To explore factors influencing adolescents and young adults’ (AYAs) risk perception of COVID-19 and adherence to public health measures, we conducted a cross-sectional online survey of AYAs (14–22 years old) from Quebec (Canada) recruited through school and community partners in April 2020 during the first wave of the COVID-19 pandemic. The study included 3037 participants (mean age = 17.7 years, 74.6% female). AYAs had higher mean (standard deviation (SD)) risk perception of COVID-19 for their relatives (8.2 (1.9)) than for themselves (5.6 (2.6)) (p < 0.001). Factors associated with higher risk perception included higher disease knowledge (adjusted odds ratio (aOR) 1.06, 95% CI 1.01–1.11), presence of chronic disease (aOR 2.31, 95%CI 1.82–2.93) and use of immunosuppressants (aOR 2.53, 95%CI 1.67–3.87). AYAs with a higher risk perception (aOR 1.06, 95%CI 1.02–1.10) those wishing to help flatten the disease curve (aOR 1.18, 95%CI 1.12–1.25) or to protect their family/friends (aOR 1.14, 95%CI 1.05–1.24) were more likely to engage in preventive behaviors. Self-perceived risk and desire to protect others were significantly associated with adherence to preventive measures among youth. These findings may help inform public health messaging to AYAs in the current and future pandemics.

BMD identified on images from dual-energy X-ray absorptiometry were strongly related to multidetector computed tomography measures of CAC. This low-cost, minimal radiation technique used widely for OP screening is a promising marker of generalized coronary atherosclerosis.

BackgroundTo support a hypothesis that there is an intrinsic interplay between coronary artery disease (CAD) and type 2 diabetes (T2D), we used RNA-seq to identify unique gene expression signatures of CAD, T2D, and coexisting conditions.MethodsAfter transcriptome sequencing, differential expression analysis was performed between each disordered state and normal control group. By comparing gene expression profiles of CAD, T2D, and coexisting conditions, common and specific patterns of each disordered state were displayed. To verify the specific gene expression patterns of CAD or T2D, the gene expression data of GSE23561 was extracted.ResultsA strong overlap of 191 genes across CAD, T2D and coexisting conditions, were mainly involved in a viral infectious cycle, anti-apoptosis, endocrine pancreas development, innate immune response, and blood coagulation. In T2D-specific PPI networks involving 64 genes, TCF7L2 (Degree = 169) was identified as a key gene in T2D development, while in CAD-specific PPI networks involving 64 genes, HIF1A (Degree = 124), SMAD1 (Degree = 112) and SKIL (Degree = 94) were identified as key genes in the CAD development. Interestingly, with the provided expression data from GSE23561, the three genes were all up-regulated in CAD, and SMAD1 and SKIL were specifically differentially expressed in CAD, while HIF1A was differentially expressed in both CAD and T2D, but with opposite trends.ConclusionsThis study provides some evidences in transcript level to uncover the association of T2D, CAD and coexisting conditions, and may provide novel drug targets and biomarkers for these diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s13000-017-0630-7) contains supplementary material, which is available to authorized users.

Context Although metabolic profiles appear to play an important role in menopausal bone loss, the functional mechanisms by which metabolites influence bone mineral density (BMD) during menopause are largely unknown. Objective We aimed to systematically identify metabolites associated with BMD variation and their potential functional mechanisms in peri-/post-menopausal women. Design and Methods We performed serum metabolomic profiling and whole-genome sequencing for 517 perimenopausal (16%) and early postmenopausal (84%) women aged 41 to 64 years in this cross-sectional study. Partial least squares (PLS) regression and general linear regression analysis were applied to identify BMD-associated metabolites, and weighted gene co-expression network analysis was performed to construct co-functional metabolite modules. Furthermore, we performed Mendelian randomization analysis to identify causal relationships between BMD-associated metabolites and BMD variation. Finally, we explored the effects of a novel prominent BMD-associated metabolite on bone metabolism through both in vivo/in vitro experiments. Results Twenty metabolites and a co-functional metabolite module (consisting of fatty acids) were significantly associated with BMD variation. We found dodecanoic acid (DA), within the identified module, causally decreased total hip BMD. Subsequently, the in vivo experiments might support that dietary supplementation with DA could promote bone loss, as well as increase the osteoblast and osteoclast numbers in normal/ovariectomized mice. DA treatment differentially promoted osteoblast and osteoclast differentiation, especially for osteoclast differentiation at higher concentrations in vitro (e.g.,10, 100μM). Conclusions This study sheds light on metabolomic profiles associated with postmenopausal osteoporosis risk, highlighting the potential importance of fatty acids, as exemplified by DA, in regulating BMD.

