The effect of air pollution on the changing pattern of glomerulopathy has not been studied. We estimated the profile of and temporal change in glomerular diseases in an 11-year renal biopsy series including 71,151 native biopsies at 938 hospitals spanning 282 cities in China from 2004 to 2014, and examined the association of long-term exposure to fine particulate matter of <2.5 μm (PM) with glomerulopathy. After age and region standardization, we identified IgA nephropathy as the leading type of glomerulopathy, with a frequency of 28.1%, followed by membranous nephropathy (MN), with a frequency of 23.4%. Notably, the adjusted odds for MN increased 13% annually over the 11-year study period, whereas the proportions of other major glomerulopathies remained stable. During the study period, 3-year average PM exposure varied among the 282 cities, ranging from 6 to 114 μg/m (mean, 52.6 μg/m). Each 10 μg/m increase in PM concentration associated with 14% higher odds for MN (odds ratio, 1.14; 95% confidence interval, 1.10 to 1.18) in regions with PM concentration >70 μg/m We also found that higher 3-year average air quality index was associated with increased risk of MN. In conclusion, in this large renal biopsy series, the frequency of MN increased over the study period, and long-term exposure to high levels of PM was associated with an increased risk of MN.

Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results.

Image reconstruction from low-count PET projection data is challenging because the inverse problem is ill-posed. Prior information can be used to improve image quality. Inspired by the kernel methods in machine learning, this paper proposes a kernel based method that models PET image intensity in each pixel as a function of a set of features obtained from prior information. The kernel-based image model is incorporated into the forward model of PET projection data and the coefficients can be readily estimated by the maximum likelihood (ML) or penalized likelihood image reconstruction. A kernelized expectation-maximization (EM) algorithm is presented to obtain the ML estimate. Computer simulations show that the proposed approach can achieve better bias versus variance trade-off and higher contrast recovery for dynamic PET image reconstruction than the conventional maximum likelihood method with and without post-reconstruction denoising. Compared with other regularization-based methods, the kernel method is easier to implement and provides better image quality for low-count data. Application of the proposed kernel method to a 4D dynamic PET patient dataset showed promising results.

This paper extends the kernel method that was proposed previously for dynamic PET reconstruction, to incorporate anatomical side information into the PET reconstruction model. In contrast to existing methods that incorporate anatomical information using a penalized likelihood framework, the proposed method incorporates this information in the simpler maximum likelihood (ML) formulation and is amenable to ordered subsets. The new method also does not require any segmentation of the anatomical image to obtain edge information. We compare the kernel method with the Bowsher method for anatomically-aided PET image reconstruction through a simulated data set. Computer simulations demonstrate that the kernel method offers advantages over the Bowsher method in region of interest (ROI) quantification. Additionally the kernel method is applied to a 3D patient data set. The kernel method results in reduced noise at a matched contrast level compared with the conventional ML expectation maximization (EM) algorithm.

Transforming growth factor–β (TGF-β) is a well-established central mediator of renal fibrosis, a common outcome of almost all progressive chronic kidney diseases. Here, we identified a poorly conserved and kidney-enriched long noncoding RNA in TGF-β1–stimulated human tubular epithelial cells and fibrotic kidneys, which we termed TGF-β/Smad3-interacting long noncoding RNA (lnc-TSI). Lnc-TSI was transcriptionally regulated by Smad3 and specifically inhibited TGF-β–induced Smad3 phosphorylation and downstream profibrotic gene expression. Lnc-TSI acted by binding with the MH2 domain of Smad3, blocking the interaction of Smad3 with TGF-β receptor I independent of Smad7. Delivery of human lnc-TSI into unilateral ureteral obstruction (UUO) mice, a well-established model of renal fibrosis, inhibited phosphorylation of Smad3 in the kidney and attenuated renal fibrosis. In a cohort of 58 patients with biopsy-confirmed IgA nephropathy (IgAN), lnc-TSI renal expression negatively correlated with the renal fibrosis index (r = −0.56, P < 0.001) after adjusting for cofounders. In a longitudinal study, 32 IgAN patients with low expression of renal lnc-TSI at initial biopsy had more pronounced increases in their renal fibrosis index and experienced stronger declines in renal function at repeat biopsy at a mean of 48 months of follow-up. These data suggest that lnc-TSI reduced renal fibrogenesis through negative regulation of the TGF-β/Smad pathway.

Dynamic positron emission tomography (PET) can monitor spatiotemporal distribution of radiotracer in vivo. The spatiotemporal information can be used to estimate parametric images of radiotracer kinetics that are of physiological and biochemical interests. Direct estimation of parametric images from raw projection data allows accurate noise modeling and has been shown to offer better image quality than conventional indirect methods, which reconstruct a sequence of PET images first and then perform tracer kinetic modeling pixel-by-pixel. Direct reconstruction of parametric images has gained increasing interests with the advances in computing hardware. Many direct reconstruction algorithms have been developed for different kinetic models. In this paper we review the recent progress in the development of direct reconstruction algorithms for parametric image estimation. Algorithms for linear and nonlinear kinetic models are described and their properties are discussed.

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Parametric imaging using dynamic positron emission tomography (PET) provides important information for biological research and clinical diagnosis. Indirect and direct methods have been developed for reconstructing linear parametric images from dynamic PET data. Indirect methods are relatively simple and easy to implement because the image reconstruction and kinetic modeling are performed in two separate steps. Direct methods estimate parametric images directly from raw PET data and are statistically more efficient. However, the convergence rate of direct algorithms can be slow due to the coupling between the reconstruction and kinetic modeling. Here we present two fast gradient-type algorithms for direct reconstruction of linear parametric images. The new algorithms decouple the reconstruction and linear parametric modeling at each iteration by employing the principle of optimization transfer. Convergence speed is accelerated by running more sub-iterations of linear parametric estimation because the computation cost of the linear parametric modeling is much less than that of the image reconstruction. Computer simulation studies demonstrated that the new algorithms converge much faster than the traditional expectation maximization (EM) and the preconditioned conjugate gradient algorithms for dynamic PET.