Circulating endostatin has been linked to increased mortality, cardiovascular comorbidities, and renal impairment. However, its role as a cardiovascular risk marker in the general population remains largely unexplored. This study investigates the association between plasma endostatin and atherosclerosis, inflammation, and kidney function in a cohort of 5,026 randomly selected middle-aged individuals from the Swedish CArdioPulmonary bioImage Study (SCAPIS). Plasma levels of endostatin, C-reactive protein (CRP), HbA1c, lipids, and creatinine were analyzed, and their associations with atherosclerosis and related markers were assessed. Coronary artery atherosclerosis was evaluated using coronary computed tomography. Blood pressure, body mass index, and waist circumference were measured, and medication use for diabetes, hyperlipidemia, and hypertension was recorded. Smoking habits were also documented. The following main results were significantly associated with endostatin. Severe coronary atherosclerosis was positively associated in men. Being on hypertensive medication or not, as reported by the participants at the interview at study inclusion, was significantly associated with endostatin. Hypertensive medication increased from 12% to 26% from the lowest to the highest quartile of endostatin. Waist circumference was positively associated, where endostatin increases, on average, 0.21±SD for a 1±SD increase of waist circumference. Kidney function, measured as eGFR, was negatively associated, where endostatin decreases, on average, 0.22±SD for a 1±SD increase in eGFR. Elevated endostatin levels were associated with advanced coronary atherosclerosis in men, antihypertensive treatment, systemic inflammation (increased CRP), increased waist circumference, and impaired kidney function (lower eGFR).

Four-dimensional flow (4D Flow) CMR affords comprehensive 3D maps of advanced hemodynamics parameters such as wall shear stress (WSS). However, the evaluation of these data is often restricted to spatial averages in large regions of interests, such as the ascending aorta. Recent studies have explored ways of analyzing local intercohort WSS differences by using basic statistical tests with a p-value of 0.05 for determining significance, thus not accounting for the large number of comparisons made when exploring differences for multiple locations across the ascending aorta surface.

Permutation tests, frequently used in brain MRI, permit statistical analysis on a local level while controlling for the family-wise error rate by constructing the null hypothesis distribution based on the maximum statistic over the voxels at each permutation. We sought to use permutation tests to identify local regions of abnormal WSS in the ascending aorta in patients with aortic dilation.

Objective: In non-alcoholic fatty liver disease (NAFLD), liver fibrosis is the strongest predictor of adverse outcomes. We sought to investigate the relationship between liver fibrosis and cardiac remodeling in participants from the general population using magnetic resonance imaging (MRI), as well as explore potential mechanistic pathways by analyzing circulating cardiovascular biomarkers.

Methods: In this cross-sectional study, we prospectively included participants with type 2 diabetes and individually matched controls from the SCAPIS (Swedish CArdioPulmonary bioImage Study) cohort in Linköping, Sweden. Between November 2017 and July 2018, participants underwent MRI at 1.5 Tesla for quantification of liver proton density fat fraction (spectroscopy), liver fibrosis (stiffness from elastography), left ventricular (LV) structure and function, as well as myocardial native T1 mapping. We analyzed 278 circulating cardiovascular biomarkers using a Bayesian statistica lapproach.

Results: In total, 92 participants were enrolled (mean age 59.5 ± 4.6 years, 32 women). The mean liver stiffness was 2.1 ± 0.4 kPa. 53 participants displayed hepatic steatosis. LV concentricity increased across quartiles of liver stiffness. Neither liver fat nor liver stiffness displayed any relationships to myocardial tissue characteristics (native T1). In a regression analysis, liver stiffness was related to increased LV concentricity. This association was independent of diabetes and liver fat (Beta = 0.26, p = 0.0053), but was attenuated (Beta = 0.17, p = 0.077) when also adjusting for circulating levels of interleukin-1 receptor type 2.

Conclusion: MRI reveals that liver fibrosis is associated to structural LV remodeling, in terms of increased concentricity, in participants from the general population. This relationship could involve the interleukin-1 signaling.

Heavy metals are known to be able to cross the placental and blood brain barriers to affect critical neurodevelopmental processes in the fetus. We measured metal levels (Al, Cd, Hg, Li, Pb and Zn) in the cord blood of newborns and in the serum of the same children at 5 years of age, and compared between individuals with or without (controls) autism spectrum disorder (ASD) diagnosis. The samples were from a biobank associated with the All Babies in Southeast Sweden (ABIS) registry. We proposed a Bayesian multivariate log-normal model for partially censored values to identify potentially relevant metals for the etiology of ASD. Our results in cord blood suggest prenatal Al levels could be indicative of later ASD incidence, which could also be related to an increased possibility of a high, potentially toxic, exposure to Al and Li during pregnancy. In addition, a larger possibility of a high, potentially beneficial, exposure to Zn could occur during pregnancy in controls. Finally, we found decisive evidence for an average increase of Hg in 5-year-old ASD children compared to only weak evidence for controls. This is concordant with previous research showing an impaired ability for eliminating Hg in the ASD group.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N?=?2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC?=?0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

Private and common values (CVs) are the two main competing valuation models in auction theory and empirical work. In the framework of second-price auctions, we compare the empirical performance of the independent private value (IPV) model to the CV model on a number of different dimensions, both on real data from eBay coin auctions and on simulated data. Both models fit the eBay data well with a slight edge for the CV model. However, the differences between the fit of the models seem to depend to some extent on the complexity of the models. According to log predictive score the IPV model predicts auction prices slightly better in most auctions, while the more robust CV model is much better at predicting auction prices in more unusual auctions. In terms of posterior odds, the CV model is clearly more supported by the eBay data.