cells between groups.ResultsA total of 240 LAA cases and 211 healthy controls were enrolled in this study. All the enrolled subjects presented CC genotype of rs9997926, while the other five SNPs (rs3813825, rs17041272, rs4141123, rs6824447, and rs12504538) were genotyped successfully in all the enrolled subjects. rs17041272 polymorphism and TGTTG haplotype were significantly associated with LAA risk in studied population [CC/(CG+GG): odds ratio (OR)=0.640, 95% confidence interval (CI) (0.423, 0.968), P=0.034; TGTTG: OR=1.776, 95%CI (1.069, 2.951), P=0.024], and the interaction among rs17041272, rs6824447 SNPs and dyslipidemia increased susceptibility to LAA [OR=2.737, 95%CI (1.715, 4.368), P<0.001]. The ELOVL6 gene expression level was higher in LAA subjects (t=−3.167, P=0.003).ConclusionsELOVL6 gene is associated with LAA risk in Han nationality of Chinese population in Chengdu, and the interaction of gene-environmental risk factors could be of great importance in pathophysiology of LAA.
The interaction mechanism between mental disorders and diabetes is complex, involving genetics, endocrine metabolism, inflammation, oxidative stress and other aspects, which makes it difficult to treat patients with mental disorders complicated by diabetes. Such patients mostly use drugs and non-drug interventions to relieve symptoms of mental disorders and improve blood sugar levels, but the mechanism of mental disorders and diabetes needs to be systematically summarized and needs practical means to intervene. This article starts with the pathogenesis of diabetes and then describes the interaction mechanism of schizophrenia, bipolar disorder, depression and diabetes in detail. Finally, the intervention measures for patients with mental disorders complicated by diabetes are summarized, which aims to provide a reference for medical staff engaged in related work.
ObjectiveTo investigate the factors that influence Chinese residents self-rated health and the effects of the multilevel health insurance system and neighborhood social capital on self-rated health. MethodsBased on the 2018 China labor-force dynamics survey data, and Stata 15.0 software was used to conduct χ2 test, univariate analysis and multiple logistic regression model to analyze the influencing factors of self-rated health of Chinese residents. An interaction model was used to analyze the interactive effects of the multilevel health insurance system and the social capital of the neighborhood on self-rated health. ResultsA total of 10 201 people were investigated in this study, and 39.20% of them were self-rated unhealthy. After adjusting for confounders, the results of the multivariate logistic regression model showed that having social health insurance (OR=0.8, 95%CI 0.7 to 1.0) and having neighborhood social capital (OR=0.7, 95%CI 0.6 to 0.8) were more inclined to self-rated health. In addition, the results showed that being male, having a college degree or higher, having a job, and drinking alcohol increased the risk of self-rated unhealthy (P<0.05); whereas being 45-59 years of age, 60 years of age or older, in the central and western regions, exercising regularly, and having a disease or injury within two weeks decreased the risk of self-rated unhealthy (P<0.05). There was a positive multiplicative interaction between health insurance and neighborhood social capital on residents' self-rated health (univariate: OR=1.5, 95%CI 1.1 to 3.7, P<0.05; multivariate: OR=1.7, 95%CI 1.2 to 2.4, P<0.05), and negative additive interactions (RERI=−0.8, 95%CI −1.4 to −0.1; AP=−0.3, 95%CI −0.6 to −0.1; SI=0.6, 95%CI 0.5 to 0.8). ConclusionAttention should be paid to the self-rated health status of key populations through such means as health promotion and education, and healthy behavior lifestyles should be promoted. The health insurance system should be further improved, and attention should be paid to the role of social capital in the neighborhood, encouraging residents to actively build a good social neighborhood, and realizing the coordinated development of the multilevel health insurance system and the social capital in the neighborhood.
Subpopulation treatment effect pattern plot (STEPP) method is a method for examining the relationship between treatment effects and continuous covariates and is characterized by dividing the study population into multiple overlapping subpopulations to be analyzed based on continuous covariate values. STEPP method has a different purpose than traditional subgroup analyses, and STEPP has a clear advantage in exploring the relationship between treatment effects and continuous covariates. In this study, the concepts, advantages, and subpopulation delineation methods of the STEPP method are introduced, and the specific operation process and result interpretation methods of STEPP method analysis using the STEPP package in R language are presented with examples.