Co-exposure to particulate matter and humidity increases blood pressure in hypertensive mice via the TRPV4-cPLA2-COX2 pathway.

Epidemiological studies have demonstrated that particulate matter (PM) can induce or exacerbate hypertension. High relative humidity has been associated with elevated blood pressure in certain regions. However, the coupling effect of humidity and PM on elevated blood pressure and the underlying mechanisms remain unknown. Herein, we aimed to explore the effects of exposure to PM and/or high relative humidity on hypertension, as well as elucidate underlying mechanisms. Male C57/BL6 mice were intraperitoneally administered NG-nitro-L-arginine methyl ester (L-NAME) to establish a hypertensive mouse model. The hypertensive mice were exposed to PM (0.15 mg/kg/day) and/or different relative humidities (45/90%) for eight weeks. Histopathological changes, systolic blood pressure (SBP), endothelial-derived contracting factors (thromboxane B2 [TXB2], Prostaglandin F2α [PGF2α], endothelin-1 [ET-1], and angiotensin II [Ang II]), and relaxing factors (prostaglandin I2 [PGI2] and nitric oxide [NO]) were measured to assess the effects of PM exposure and humidity on hypertension in mice. Levels of transient receptor potential vanilloid 4 (TRPV4), cytosolic phospholipase A2 (cPLA2), and cyclooxygenase 2 (COX2) were measured to explore their potential mechanisms. Herein, exposure to 90% relative humidity or PM alone had a slight but insignificant effect on hypertension. However, pathological changes and elevated blood pressure were markedly exacerbated following exposure to PM and 90% relative humidity. Levels of PGF2α, TXB2, and ET-1 were significantly increased, whereas the PGI2 level was substantially decreased. HC-067047-mediated blockade of TRPV4 suppressed TRPV4, cPLA2, and COX2 expression and effectively alleviated the increased blood pressure induced by exposure to PM and 90% relative humidity. These results indicate that 90% relative humidity and PM can activate the TRPV4-cPLA2-COX2 ion channel in the aorta, altering the endothelial-derived contracting and relaxing factors and enhancing blood pressure in hypertensive mice.

Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior.

BACKGROUND AND OBJECTIVE: Neural network based image reconstruction methods are becoming increasingly popular. However, limited training data and the lack of theoretical guarantees for generalizability raised concerns, especially in biomedical imaging applications. These challenges are known to lead to an unstable reconstruction process that poses significant problems in biomedical image reconstruction. In this paper, we present a new framework that uses untrained generator networks to tackle this challenge, leveraging the structure of deep networks for regularizing solutions based on a technique known as Deep Image Prior (DIP). METHODS: To achieve a high reconstruction accuracy, we propose a framework optimizing both the latent vector and the weights of a generator network during the reconstruction process. We also propose the corresponding reconstruction strategies to improve the stability and convergent performance of the proposed framework. Furthermore, instead of calculating forward projection in each iteration, we propose implementing its normal operator as a convolutional kernel under parallel beam geometry, thus greatly accelerating the calculation. RESULTS: Our experiments show that the proposed framework has significant improvements over other state-of-the-art conventional, pre-trained, and untrained methods under sparse-view, limited-angle, and low-dose conditions. CONCLUSIONS: Applying to parallel beam X-ray imaging, our framework shows advantages in speed, accuracy, and stability of the reconstruction process. We also show that the proposed framework is compatible with all differentiable regularizations that are commonly used in biomedical image reconstruction literature. Our framework can also be used as a post-processing technique to further improve the reconstruction generated by any other reconstruction methods. Furthermore, the proposed framework requires no training data and can be adjusted on-demand to adapt to different conditions (e.g. noise level, geometry, and imaged object).

Exact gram filtering and efficient backprojection for iterative CT reconstruction.

PURPOSE: Forward and backprojections are the basis of all model-based iterative reconstruction (MBIR) methods. However, computing these accurately is time-consuming. In this paper, we present a method for MBIR in parallel X-ray beam geometry that utilizes a Gram filter to efficiently implement forward and backprojection. METHODS: We propose using voxel-basis and modeling its footprint in a box spline framework to calculate the Gram filter exactly and improve the performance of backprojection. In the special case of parallel X-ray beam geometry, the forward and backprojection can be implemented by an estimated Gram filter efficiently if the sinogram signal is bandlimited. In this paper, a specialized sinogram interpolation method is proposed to eliminate the bandlimited prerequisite and thus improve the reconstruction accuracy. We build on this idea by utilizing the continuity of the voxel-basis' footprint, which provides a more accurate sinogram interpolation and further improves the efficiency and quality of backprojection. In addition, the detector blur effect can be efficiently accounted for in our method to better handle realistic scenarios. RESULTS: The proposed method is tested on both phantom and real computed tomography (CT) images under different resolutions, sinogram sampling steps, and noise levels. The proposed method consistently outperforms other state-of-the-art projection models in terms of speed and accuracy for both backprojection and reconstruction. CONCLUSIONS: We proposed a iterative reconstruction methodology for 3D parallel-beam X-ray CT reconstruction. Our experimental results demonstrate that the proposed methodology is accurate, fast, and reproducible, and outperforms alternative state-of-the-art projection models on both backprojection and reconstruction results significantly.