Anti-EMT and anti-fibrosis effects of protocatechuic aldehyde in renal proximal tubular cells and the unilateral ureteral obstruction animal model.

CONTEXT: Protocatechuic aldehyde (PCA) is a natural product that has various benefits for fibrosis. OBJECTIVE: This study evaluated the effects of PCA on renal fibrosis. MATERIALS AND METHODS: Epithelial-mesenchymal transition (EMT) was induced by 20 ng/mL transforming growth factor-ß1 (TGF-ß1), followed by treatment with 1 and 5 µM PCA, in the rat renal proximal tubular cell line NRK-52E. Cell viability, protein expression, and scratch wound-healing assays were conducted. Sprague-Dawley (SD) rats underwent unilateral ureteral obstruction (UUO) surgery for renal fibrosis indication and were treated with 50 and 100 mg/kg PCA for 14 days. RESULTS: The IC50 of PCA was appropriately 13.75 ± 1.91 µM in NRK-52E cells, and no significant difference at concentrations less than 5 µM. PCA ameliorated TGF-ß1-induced EMT, such as enhanced E-cadherin and decreased vimentin. Fibrotic markers collagen IV and α-smooth muscle actin (α-SMA) increased in TGF-ß1-induced NRK-52E. Moreover, PCA reduced TGF-ß1-induced migration in the wound-healing assay. Analysis of rat kidneys indicated that PCA reduced UUO-induced hydronephrosis (control: 15.11 ± 1.00%; UUO: 39.89 ± 1.91%; UUO + PCA50: 18.37 ± 1.61%; UUO + PCA100: 17.67 ± 1.39%). Protein level demonstrated that PCA not only decreased vimentin expression and enhanced E-cadherin expression, but inhibited UUO-induced collagen IV and α-SMA upregulation, indicating that it could mitigate EMT in a rat model of UUO-induced renal fibrosis. DISCUSSION AND CONCLUSIONS: This study suggested that PCA decreases TGF-ß1-induced fibrosis and EMT in vitro and in vivo. These findings demonstrate pharmacological effects of PCA and might be a potential strategy for the prevention of organ fibrosis in clinics.

PMMA Application in Piezo Actuation Jet for Dissipating Heat of Electronic Devices.

The present study utilizes an acrylic (PMMA) plate with circular piezoelectric ceramics (PC) as an actuator to design and investigate five different types of piezo actuation jets (PAJs) with operating conditions. The results show that the heat transfer coefficient of a device of PAJ is 200% greater than that of a traditional rotary fan when PAJ is placed at the proper distance of 10 to 20 mm from the heat source, avoiding the suck back of surrounding fluids. The cooling effect of these five PAJs was calculated by employing the thermal analysis method and the convection thermal resistance of the optimal PAJ can be reduced by about 36%, while the voltage frequency, wind speed, and noise were all positively correlated. When the supplied piezoelectric frequency is 300 Hz, the decibel level of the noise is similar to that of a commercial rotary fan. The piezoelectric sheets had one of two diameters of 31 mm or 41 mm depending on the size of the tested PAJs. The power consumption of a single PAJ was less than 10% of that of a rotary fan. Among the five types of PAJ, the optimal one has the characteristics that the diameter of the piezoelectric sheet is 41 mm, the piezoelectric spacing is 2 mm, and the length of the opening is 4 mm. Furthermore, the optimal operating conditions are a voltage frequency of 300 Hz and a placement distance of 20 mm in the present study.

Evaluation of the Therapeutic Effects of Protocatechuic Aldehyde in Diabetic Nephropathy.

Diabetic nephropathy (DN) is one of the most severe chronic kidney diseases in diabetes and is the main cause of end-stage renal disease (ESRD). Protocatechuic aldehyde (PCA) is a natural product with a variety of effects on pulmonary fibrosis. In this study, we examined the effects of PCA in C57BL/KS db/db male mice. Kidney morphology, renal function indicators, and Western blot, immunohistochemistry, and hematoxylin and eosin (H&E) staining data were analyzed. The results revealed that treatment with PCA could reduce diabetic-induced renal dysfunction, as indicated by the urine albumin-to-creatinine ratio (db/m: 120.1 ± 46.1µg/mg, db/db: 453.8 ± 78.7 µg/mg, db/db + 30 mg/kg PCA: 196.6 ± 52.9 µg/mg, db/db + 60 mg/kg PCA: 163.3 ± 24.6 µg/mg, p < 0.001). However, PCA did not decrease body weight, fasting plasma glucose, or food and water intake in db/db mice. H&E staining data revealed that PCA reduced glomerular size in db/db mice (db/m: 3506.3 ± 789.3 µm2, db/db: 6538.5 ± 1818.6 µm2, db/db + 30 mg/kg PCA: 4916.9 ± 1149.6 µm2, db/db + 60 mg/kg PCA: 4160.4 ± 1186.5 µm2p < 0.001). Western blot and immunohistochemistry staining indicated that PCA restored the normal levels of diabetes-induced fibrosis markers, such as transforming growth factor-beta (TGF-ß) and type IV collagen. Similar results were observed for epithelial-mesenchymal transition-related markers, including fibronectin, E-cadherin, and α-smooth muscle actin (α-SMA). PCA also decreased oxidative stress and inflammation in the kidney of db/db mice. This research provides a foundation for using PCA as an alternative therapy for DN in the future.

To cut or not to cut? Assessing the modular structure of brain networks.

A wealth of methods has been developed to identify natural divisions of brain networks into groups or modules, with one of the most prominent being modularity. Compared with the popularity of methods to detect community structure, only a few methods exist to statistically control for spurious modules, relying almost exclusively on resampling techniques. It is well known that even random networks can exhibit high modularity because of incidental concentration of edges, even though they have no underlying organizational structure. Consequently, interpretation of community structure is confounded by the lack of principled and computationally tractable approaches to statistically control for spurious modules. In this paper we show that the modularity of random networks follows a transformed version of the Tracy-Widom distribution, providing for the first time a link between module detection and random matrix theory. We compute parametric formulas for the distribution of modularity for random networks as a function of network size and edge variance, and show that we can efficiently control for false positives in brain and other real-world networks.

Frontal information flow and connectivity in psychopathy.

Despite accumulating evidence of structural deficits in individuals with psychopathy, especially in frontal regions, our understanding of systems-level disturbances in cortical networks remains limited. We applied novel graph theory-based methods to assess information flow and connectivity based on cortical thickness measures in 55 individuals with psychopathy and 47 normal controls. Compared with controls, the psychopathy group showed significantly altered interregional connectivity patterns. Furthermore, bilateral superior frontal cortices in the frontal network were identified as information flow control hubs in the psychopathy group in contrast to bilateral inferior frontal and medial orbitofrontal cortices as network hubs of the controls. Frontal information flow and connectivity may have a significant role in the neuropathology of psychopathy.

Modularity-based graph partitioning using conditional expected models.

Modularity-based partitioning methods divide networks into modules by comparing their structure against random networks conditioned to have the same number of nodes, edges, and degree distribution. We propose a novel way to measure modularity and divide graphs, based on conditional probabilities of the edge strength of random networks. We provide closed-form solutions for the expected strength of an edge when it is conditioned on the degrees of the two neighboring nodes, or alternatively on the degrees of all nodes comprising the network. We analytically compute the expected network under the assumptions of Gaussian and Bernoulli distributions. When the Gaussian distribution assumption is violated, we prove that our expression is the best linear unbiased estimator. Finally, we investigate the performance of our conditional expected model in partitioning simulated and real-world networks.