Early diagnosis and noninvasive detection of liver fibrosis and its heterogeneity remain as major unmet medical needs for stopping further disease progression toward severe clinical consequences. Here we report a collagen type I targeting protein-based contrast agent (ProCA32.collagen1) with strong collagen I affinity. ProCA32.collagen1 possesses high relaxivities per particle (r1 and r2) at both 1.4 and 7.0 T, which enables the robust detection of early-stage (Ishak stage 3 of 6) liver fibrosis and nonalcoholic steatohepatitis (Ishak stage 1 of 6 or 1 A Mild) in animal models via dual contrast modes. ProCA32.collagen1 also demonstrates vasculature changes associated with intrahepatic angiogenesis and portal hypertension during late-stage fibrosis, and heterogeneity via serial molecular imaging. ProCA32.collagen1 mitigates metal toxicity due to lower dosage and strong resistance to transmetallation and unprecedented metal selectivity for Gd3+ over physiological metal ions with strong translational potential in facilitating effective treatment to halt further chronic liver disease progression.
The chromosome copy number variation (CNV) is the deviation of genomic regions from their normal copy number states, which may associate with many human diseases. Current genetic studies usually collect hundreds to thousands of samples to study the association between CNV and diseases. CNVs can be called by detecting the change-points in mean for sequences of array-based intensity measurements. Although multiple samples are of interest, the majority of the available CNV calling methods are single sample based. Only a few multiple sample methods have been proposed using scan statistics that are computationally intensive and designed toward either common or rare change-points detection. In this paper, we propose a novel multiple sample method by adaptively combining the scan statistic of the screening and ranking algorithm (SaRa), which is computationally efficient and is able to detect both common and rare change-points. We prove that asymptotically this method can find the true change-points with almost certainty and show in theory that multiple sample methods are superior to single sample methods when shared change-points are of interest. Additionally, we report extensive simulation studies to examine the performance of our proposed method. Finally, using our proposed method as well as two competing approaches, we attempt to detect CNVs in the data from the Primary Open-Angle Glaucoma Genes and Environment study, and conclude that our method is faster and requires less information while our ability to detect the CNVs is comparable or better.
Determination of the optimal interventricular (VV) delay in cardiac resynchronization therapy currently relies on costly, time-consuming echocardiographic (ECHO) methods. This study evaluated the performance of a new intracardiac electrogram (IEGM)-based VV method compared to the aortic velocity time integral (AVTI) method of VV delay optimization. The study included two patient groups. Eleven patients enrolled by a single center in the Rhythm II ICD trial underwent prospective comparisons of the AVTI at the VV interval determined by the IEGM VV method versus the maximum AVTI at the echocardiographically determined optimal VV delay. In 61 patients enrolled in the RHYTHM VV trial, the same testing methods were compared retrospectively. In the prospective study, the maximum AVTI by the ECHO-based method (24.3 +/- 7.9 cm), was closely correlated with maximum AVTI by the IEGM-based method (23.9 +/- 7.9 cm; concordance correlation coefficient = 0.99; 95% confidence, lower limit of 98%. Likewise, in the retrospective analysis, the ECHO-determined maximum AVTI (22.1 +/- 8.2 cm) was similar to that determined by the IEGM-based method (20.9 +/- 8.3 cm; concordance correlation coefficient = 0.98; 95% confidence, lower limit of 97%).
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