Search Results (4)

This study aims to provide a comprehensive evaluation of the bioactive compounds present in honeysuckle flower (Lonicera japonica Thunb.) extract (HSF) and their remarkable antioxidant activity. A docking simulation was performed to clarify the binding affinities of the identified phytochemicals to enzymes associated with anti-aging and anti-inflammatory activities. In addition, the low-energy nanoemulsions based on optimally formulated polyglycerol fatty acid esters (PGFEs), developed through D-optimality, were designed for the incorporation of HSF extract. The result revealed that HSF is a rich source of diverse phenolic and flavonoid compounds that contribute to its remarkable antioxidant capacity. Molecular docking analysis indicates that its compounds exhibit anti-aging and anti-inflammatory activities, particularly through collagenase, hyaluronidase, and TNF-α inhibition. Furthermore, D-optimality revealed that HSF-loaded nanoemulsions can be fabricated by a surfactant to oil ratio (SOR) of 2:1 with a ratio of low hydrophilic-lipophilic balance (HLB) surfactant to high HLB surfactant (LHR) of 1:2. Polyglyceryl-6 laurate as a high HLB surfactant produced the optimal nanoemulsion with small particle size and possessed an encapsulation efficiency (EE) of 74.32 ± 0.19%. This is the first report to combine D-optimal design-based nanoemulsion development with a multi-level analysis of HSF, including phytochemical profiling, antioxidant evaluation, and in silico molecular docking. These findings highlight that HSF-loaded polyglycerol fatty acid ester-based nanoemulsions could be a skin health-promoting ingredient and effective alternative for a variety of skincare applications. Full article

Although polyethylene glycol (PEG)-based surfactants are widely used in various industries due to their wide range of hydrophile–lipophile balance (HLB) values, their possible by-product, 1,4-dioxane, has been listed as a reasonably anticipated human carcinogen, which may limit their applications in fields closely related to the human body. Polyglycerol fatty acid esters (PGFEs), a class of surfactants based on polyglycerol (another polyether), also have a wide range of HLB values that can be adjust by varying the degree of polymerization of the polyglycerol, the length of the fatty acid carbon chain, or the degree of esterification, but do not have the risks caused by 1,4-dioxane. In addition, all the raw materials (glycerol and fatty acids) required for the preparation of PGFEs can be obtained via hydrolysis of renewable vegetable oils. Therefore, PGFEs, as new eco-friendly and biodegradable non-ionic surfactants, have been proposed as potential green alternatives to PEG-based non-ionic surfactants. This review summarizes the properties of PGFEs specifically including their HLB, surface properties, phase behaviors, stabilizing effect on foams and emulsions, and stability, and highlights their potential applications in food, cosmetics, and pharmaceuticals observed in the last decade. Full article

Periodontitis is an infectious inflammatory disease of tissues around teeth that destroys connective tissues and is characterized by the loss of periodontal ligaments and alveolar bone. A new treatment strategy is needed owing to the limitations of the current surgical treatment method and the side effects of anti-inflammatory drugs. Therefore, here, we assessed whether Panax ginseng fruit extract (PGFE) is a new therapeutic agent for periodontitis in vitro and in vivo. According to the results, PGFE suppressed pro-inflammatory cytokines such as tumor necrosis factor-α, interleukin (IL)-1β, and IL-6, and pro-inflammatory mediators such as inducible nitric oxide synthase and cyclooxygenase-2 through heme oxygenase-1 expression in human periodontal ligament cells stimulated with Porphyromonas gingivalis lipopolysaccharide (PG-LPS). In addition, the osteogenic induction of human periodontal ligament cells was inhibited by PG-LPS, and protein and mRNA levels of osteogenic markers such as alkaline phosphatase, collagen type 1 (COL1), osteopontin (OPN), and runt-related transcription factor 2 (RUNX2) were increased. The efficacy of PGFE for inhibiting periodontitis in vitro was demonstrated in a representative in vitro model of periodontitis induced by ligature and PG-LPS. Subsequently, hematoxylin and eosin staining and micro-computed tomography of the euthanized experimental animal model confirmed suppressed periodontal inflammation, which is an important strategy for treating periodontitis and for recovering the resulting alveolar bone loss. Therefore, PGFE is a potential, novel therapeutic agent for periodontal diseases. Full article

Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach. Full article