Betulinic acid improves TNF-α-induced insulin resistance by inhibiting negative regulator of insulin signalling and inflammation-activated protein kinase in 3T3-L1 adipocytes.

CONTEXT: Obesity is related to insulin resistance, and adipose tissue-secreted TNF-α may play a role in inducing obesity. TNF-α activates inflammatory protein kinase and impairs insulin signalling. OBJECTIVES: We investigated the effect of betulinic acid on insulin resistance caused by TNF-α treatment in 3T3-L1 adipocytes. MATERIAL AND METHODS: 3T3-L1 was exposed to TNF-α in the presence and absence of betulinic acid. Various parameters such as glucose uptake assay, cell viability, expression of proteins involved in insulin resistance were studied. RESULTS: Betulinic acid increased glucose uptake in TNF-α pre-treated cells and inhibited the activation of PTP1B and JNK and reduced IκBα degradation. Tyrosine phosphorylation was increased, and serine phosphorylation was decreased in IRS-1. DISCUSSION: Betulinic acid restored TNF-α impaired insulin signalling and increased PI3K activation and phosphorylation of Akt and increased plasma membrane expression of GLUT 4, which stimulated glucose uptake concentration-dependently. CONCLUSION: These results suggest that betulinic acid is effective at improving TNF-α-induced insulin resistance in adipocytes via inhibiting the activation of negative regulator of insulin signalling and inflammation-activated protein kinase and may potentially improve insulin resistance.

Combining random forest with multi-block local binary pattern feature selection for multiclass head pose estimation.

A new head pose estimation technique based on Random Forest (RF) and texture features for facial image analysis using a monocular camera is proposed in this paper, especially about how to efficiently combine the random forest and the features. In the proposed technique a randomized tree with useful attributes is trained to improve estimation accuracy and tolerance of occlusions and illumination. Specifically, a number of features including Multi-scale Block Local Block Pattern (MB-LBP) are extracted from an image, and random features such as the MB-LBP scale parameters, a block coordinate, and a layer of an image pyramid in the feature pool are used for training the tree. The randomized tree aims to maximize the information gain at each node while random samples traverse the nodes in the tree. To this aim, a split function considering the uniform property of the LBP feature is developed to move sample blocks to the left or the right children nodes. The trees are independently trained with random inputs, yet they are grouped to form a random forest so that the results collected from the trees are used for make the final decision. Precisely, we use a Maximum-A-Posteriori criterion in the decision. It is demonstrated with experimental results that the proposed technique provides significantly enhanced classification performance in the head pose estimation in various conditions of illumination, poses, expressions, and facial occlusions.