Metformin Reduces the Progression of Atherogenesis by Regulating the Sestrin2-mTOR Pathway in Obese and Diabetic Rats.

Objective: In previous research, we found that Sestrin2 has a strong association with plasma atherogenicity and combats the progression of atherogenesis by regulating the AMPK-mTOR pathway. Metformin, an activator of AMPK, is widely used as a first-line therapy for diabetes, but its role in preventing atherosclerosis and cardiac outcomes is unclear. Hence, we aimed to assess the effect of metformin on preventing atherosclerosis and its regulatory role in the Sestrin2-AMPK -mTOR pathway in obese/diabetic rats. Methods: Animals were fed a high-fat diet to induce obesity, administered streptozotocin to induce diabetes, and then treated with metformin (150 mg/kg body weight) for 14 weeks. Aorta and heart tissues were analyzed for Sestrin2 status by western blotting and immunohistochemistry, AMPK and mTOR activities were investigated using western blotting, and atherogenicity-related events were evaluated using reverse transcription quantitative polymerase chain reaction and histology. Results: Obese and diabetic rats showed significant decrease in Sestrin2 levels and AMPK activity, accompanied by increased mTOR activity in the heart and aorta tissues. Metformin treatment significantly restored Sestrin2 and AMPK levels, reduced mTOR activity, and restored the altered expression of inflammatory markers and adhesion molecules in obese and diabetic rats to normal levels. A histological analysis of samples from obese and diabetic rats showed atherosclerotic lesions both in aorta and heart tissues. The metformin-treated rats showed a decrease in atherosclerotic lesions, cardiac hypertrophy, and cardiomyocyte degeneration. Conclusion: This study presents further insights into the beneficial effects of metformin and its protective role against atherosclerosis through regulation of the Sestrin2-AMPK-mTOR pathway.

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms.

In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient energy usage. Therefore, accurate estimation of agricultural energy usage and environmental factors is considered as one of the significant tasks for crop growth control. The growth sequences of crops in agricultural environments like smart farms are related to agricultural energy usage and consumption. This study aims to develop and validate an algorithm that can interpret the crop growth rate response to environmental and solar energy factors based on machine learning, and to evaluate the algorithm's accuracy compared to the base model. The proposed model was determined through a comparative experiment of three representative machine learning techniques, which are random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM), considering the energy usage for environmental control is highly associated with the paprika crop growth. Through the experiment performance with real data gathered from a paprika smart farm in South Korea, the multi-level RF can effectively predict paprika growth with an accuracy of 0.88, considering data analysis of factors that use solar energy. As a result of the experiment with the suggested model, the growth factors such as leaf length, leaf width, and environmental factors were found. Furthermore, the proposed algorithm can contribute to the development of applications through analysis of the crop growth big data for various plants in agricultural environments such as a smart farm.

Asymmetric dimethylarginine (ADMA) accelerates renal cell fibrosis under high glucose condition through NOX4/ROS/ERK signaling pathway.

We previously reported that the circulatory level of Asymmetric dimethylarginine (ADMA), an endogenous competitive inhibitor of nitric oxide synthase, was increased in diabetic kidney disease patients. However, the mechanism and the role of ADMA in diabetic kidney injury remain unclear. Hence, our principal aim is to investigate the causal role of ADMA in the progression of renal cell fibrosis under high glucose (HG) treatment and to delineate its signaling alterations in kidney cell injury. High Glucose/ADMA significantly increased fibrotic events including cell migration, invasion and proliferation along with fibrotic markers in the renal cells; whereas ADMA inhibition reversed the renal cell fibrosis. To delineate the central role of ADMA induced fibrotic signaling pathway and its downstream signaling, we analysed the expression levels of fibrotic markers, NOX4, ROS and ERK activity by using specific inhibitors and genetic manipulation techniques. ADMA stimulated the ROS generation along with a significant increase in NOX4 and ERK activity. Further, we observed that ADMA activated NOX-4 and ERK are involved in the extracellular matrix proteins accumulation. Also, we observed that ADMA induced ERK1/2 phosphorylation was decreased after NOX4 silencing. Our study mechanistically demonstrates that ADMA is involved in the progression of kidney cell injury under high glucose condition by targeting coordinated complex mechanisms involving the NOX4- ROS-ERK pathway.

Disturbed flow mediated modulation of shear forces on endothelial plane: A proposed model for studying endothelium around atherosclerotic plaques.

Disturbed fluid flow or modulated shear stress is associated with vascular conditions such as atherosclerosis, thrombosis, and aneurysm. In vitro simulation of the fluid flow around the plaque micro-environment remains a challenging approach. Currently available models have limitations such as complications in protocols, high cost, incompetence of co-culture and not being suitable for massive expression studies. Hence, the present study aimed to develop a simple, versatile model based on Computational Fluid Dynamics (CFD) simulation. Current observations of CFD have shown the regions of modulated shear stress by the disturbed fluid flow. To execute and validate the model in real sense, cell morphology, cytoskeletal arrangement, cell death, reactive oxygen species (ROS) profile, nitric oxide production and disturbed flow markers under the above condition were assessed. Endothelium at disturbed flow region which had been exposed to low shear stress and swirling flow pattern showed morphological and expression similarities with the pathological disturbed flow environment reported previously. Altogether, the proposed model can serve as a platform to simulate the real time micro-environment of disturbed flow associated with eccentric plaque shapes and the possibilities of studying its downstream events.