ABSTRACT
Tangeretin (TAN) exhibits many bioactivities, including neuroprotective effects. However, the efficacy of TAN in Alzheimer's disease (AD) has not been sufficiently investigated. In the present study, we integrated behavioral tests, pathology assessment, and biochemical analyses to elucidate the antidementia activity of TAN in APPswe/PSEN1dE9 transgenic (Tg) mice. At supplementation levels of 100 mg/kg body weight per day, TAN significantly attenuated the cognitive impairment of Tg mice in behavioral tests. These effects were associated with less synaptic impairments and fewer ß-amyloid accumulations after TAN administration. Furthermore, our study revealed that TAN possessed powerful inhibitory activity against ß-secretase both in vitro and in vivo, which played a crucial role in the process of Aß generation. These findings indicate that TAN is a potential drug for preventing AD pathology. The key mechanism underlying the antidementia effect of TAN may include its inhibitory activity against ß-secretase.
Subject(s)
Alzheimer Disease , Amyloid Precursor Protein Secretases/antagonists & inhibitors , Aspartic Acid Endopeptidases/antagonists & inhibitors , Cognitive Dysfunction , Flavones/pharmacology , Alzheimer Disease/drug therapy , Alzheimer Disease/genetics , Amyloid Precursor Protein Secretases/genetics , Amyloid beta-Peptides , Amyloid beta-Protein Precursor/genetics , Animals , Aspartic Acid Endopeptidases/genetics , Cognitive Dysfunction/drug therapy , Cognitive Dysfunction/genetics , Disease Models, Animal , Mice , Mice, TransgenicABSTRACT
Real-time sensing and modeling of the human body, especially the hands, is an important research endeavor for various applicative purposes such as in natural human computer interactions. Hand pose estimation is a big academic and technical challenge due to the complex structure and dexterous movement of human hands. Boosted by advancements from both hardware and artificial intelligence, various prototypes of data gloves and computer-vision-based methods have been proposed for accurate and rapid hand pose estimation in recent years. However, existing reviews either focused on data gloves or on vision methods or were even based on a particular type of camera, such as the depth camera. The purpose of this survey is to conduct a comprehensive and timely review of recent research advances in sensor-based hand pose estimation, including wearable and vision-based solutions. Hand kinematic models are firstly discussed. An in-depth review is conducted on data gloves and vision-based sensor systems with corresponding modeling methods. Particularly, this review also discusses deep-learning-based methods, which are very promising in hand pose estimation. Moreover, the advantages and drawbacks of the current hand gesture estimation methods, the applicative scope, and related challenges are also discussed.
