Flavonoids are a class of plant and fungus secondary metabolites and are the most common group of polyphenolic compounds in the human diet. In recent studies, flavonoids have been shown to induce browning of white adipocytes, increase energy consumption, inhibit high-fat diet (HFD)-induced obesity and improve metabolic status. Promoting the activity of brown adipose tissue (BAT) and inducing white adipose tissue (WAT) browning are promising means to increase energy expenditure and improve glucose and lipid metabolism. This review summarizes recent advances in the knowledge of flavonoid compounds and their metabolites. We searched the following databases for all research related to flavonoids and WAT browning published through March 2019: PubMed, MEDLINE, EMBASE, and the Web of Science. All included studies are summarized and listed in Table 1. We summarized the effects of flavonoids on fat metabolism and the specific underlying mechanisms in sub-categories. Flavonoids activated the sympathetic nervous system (SNS), promoted the release of adrenaline and thyroid hormones to increase thermogenesis and induced WAT browning through the AMPK-PGC-1α/Sirt1 and PPAR signalling pathways. Flavonoids may also promote brown preadipocyte differentiation, inhibit apoptosis and produce inflammatory factors in BAT. Flavonoids induced WAT browning and activated BAT to increase energy consumption and non-shivering thermogenesis, thus inhibiting weight gain and preventing metabolic diseases.

The clinical treatment of degenerative and developmental lumbar spinal stenosis (LSS) is different. Computed tomography (CT) is helpful in distinguishing degenerative and developmental LSS due to its advantage in imaging of osseous and calcified tissues. However, boundaries of the vertebral body, spinal canal and dural sac have low contrast and hard to identify in a CT image, so the diagnosis depends heavily on the knowledge of expert surgeons and radiologists. In this paper, we develop an automatic lumbar spinal CT image segmentation method to assist LSS diagnosis. The main contributions of this paper are the following: 1) a new lumbar spinal CT image dataset is constructed that contains 2393 axial CT images collected from 279 patients, with the ground truth of pixel-level segmentation labels; 2) a dual densely connected U-shaped neural network (DDU-Net) is used to segment the spinal canal, dural sac and vertebral body in an end-to-end manner; 3) DDU-Net is capable of segmenting tissues with large scale-variant, inconspicuous edges (e.g., spinal canal) and extremely small size (e.g., dural sac); and 4) DDU-Net is practical, requiring no image preprocessing such as contrast enhancement, registration and denoising, and the running time reaches 12 FPS. In the experiment, we achieve state-of-the-art performance on the lumbar spinal image segmentation task. We expect that the technique will increase both radiology workflow efficiency and the perceived value of radiology reports for referring clinicians and patients.