While the gut microbiome has been reported to play a role in bone metabolism, the individual species and underlying functional mechanisms have not yet been characterized. We conducted a systematic multi-omics analysis using paired metagenomic and untargeted serum metabolomic profiles from a large sample of 499 peri- and early post-menopausal women to identify the potential crosstalk between these biological factors which may be involved in the regulation of bone mineral density (BMD). Single omics association analyses identified 22 bacteria species and 17 serum metabolites for putative association with BMD. Among the identified bacteria, Bacteroidetes and Fusobacteria were negatively associated, while Firmicutes were positively associated. Several of the identified serum metabolites including 3-phenylpropanoic acid, mainly derived from dietary polyphenols, and glycolithocholic acid, a secondary bile acid, are metabolic byproducts of the microbiota. We further conducted a supervised integrative feature selection with respect to BMD and constructed the inter-omics partial correlation network. Although still requiring replication and validation in future studies, the findings from this exploratory analysis provide novel insights into the interrelationships between the gut microbiome and serum metabolome that may potentially play a role in skeletal remodeling processes.

Although gut microbiota influences osteoporosis risk, the individual species involved, and underlying mechanisms, are unknown. We performed integrative analyses in a Chinese cohort with metagenomics/targeted metabolomics/whole-genome sequencing. Bacteroides vulgatus was found negatively associated with bone mineral density (BMD), this association was validated in US Caucasians. Serum valeric acid was positively associated with BMD, and B.vulgatus causally downregulated it. Ovariectomized mice fed B.vulgatus had decreased bone formation and increased bone resorption, lower BMD and poorer bone micro-structure. Valeric acid suppressed NF-κB p65 protein production (pro-inflammatory), and enhanced IL-10 mRNA expression (anti-inflammatory), leading to suppressed maturation of osteoclast-like cells, and enhanced maturation of osteoblasts in vitro. B.vulgatus and valeric acid represent promising targets for osteoporosis prevention/treatment.

Background Color is the major ornamental feature of the Rhododendron genus, and it is related to the contents of flavonoid in petals. However, the regulatory mechanism of flavonoid biosynthesis in Rhododendron pulchrum remains unknown. The transcriptome and metabolome analysis of Rhododendron pulchrum with white, pink and purple color in this study aimed to reveal the mechanism of flavonoid biosynthesis and to provide insight for improving the petal color. Results Flavonoids and flavonols are the major components of flavonoid metabolites in R.pulchrum, such as laricitrin, apigenin, tricin, luteolin, isoorientin, isoscutellarein, diosmetin and their glycosides derivatives. With transcriptome and metabolome analysis, we found CHS, FLS, F3’H, F3′5’H, DFR, ANS, GT, FNS, IFR and FAOMT genes showed significantly differential expression in cultivar ‘Zihe'. FNS and IFR were discovered to be associated with coloration in R.pulchrum for the first time. The FNS gene existed in the form of FNSI. The IFR gene and its related metabolites of medicarpin derivatives were highly expressed in purple petal. In cultivar ‘Fenhe', up-regulation of F3’H and F3′5’H and down-regulation of 4CL, DFR, ANS, and GT were associated with pink coloration. With the transcription factor analysis, a subfamily of DREBs was found to be specifically enriched in pink petals. This suggested that the DREB family play an important role in pink coloration. In cultivars ‘Baihe', flavonoid biosynthesis was inhibited by low expression of CHS, while pigment accumulation was inhibited by low expression of F3′5'H, DFR, and GT, which led to a white coloration. Conclusions By analyzing the transcriptome and metabolome of R.pulchrum, principal differential expression genes and metabolites of flavonoid biosynthesis pathway were identified. Many novel metabolites, genes, and transcription factors associated with coloration have been discovered. To reveal the mechanism of the coloration of different petals, a model of the flavonoid biosynthesis pathway of R.pulchrum was constructed. These results provide in depth information regarding the coloration of the petals and the flavonoid metabolism of R.pulcherum. The study of transcriptome and metabolome profiling gains insight for further genetic improvement in Rhododendron.

Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last.