Dietary magnesium is able to influence the relationship between vitamin C and estimated glomerular filtration rate: A cross-sectional study.

The estimated glomerular filtration rate (eGFR) is a comprehensive index that is widely used to assess renal function. Although studies have confirmed a correlation between eGFR and dietary vitamin C, the impact of varying levels of vitamin C on eGFR remains unclear. Additionally, the interaction between dietary magnesium intake and vitamin C concentration on eGFR is not well understood. As such, the objective of this study was to investigate the relationship between dietary magnesium intake and vitamin C in relation to eGFR. This study analyzed the data of consecutive NHANES from 2005 to 2018. We included 17,633 participants aged 20 or older and used multiple linear regression analysis to evaluate the relationship between dietary vitamin C and eGFR. Dietary Mg intake from experimental data was dichotomized into a low dietary Mg intake group (≤254 mg/day) and a normal dietary Mg intake group (>254 mg/day). To evaluate the impact of dietary magnesium intake on eGFR, a multivariable linear regression was conducted utilizing an interaction test between dietary vitamin C and eGFR. We discovered a positive association between dietary vitamin C content and eGFR. The relationship between dietary vitamin C levels and eGFR differed between individuals with low Mg intake and those with normal Mg intake (ß: 2.96 95% CI:1.63 ~ 4.29 vs. ß: 1.05 95% CI: -0.15 to 2.25), and the positive association of high dietary vitamin C content with eGFR was stronger in the low Mg intake group. Furthermore, we observed that dietary magnesium intake significantly altered the positive association between dietary vitamin C and eGFR (interaction value of 0.020). Our experimental study revealed that the interaction between dietary magnesium and dietary vitamin C can significantly impact eGFR. This finding carries significant implications for the treatment of diseases resulting from abnormal eGFR, as well as the selection of clinically relevant drugs.

Targeting the TGF-ß signaling pathway for fibrosis therapy: a patent review (2015-2020).

INTRODUCTION: Fibrosis is a serious disease that occurs in many organs, such as kidney, liver and lung. The deterioration of these organs ultimately leads to death. Due to the complex mechanisms of fibrosis, research and development of antifibrotic drugs is difficult. One solution is to focus on core pathways, one of which is the TGF-ß signaling pathway. In virtually every type of fibrosis, TGF-ß signaling is recognized as a critical pathway. AREA COVERED: This review discusses patents on active molecules related to the TGF-ß signaling. Molecules targeting components related to the activation of TGF-ß are introduced. Several strategies preventing signal propagation from active TGF-ß to downstream targets are also introduced, including TGF-ß antibodies, TGF-ß ligand traps, and inhibitors of TGF-ß receptor kinases. Finally, molecules affecting downstream targets in both canonical and noncanonical TGF-ß signaling pathways are described. EXPERT OPINION: Since the approval of pirfenidone, targeting TGF-ß signaling has been anticipated as an effective therapy for fibrosis. The potential of this therapy has been further supported by emerging patents on the TGF-ß signaling. This pathway can be entirely inhibited, from the activation of TGF-ß to downstream signaling. Inhibiting TGF-ß signaling is expected to provide more effective treatments for fibrosis.

Volume illustration of muscle from diffusion tensor images.

Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.

A novel interface for interactive exploration of DTI fibers.

Visual exploration is essential to the visualization and analysis of densely sampled 3D DTI fibers in biological specimens, due to the high geometric, spatial, and anatomical complexity of fiber tracts. Previous methods for DTI fiber visualization use zooming, color-mapping, selection, and abstraction to deliver the characteristics of the fibers. However, these schemes mainly focus on the optimization of visualization in the 3D space where cluttering and occlusion make grasping even a few thousand fibers difficult. This paper introduces a novel interaction method that augments the 3D visualization with a 2D representation containing a low-dimensional embedding of the DTI fibers. This embedding preserves the relationship between the fibers and removes the visual clutter that is inherent in 3D renderings of the fibers. This new interface allows the user to manipulate the DTI fibers as both 3D curves and 2D embedded points and easily compare or validate his or her results in both domains. The implementation of the framework is GPU based to achieve real-time interaction. The framework was applied to several tasks, and the results show that our method reduces the user's workload in recognizing 3D DTI fibers and permits quick and accurate DTI fiber selection.