Carbon nanotubes (CNTs) have been shown to affect cell behavior. But how and why the CNTs affect potential differentiation of the attached cells has not been largely known. In this study, multiwalled carbon nanotubes (MWNTs) and graphite (GP) were pressed as compacts. Higher ability of CNTs to adsorb proteins, compared with GP, was shown. Myoblastic mouse cells (C2C12) were cultured and the cell responses to the two kinds of compacts were compared in vitro. Meanwhile, we used cell culture on the culture plate as a control. During the conventional culture, significantly better cell attachment, proliferation, and differentiation of cells on the MWNTs were found. To confirm the hypothesis that the larger amount of protein adsorbed on the CNTs was crucial for this, we made the compacts adsorb more proteins in culture medium with 50% fetal bovine serum (FBS) before cell culture. With the adsorption of the proteins in advance, the increments of the total-protein/DNA and alkaline phosphatase (ALP)/DNA for the MWNTs was respectively as about 11 times and 18 times as the increments of those for GP and the control at both day 4 and day 7. Therefore, the CNTs might induce cellular functions by adsorbing more proteins, which indicated that the CNTs might be a candidate for scaffold material for tissue engineering.

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Background This Mendelian randomization study aims to investigate causal associations between genetically predicted insomnia and 14 cardiovascular diseases (CVDs) as well as the potential mediator role of 17 cardiometabolic risk factors. Methods and Results Using genetic association estimates from large genome‐wide association studies and UK Biobank, we performed a 2‐sample Mendelian randomization analysis to estimate the associations of insomnia with 14 CVD conditions in the primary analysis. Then mediation analysis was conducted to explore the potential mediator role of 17 cardiometabolic risk factors using a network Mendelian randomization design. After correcting for multiple testing, genetically predicted insomnia was consistent significantly positively associated with 9 of 14 CVDs, those odds ratios ranged from 1.13 (95% CI, 1.08–1.18) for atrial fibrillation to 1.24 (95% CI, 1.16–1.32) for heart failure. Moreover, genetically predicted insomnia was consistently associated with higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol, each of which may act as a mediator in the causal pathway from insomnia to several CVD outcomes. Additionally, we found very little evidence to support a causal link between insomnia with abdominal aortic aneurysm, thoracic aortic aneurysm, total cholesterol, low‐density lipoprotein cholesterol, glycemic traits, renal function, and heart rate increase during exercise. Finally, we found no evidence of causal associations of genetically predicted body mass index, high‐density lipoprotein cholesterol, or triglycerides on insomnia. Conclusions This study provides evidence that insomnia is associated with 9 of 14 CVD outcomes, some of which may be partially mediated by 1 or more of higher body mass index, triglycerides, and lower high‐density lipoprotein cholesterol.

Genome-wide association study (GWAS) is fundamentally designed to detect disease-causing genes. To reduce spurious associations or improve statistical power, about 80% of GWASs arbitrarily adjusted for demographic and clinical covariates. However, adjustment strategies in GWASs have not achieved consistent conclusions. Given the initial aim of GWAS that is to identify the causal association between a specific causal single-nucleotide polymorphism (SNP) and disease trait, we summarized all complex relationships of the target SNP, covariate and disease trait into 15 causal diagrams according to various roles of the covariate. Following each causal diagram, we conducted a series of theoretical justifications and statistical simulations. Our results demonstrate that it is unadvisable to adjust for any demographic or clinical covariates. We illustrate our point by applying GWASs for body mass index (BMI) and breast cancer, including adjusting and non-adjusting for age and smoking status. Genetic effects and P values might vary across different strategies. Instead, adjustments for SNPs (G') should be strongly recommended when G' are in linkage disequilibrium with the target SNP, and correlated with disease trait conditional on the target SNP. Specifically, adjustment for such G' can block all the confounding paths between the target SNP and disease trait, and avoid over-adjusting for colliders or intermediaries.

A rat model of chronic AAN was successfully reproduced by gavage with CAM extract. Dynamic changes of mitochondrial injury induced by CAM might contribute to the AAN progression.

In many empirical studies, there exist rich individual studies to separately estimate causal effect of the treatment or exposure variable on the outcome variable, but incomplete confounders are adjusted in each study. Suppose we are interested in the causal effect of a treatment or exposure on an outcome variable, and we have available rich datasets that contain different confounders. How to integrate summary‐level statistics from multiple individual datasets to improve causal inference has become a main challenge in data fusion. We propose a novel method in this article to identify the causal effect of a treatment or exposure on the continuous outcome. We show that the causal effect is identifiable and can be estimated by combining summary‐level statistics from multiple datasets containing subsets of confounders and an external dataset only containing complete confounding information. Simulation studies indicate the unbiasedness of causal effect estimate by our method and we apply our method to a study about the effect of body mass index on fasting blood glucose.