Bisphenol A (BPA) exposure aggravates hepatic oxidative stress and inflammatory response under hypertensive milieu - Impact of low dose on hepatocytes and influence of MAPK and ER stress pathways.

Human exposure to the hazardous chemical, Bisphenol A (BPA), is almost ubiquitous. Due to the prevalence of hypertension (CVD risk factor) in the aged human population, it is necessary to explore its adverse effect in hypertensive subjects. The current study exposed the Nω-nitro-l-arginine methyl ester (L-NAME) induced hypertensive Wistar rats to human exposure relevant low dose of BPA (50 µg/kg) for 30 days period. The liver biochemical parameters, histopathology, immunohistochemistry, gene expression (RT-qPCR), trace elements (ICP-MS), primary rat hepatocytes cell culture and metabolomic (1H NMR) assessments were performed. Results illustrate that BPA exposure potentiates/aggravates hypertension induced tissue abnormalities (hepatic fibrosis), oxidative stress, ACE activity, malfunction of the antioxidant system, lipid abnormalities and inflammatory factor (TNF-α and IL-6) expression. Also, in cells, BPA increased ROS generation, mitochondrial dysfunction and lipid peroxidation without any impact on cytotoxicity and caspase 3 and 9 activation. Notably, BPA exposure modulate lipid metabolism (cholesterol and fatty acid) in primary hepatocytes. Finally, the influence of ERK1/2, p38MAPK, ER stress and oxidative stress during relatively high dose of BPA elicited cytotoxicity was observed. Therefore, a precise hazardous risk investigation of BPA exposure in hypertensive populations is highly recommended.

Power and Area Efficient Cascaded Effectless GDI Approximate Adder for Accelerating Multimedia Applications Using Deep Learning Model.

Approximate computing is an upsurging technique to accelerate the process through less computational effort while keeping admissible accuracy of error-tolerant applications such as multimedia and deep learning. Inheritance properties of the deep learning process aid the designer to abridge the circuitry and also to increase the computation speed at the cost of the accuracy of results. High computational complexity and low-power requirement of portable devices in the dark silicon era sought suitable alternate for Complementary Metal Oxide Semiconductor (CMOS) technology. Gate Diffusion Input (GDI) logic is one of the prompting alternatives to CMOS logic to reduce transistors and low-power design. In this work, a novel energy and area efficient 1-bit GDI-based full swing Energy and Area efficient Full Adder (EAFA) with minimum error distance is proposed. The proposed architecture was constructed to mitigate the cascaded effect problem in GDI-based circuits. It is proved by extending the proposed 1-bit GDI-based adder for different 16-bit Energy and Area Efficient High-Speed Error-Tolerant Adders (EAHSETA) segmented as accurate and inaccurate adder circuits. The proposed adder's design metrics in terms of delay, area, and power dissipation are verified through simulation using the Cadence tool. The proposed logic is deployed to accelerate the convolution process in the Low-Weight Digit Detector neural network for real-time handwritten digit classification application as a case study in the Intel Cyclone IV Field Programmable Gate Array (FPGA). The results confirm that our proposed EAHSETA occupies fewer logic elements and improves operation speed with the speed-up factor of 1.29 than other similar techniques while producing 95% of classification accuracy.