Background: This study was to systematically test whether previously reported risk factors for chronic kidney disease (CKD) are causally related to CKD in European and East Asian ancestries using Mendelian randomization. Methods: A total of 45 risk factors with genetic data in European ancestry and 17 risk factors in East Asian participants were identified as exposures from PubMed. We defined the CKD by clinical diagnosis or by estimated glomerular filtration rate of <60 ml/min/1.73 m 2 . Ultimately, 51 672 CKD cases and 958 102 controls of European ancestry from CKDGen, UK Biobank and HUNT, and 13 093 CKD cases and 238 118 controls of East Asian ancestry from Biobank Japan, China Kadoorie Biobank and Japan-Kidney-Biobank/ToMMo were included. Results: Eight risk factors showed reliable evidence of causal effects on CKD in Europeans, including genetically predicted body mass index (BMI), hypertension, systolic blood pressure, high-density lipoprotein cholesterol, apolipoprotein A-I, lipoprotein(a), type 2 diabetes (T2D) and nephrolithiasis. In East Asians, BMI, T2D and nephrolithiasis showed evidence of causality on CKD. In two independent replication analyses, we observed that increased hypertension risk showed reliable evidence of a causal effect on increasing CKD risk in Europeans but in contrast showed a null effect in East Asians. Although liability to T2D showed consistent effects on CKD, the effects of glycaemic phenotypes on CKD were weak. Non-linear Mendelian randomization indicated a threshold relationship between genetically predicted BMI and CKD, with increased risk at BMI of >25 kg/m 2 . Conclusions: Eight cardiometabolic risk factors showed causal effects on CKD in Europeans and three of them showed causality in East Asians, providing insights into the design of future interventions to reduce the burden of CKD.
Background The aim of this study was to develop and validate systematic nomograms to predict cancer specific survival (CSS) and overall survival (OS) in osteosarcoma patients aged over 60 years. Methods We used data from the Surveillance, Epidemiology, and End Results (SEER) database and identified 982 patients with osteosarcoma over 60 years of age diagnosed between 2004 and 2015. Overall, 306 patients met the requirements for the training group. Next, we enrolled 56 patients who met the study requirements from multiple medical centers as the external validation group to validate and analyze our model. We collected all available variables and finally selected eight that were statistically associated with CSS and OS through Cox regression analysis. Integrating the identified variables, we constructed 3‐ and 5‐year OS and CSS nomograms, respectively, which were further evaluated by calculating the C‐index. A calibration curve was used to evaluate the accuracy of the model. Receiver operating characteristic (ROC) curves measured the predictive capacity of the nomograms. The Kaplan–Meier analysis was used for all patient‐based variables to explore the influence of various factors on patient survival. Finally, a decision curve analysis (DCA) curve was used to analyze whether our model would be suitable for application in clinical practice. Results Cox regression analysis of clinical variables identified age, sex, marital status, tumor grade, tumor laterality, tumor size, M‐stage, and surgical treatment as prognostic factors. Nomograms showed good predictive capacity for OS and CSS. We calculated that the C‐index of the OS nomogram of the training population was 0.827 (95% CI 0.778–0.876), while that of the CSS nomogram was 0.722 (95% CI 0.665–0.779). The C‐index of the OS nomogram evaluated on the external validation population was 0.716 (95% CI 0.575–0.857), while that of the CSS nomogram was 0.642 (95% CI 0.50–0.788). Furthermore, the calibration curve of our prediction models indicated the nomograms could accurately predict patient outcome. Conclusions The constructed nomogram is a useful tool for accurately predicting OS and CSS at 3 and 5 years for patients over 60 years of age with osteosarcoma and can assist clinicians in making appropriate decisions in practice.
结构, 在低温下, FeSe材料呈正交晶格, 体态的T c 为 9 K. 对于在SiC上生长的8层FeSe膜, 扫描隧道谱(STS) 表明8 K时超导能隙已闭合(图1(a)), 与体态FeSe的T c 相近. 对于更薄的2原胞层FeSe膜, 在3.7 K时(图1(b)), 超导能隙就已经消失. 2-8层FeSe的实验表明, T c 随着 膜的变薄而下降(图1(c)), 此即我们之前提到过的, 随 着维度的降低, 涨落的加剧会倾向于破坏超导效 应. SiC上制备的FeSe薄膜的T c 随厚度的变化关系可 以用公式T c (d)=T c0 (1−d c /d) [28] 来很好地拟合(图1(c)). 外 推至厚度d趋于无穷时(对应体材料的情况), 得到 了T c ≈9.3 K, 这和测量得到的β型FeSe的体态T c 几乎 一致. SiC表面生长的FeSe薄膜与体态FeSe的相似性 源于FeSe和SiC表面间是靠较弱的范德瓦耳斯力连接 (界面为双层石墨烯), 两者间的耦合较弱, 从而SiC表 面生长的FeSe的晶格常数和超导特性接近体态的 FeSe.
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