Pear juice concentrate prepared by boiling Japanese pear (Pyrus pyrifolia Nakai cv. Nijisseiki) juice can significantly inhibit the activity of tyrosinase, a key enzyme in melanin synthesis in human skin. Using the ethanol extract of pear juice concentrate, we homogeneously purified an active compound that was identified as 5-hydroxymethyl-2-furaldehyde (5-HMF) through 1H- and 13C-NMR and mass spectroscopy. We observed that 5-HMF inhibited the monophenolase and diphenolase activities of mushroom tyrosinase as a mixed-type inhibitor (Ki values of 3.81 and 3.70 mmol/L, respectively). In B16 mouse melanoma cells, treatment with 170 µmol/L of 5-HMF significantly reduced α-melanocyte-stimulated melanin synthesis by suppressing the cyclic adenosine monophosphate-dependent signaling pathway involved in melanogenesis. The results of our study indicated that 5-HMF can be potentially used as a skin-lightening agent in the cosmetic industry.
Abbreviations: AC: adenylate cyclase; CREB: cAMP response element-binding protein; dhFAME: S-(−)-10,11-Dihydroxyfarnesoic acid methyl ester; DMEM: dulbecco′s modified eagle medium; l-DOPA: 3-(3,4-Dihydroxyphenyl)- l-alanine; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; HEPES: 4-(2-Hydroxyethyl)-1-piperazine ethane sulfonic acid; 5-HMF: 5-Hydroxymethyl-2-furaldehyde; MITF: microphthalmia-associated transcription factor; α-MSH: α-Melanocyte-stimulating hormone; PKA: protein kinase A; PVDF: polyvinylidene difluoride; SDS: sodium dodecyl sulfate; TRP1: tyrosinase-related protein 1; TRP2: tyrosinase-related protein 2
Green wall plants have various functionality such as mitigating heat island, creating green space, and absorbing carbon in an urban area. A method to evaluate the various functionality is needed for construction field. This study focuses on the plant structure and deep learning was introduced to estimate the leaf area and counting leaves automatically. To achieve this, the panorama distant image was created from 3D data acquired by terrestrial laser to prepare for the deep learning model. The model for the presence or absence of leaves had 90% accuracy and the counting leaves had 72% accuracy and leaf area estimated through deep learning had 27% error compared to the destructively sampled data.
Recognition-basis odor concentration measurement method (RO cm method), which is superior to odor intensity reduction measurement method (OIRM method) in objectivity, was proposed to measure sensory deodorizing effect. This study, in order to evaluate the usefulness of the RO cm method, the comparison of deodorization effect of several deodorants was confirmed, and the usefulness as an evaluation method of the deodorization effect was evaluated in comparison with the OIRM method. As the result, the RO cm method allowed the identification of odor index difference of half of odor index even in the presence of fragrance, which is equivalent to the odor intensity difference of 1. In the RO cm method, the significant difference was shown on the difference between 2 fragrances in which the OIRM method could not show the difference of the sensory deodorizing effect. This result agrees with the evaluation result got from the general consumer answer.
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