H inokitiol purified from the heartwood of cupressaceous plants has had various biological functions of cell differentiation and growth. Hinokitiol has been demonstrated as having an important role in anti-inflammation and anti-bacteria effect, suggesting that it is potentially useful in therapies for hyperpigmentation. Previously, hinokitiol inhibited the production of melanin by inhibiting tyrosinase activity. The autophagic signaling pathway can induce hypopigmentation. This study is warranted to investigate the mechanism of hinokitiol-induced hypopigmentation through autophagy in B16F10 melanoma cells. The melanin contents and expression of microthphalmia associated transcription factor (MITF) and tyrosinase were inhibited by treatment with hinokitiol. Moreover, the phosphorylation of the protein express levels of phospho-protein kinase B (P-AKT) and phospho-mammalian targets of rapamycin (P-mTOR) were reduced after hinokitiol treatment. In addition, the microtubule associated protein 1 light chain 3 (LC3) -II and beclin 1 (autophagic markers) were increased after the B16F10 cell was treated with hinokitiol. Meanwhile, hinokitiol decreased cellular melanin contents in a dose-dependent manner. These findings establish that hinokitiol inhibited melanogenesis through the AKT/mTOR signaling pathway.

Ubiquitous health management (UHM) is vital in the aging society. The UHM services with artificial intelligence of things (AIoT) can assist home-isolated healthcare in tracking rehabilitation exercises for clinical diagnosis. This study combined a personalized rehabilitation recognition (PRR) system with the AIoT for the UHM of lower-limb rehabilitation exercises. The three-tier infrastructure integrated the recognition pattern bank with the sensor, network, and application layers. The wearable sensor collected and uploaded the rehab data to the network layer for AI-based modeling, including the data preprocessing, featuring, machine learning (ML), and evaluation, to build the recognition pattern. We employed the SVM and ANFIS methods in the ML process to evaluate 63 features in the time and frequency domains for multiclass recognition. The Hilbert-Huang transform (HHT) process was applied to derive the frequency-domain features. As a result, the patterns combining the time- and frequency-domain features, such as relative motion angles in y- and z-axis, and the HHT-based frequency and energy, could achieve successful recognition. Finally, the suggestive patterns stored in the AIoT-PRR system enabled the ML models for intelligent computation. The PRR system can incorporate the proposed modeling with the UHM service to track the rehabilitation program in the future.