Background Previous studies focus on one or several serum biomarkers and the risk of cardiovascular disease (CVD). This study aims to investigate the association of multiple serum biomarkers and the risk of CVD and evaluate the dose-relationship between a single serum metabolite and CVD. Methods Our case-control study included 161 CVD and 160 non-CVD patients who had a physical examination in the same hospital. We used stratified analysis and cubic restricted analysis to investigate the dose-response relationship of individual serum biomarkers and the CVD incident. Moreover, to investigate serum biomarkers and CVD, we used elastic net regression and logistic regression to build a multi-biomarker model. Results In a single serum biomarker model, we found serum FT4, T4. GLU, CREA, TG and LDL-c were positively associated with CVD. In the male group, serum T4, GLU and LDL-c were positively associated with CVD; and serum TG was positively associated with CVD in the female group. When patients ≤63 years old, serum T4, GLU, CREA and TG were positively associated with CVD, and serum TG and LDL-c were positively associated with CVD when patients > 63 years old. Moreover, serum GLU had nonlinearity relationship with CVD and serum TG and LDL-c had linearity association with CVD. Furthermore, we used elastic regression selecting 5 serum biomarkers (GLU, FT4, TG, HDL-c, LDL-c) which were independently associated with CVD incident and built multi-biomarker model. And the multi-biomarker model had much better sensitivity than single biomarker model. Conclusion The multi-biomarker model had much higher sensitivity than a single biomarker model for the prediction of CVD. Serum FT4, TG and LDL-c were positively associated with the risk of CVD in single and multiple serum biomarkers models, and serum TG and LDL-c had linearity relationship with CVD.
Background Previous observational studies have provided conflicting results on the association between serum iron status and the risk of breast cancer. Considering the relevance of this relationship to breast cancer prevention, its elucidation is warranted. Object We used a two-sample Mendelian randomisation (MR) study to explore the causal relationship between serum iron status and the risk of breast cancer. Method To select single nucleotide polymorphisms (SNPs) that could be used as instrumental variables for iron status, we used the Genetics of Iron Status consortium, which includes 11 discovery and 8 replication cohorts, encompassing 48,972 individuals of European descent. Moreover, we used the OncoArray network to select SNPs that could be considered instrumental variables for the outcome of interest (breast cancer); this dataset included 122,977 individuals of European descent with breast cancer and 105,974 peers without breast cancer. Both conservative (SNPs associated with overall iron status markers) and liberal (SNPs associated with the levels of at least one iron status marker) approaches were used as part of the MR analysis. For the former, we used an inverse-variance weighted (IVW) method, whereas for the latter, we used the IVW, MR-Egger regression, weighted median and simple mode methods. Results When the conservative approach was used, iron status showed no significant association with the risk of breast cancer or any of its subtypes. However, when the liberal approach was used, transferrin levels were found to be positively associated with the risk of ER-negative breast cancer based on the simple mode method (OR for MR, 1.225; 95% CI, 1.064, 1.410; P = 0.030). Nevertheless, the levels of the other iron status markers showed no association with the risk of breast cancer or its subtypes (P > 0.05). Conclusion In our MR study, the liberal approach suggested that changes in the concentration of transferrin could increase the risk of ER-negative breast cancer, although the levels of other iron status markers had no effect on the risk of breast cancer or its subtypes. This should be verified in future studies.
BackgroundPhenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort.ResultA total of 1999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson’s test, Jaccard’s index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10− 4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10− 8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3 T3-L1 preadipocytes.ConclusionAll these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.
With intensification of urbanization throughout the world, food security is being threatened by the population surge, frequent occurrence of extreme climate events, limited area of available cultivated land, insufficient utilization of urban space, and other factors. Determining the means by which high-yielding and high-quality crops can be produced in a limited space is an urgent priority for plant scientists. Dense planting, vertical production, and indoor cultivation are effective ways to make full use of space and improve the crop yield. The results of physiological and molecular analyses of the model plant species Arabidopsis thaliana have shown that the plant response to shade is the key to regulating the plant response to changes in light intensity and quality by integrating light and auxin signals. In this study, we have summarized the major molecular mechanisms of shade avoidance and shade tolerance in plants. In addition, the biotechnological strategies of enhancing plant shade tolerance are discussed. More importantly, cultivating crop varieties with strong shade tolerance could provide effective strategies for dense planting, vertical production, and indoor cultivation in urban agriculture in the future.
