ANTI HYPERGLYCEMIC EVALUATION OF TERMINALIA CHEBULA LEAVES

Objective: The antihyperglycaemic potentiality of Terminalia chebula leaves has not yet been investigated thoroughly compared to its fruit counterpart. Therefore, the purpose of this study was to assess the hypoglycaemic potentiality of Terminalia chebula Retz leaves both in vitro and in vivo. Methods: Fresh leaves of T. chebula were collected, authenticated and grounded to a fine powder. The powdered material was extracted in methanol. The hypoglycaemic potentiality of the extract was accessed in vitro using enzyme alpha-amylase and alpha-glucosidase. The antihyperglycaemic activity of the methanol extract active fraction was accessed in vitro and in vivo. The active fraction thus obtained was partially characterized using Fourier transform infrared spectroscopy (FTIR) and High-performance liquid chromatography (HPLC) analysis. Results: The crude leave methanol extract of Terminalia chebula demonstrated 100% α glucosidase inhibition with IC50–0.956±0.342 mg/ml compared to standard drug acarbose. Oral administration of the active fraction to diabetic rats loaded with maltose significantly (P<0.05) retarded the postprandial spike of blood glucose level compared to standard drug acarbose. Partial characterization of the fraction reveals the presence of hydrosoluble tannin gallic acid. Conclusion: The study provides an in vitro and in vivo rationale evidence of Terminalia chebula leaves to retard postprandial hyperglycemia

Amelioration of Glucolipotoxicity-Induced Endoplasmic Reticulum Stress by a “Chemical Chaperone” in Human THP-1 Monocytes

Chronic ER stress is emerging as a trigger that imbalances a number of systemic and arterial-wall factors and promote atherosclerosis. Macrophage apoptosis within advanced atherosclerotic lesions is also known to increase the risk of atherothrombotic disease. We hypothesize that glucolipotoxicity might mediate monocyte activation and apoptosis through ER stress. Therefore, the aims of this study are (a) to investigate whether glucolipotoxicity could impose ER stress and apoptosis in THP-1 human monocytes and (b) to investigate whether 4-Phenyl butyric acid (PBA), a chemical chaperone could resist the glucolipotoxicity-induced ER stress and apoptosis. Cells subjected to either glucolipotoxicity or tunicamycin exhibited increased ROS generation, gene and protein (PERK, GRP-78, IRE1α, and CHOP) expression of ER stress markers. In addition, these cells showed increased TRPC-6 channel expression and apoptosis as revealed by DNA damage and increased caspase-3 activity. While glucolipotoxicity/tunicamycin increased oxidative stress, ER stress, mRNA expression of TRPC-6, and programmed the THP-1 monocytes towards apoptosis, all these molecular perturbations were resisted by PBA. Since ER stress is one of the underlying causes of monocyte dysfunction in diabetes and atherosclerosis, our study emphasize that chemical chaperones such as PBA could alleviate ER stress and have potential to become novel therapeutics

Sustainable Waste-to-Energy Technologies: Bioelectrochemical Systems

The food industry produces a large amount of waste and wastewater, of which most of the constituents are carbohydrates, proteins, lipids, and organic fibers. Therefore food wastes are highly biodegradable and energy rich. Bioelectrochemical systems (BESs) are systems that use microorganisms to biochemically catalyze complex substrates into useful energy products, in which the catalytic reactions take place on electrodes. Microbial fuel cells (MFCs) are a type of bioelectrochemical systems that oxidize substrates and generate electric current. Microbial electrolysis cells (MECs) are another type of bioelectrochemical systems that use an external power source to catalyze the substrate into by-products such as hydrogen gas, methane gas, or hydrogen peroxide. BESs are advantageous due to their ability to achieve a degree of substrate remediation while generating energy. This chapter presents an extensive literature review on the use of MFCs and MECs to remediate and recover energy from food industry waste. These bioelectrochemical systems are still in their infancy state and further research is needed to better understand the systems and optimize their performance. Major challenges and limitations for the use of BESs are summarized and future research needs are identified

Accelerating deep learning training : a storage perspective

Deep Learning, specifically Deep Neural Networks (DNNs), is stressing storage systems in new ways, moving the training bottleneck to the data pipeline (fetching, pre-processing data, and writing checkpoints), rather than computation at the GPUs; this leaves the expensive accelerator devices stalled for data. While prior research has explored different ways of accelerating DNN training time, the impact of storage systems, specifically the data pipeline, on ML training has been relatively unexplored. In this dissertation, we study the role of data pipeline in various training scenarios, and based on the insights from our study, we present the design and evaluation of systems that accelerate training. We first present a comprehensive analysis of how the storage subsystem affects the training of the widely used DNN models by building a tool, DS-Analyzer. Our study reveals that in many cases, DNN training time is dominated by data stalls: time spent waiting for data to be fetched from(or written to) storage and pre-processed. We then describe CoorDL, a user-space data loading library to address data stalls in dedicated single-user servers with fixed resource capacities. Next, we design and evaluate Synergy, a work-load aware scheduler for shared GPU clusters that mitigates data stalls by allocating auxiliary resources like CPU and memory cognizant of workload requirements. Finally, we present CheckFreq, a framework that frequently writes model state to storage (checkpoint) for fault-tolerance, thereby reducing wasted GPU work on job interruptions, while also minimizing stalls due to checkpointing. Our dissertation shows that data stalls squander away the improved performance of faster GPUs. Our dissertation further demonstrates that an efficient data pipeline is critical to speeding up end-to-end training, by building and evaluating systems that mitigate data stalls in several training scenarios.Computer Science