Anti-EMB monoclonal antibody, clone FQS22528

Leukemia threatens millions of peoples health and lives, and the pesticide-induced leukemia has been increasingly concerned because of the etiologic exposure. In this paper, cytotoxic effect of emamectin benzoate (EMB), an excellent natural-product insecticide, was evaluated through monitoring cell viability, cell apoptosis, mitochondrial membrane potential and intracellular Ca2+ concentration ([Ca2+](i)) in leukemia K562 and Molt-4 cells. Following the exposure to EMB, cell viability was decreased and positive apoptosis of K562 and Molt-4 cells was increased in a concentration- and time- dependent fashion. In the treatment of 10 M EMB, apoptotic cells accounted for 93.0% to K562 cells and 98.9% to Molt-4 cells based on the control, meanwhile, 63.47% of K562 cells and 81.15% of Molt-4 cells exhibited late apoptotic and necrotic features with damaged cytoplasmic membrane. 48 h exposure to 10 ttM EMB increased significantly the great number of cells with mitochondrial membrane potential (MMP) loss, and the elevation of [Ca2+](i) level was peaked and persisted within 70 s in K562 cells whilst 50 s in Molt-4 cells. Moreover, a stronger cytotoxicity of EMB was further observed than that of imatinib. The results authenticate the efficacious effect of EMB as a potential anti-leukemia agent and an inconsistency with regard to insecticide-induced leukemia.

The primary objective of recommender systems is to help users select their desired items, where a key challenge is providing high-quality recommendations to users in a "cold-start" situation. Recent advances in tackling this problem combine social relations and temporal information and integrate them into a unified framework. However, these methods suffer from a limitation that there not always exist links for the newcomers, thus these users are filtered in related studies. To break the boundary, in this paper, we propose a Joint Personalized Markov Chains (JPMC) model to address the cold-start issues for implicit feedback recommendation system. In our study, we first utilize user embedding to mine Network Neighbors, so that newcomers without relations can be represented by similar users, then we designed a two-level model based on Markov chains at both user level and user group level respectively to model user preferences dynamically. Experimental results on three real-world datasets have shown that our model can significantly outperform the state-of-the-art models. (c) 2019 Elsevier B.V. All rights reserved.