Transferrin Human ELISA Kit

We recently reported that loss of calcium/calmodulin-dependent protein kinase kinase-2 (CAMKK2), a serine/threonine kinase activated by intracellular calcium, in mice leads to tissue-specific aberrant turnover of transferrin (TF), a receptor-mediated iron-transporter that supplies iron to tissues. Iron dyshomeostasis is associated with the pathogenesis of several diseases, making TF transport relevant to health. In this study, we used hemizygous CAMKK2 hypomorphic human endothelial cell (EA.hy926) clones to demonstrate that cells with reduced CAMKK2 exhibit increased TF uptake and transcytosis, and decreased intracellular trafficking to subcellular organelles compared to wild-type. The abnormal TF trafficking in CAMKK2 hypomorphic cells correlated with a reduction in intracellular iron content and defective glucose metabolism including glycolysis and mitochondrial respiration. CAMKK2 deficiency also caused reduction in GAPDH and VDAC1 protein level which correlated to defective bioenergetics function. These findings have identified a novel mechanistic link between abnormal calcium signaling, iron dyshomeostasis and metabolic dysfunction involving CAMKK2.

Finding similar code snippets is a fundamental task in the field of software engineering. Several approaches have been proposed for this task by using statistical language model which focuses on syntax and structure of codes rather than deep semantic information underlying codes. In this paper, a Siamese Neural Network is proposed that maps codes into continuous space vectors and try to capture their semantic meaning. Firstly, an unsupervised pre-trained method that models code snippets as a weighted series of word vectors. The weights of the series are fitted by the Term Frequency-Inverse Document Frequency (TF-IDF). Then, a Siamese Neural Network trained model is constructed to learn semantic vector representation of code snippets. Finally, the cosine similarity is provided to measure the similarity score between pairs of code snippets. Moreover, we have implemented our approach on a dataset of functionally similar code. The experimental results show that our method improves some performance over single word embedding method.