Anti-AAMP monoclonal antibody, clone FQS23470

Despite the edible fungus Amillariella mellea possessing a variety of biological activities, its effects on diabetes are still unclear. Polysaccharides are the main bioactive ingredients. In order to destroy the cell wall to obtain more polysaccharides, we used NaOH solution to extract Amillariella mellea fruiting bodies. The alkali extraction (AAMP) was identified as a polysaccharide-enriched fraction. Using type 2 diabetic rats induced by co-treatment of a high fat diet (HFD) and dexamethasone (DEX), we evaluated the hypoglycemic effects of AAMP. The results showed that oral administration of a high dose of AAMP markedly lowered fasting blood glucose, improving glucose intolerance and insulin resistance. AAMP also enhanced the level of LPL and the expressions of two critical lipases ATGL and HSL, leading to a decrease of serum triglyceride. In addition, AAMP specifically suppressed the expression of SREBP-1c, resulting in AAMP observably inhibiting lipid accumulation in the liver. These findings demonstrated that the improvement of AAMP on HFD/DEX-induced insulin resistance was correlated with its regulation of lipid metabolism. Our results indicated that AAMP could be a novel natural drug or health food used for the treatment of diabetes.

It is highly desired for existing distributed graph processing systems to support both offline analytics and online queries adaptively. Existing offline graph analytics systems are mostly based on synchronous model. Although achieving high throughput, they suffer relatively high latency in answering simple queries due to synchronization overhead and slow convergence. On the other hand, online graph query systems adopting asynchronous model can response at any time, while incur overwhelmed messages and network packets, making them unable to meet the high throughput demand of offline analytics. In this work, we propose an adaptive asynchronous message processing (AAMP) method, which improves the efficiency of network communication while maintains low latency, to efficiently support offline analytics and online queries in one graph processing framework. We then design GiraphAsync, an implementation of AAMP on top of Apache Giraph, and evaluate it using several representative offline analytics and online queries on large graph datasets. Experimental results show that GiraphAsync gains an up to 10X improvement over synchronous model systems for graph analytics, while performs as well as specialized systems for online graph queries.