Background Trachypithecus leucocephalus, the white-headed langur, is a critically endangered primate that is endemic to the karst mountains in the southern Guangxi province of China. Studying the genomic and transcriptomic mechanisms underlying its local adaptation could help explain its persistence within a highly specialized ecological niche. Results In this study, we used PacBio sequencing and optical assembly and Hi-C analysis to create a high-quality de novo assembly of the T. leucocephalus genome. Annotation and functional enrichment revealed many genes involved in metabolism, transport, and homeostasis, and almost all of the positively selected genes were related to mineral ion binding. The transcriptomes of 12 tissues from three T. leucocephalus individuals showed that the great majority of genes involved in mineral absorption and calcium signaling were expressed, and their gene families were significantly expanded. For example, FTH1 primarily functions in iron storage and had 20 expanded copies. Conclusions These results increase our understanding of the evolution of alkali tolerance and other traits necessary for the persistence of T. leucocephalus within an ecologically unique limestone karst environment.
We propose a machine-learning-based method for grating waveguides and augmented reality, significantly reducing the computation time compared with existing finite-element-based numerical simulation methods. Among the slanted, coated, interlayer, twin-pillar, U-shaped, and hybrid structure gratings, we exploit structural parameters such as grating slanted angle, grating depth, duty cycle, coating ratio, and interlayer thickness to construct the gratings. The multi-layer perceptron algorithm based on the Keras framework was used with a dataset comprised of 3000–14,000 samples. The training accuracy reached a coefficient of determination of more than 99.9% and an average absolute percentage error of 0.5%–2%. At the same time, the hybrid structure grating we built achieved a diffraction efficiency of 94.21% and a uniformity of 93.99%. This hybrid structure grating also achieved the best results in tolerance analysis. The high-efficiency artificial intelligence waveguide method proposed in this paper realizes the optimal design of a high-efficiency grating waveguide structure. It can provide theoretical guidance and technical reference for optical design based on artificial intelligence.
Background: Phenomics provides new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the systematic mining of shared genetics among clinical biochemical indices based on phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. Result: A total of 1,999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms (SNPs) were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson’s test, Jaccard’s index, and linkage disequilibrium score regression, were used. The results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, and shared genetics analysis showed that 29 SNPs (P < 10-4) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalogue, 31 SNPs were found to be associated with several diseases (P < 10-8). Using ALDH2 as an example to preliminarily explore its biological function, we also confirmed that the rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3T3-L1 preadipocytes. Conclusion: All these findings indicated a network of shared genetics and 29 biochemical indices, which will help fully understand the genetics participating in biochemical metabolism.
Background: Phenomics provides a new technologies and platforms as a systematic phenome-genome approach. However, few studies have reported on the system mining of shared genetics among clinical biochemical indices based on Phenomics methods, especially in China. This study aimed to apply phenomics to systematically explore shared genetics among 29 biochemical indices based on the Fangchenggang Area Male Health and Examination Survey cohort. Result: A total of 1,999 subjects with 29 biochemical indices and 709,211 single nucleotide polymorphisms were subjected to phenomics analysis. Three bioinformatics methods, namely, Pearson test, Jaccard index, and linkage disequilibrium score regression , were used. Results showed that 29 biochemical indices were from a network. IgA, IgG, IgE, IgM, HCY, AFP and B12 were in the central community of 29 biochemical indices. Key genes and loci associated with metabolism traits were further identified, shared-genetics analysis showed that 29 SNPs (P < 10 -4 ) were associated with three or more traits. After integrating the SNPs related to two or more traits with the GWAS catalog, 31 SNPs were found to be associated with several diseases (P < 10 -8 ). Taking ALDH2 as an example to preliminarily explore its biological function, we also confirmed that rs671 (ALDH2) polymorphism affected multiple traits of osteogenesis and adipogenesis differentiation in 3T3-L1 preadipocytes. Conclusion: All these findings indicated a network of shared genetics and 29 biochemical indices, which will helpfully understand the genetics participated in biochemical metabolism.
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