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The skin is the largest organ of the human body, and it is also the one most exposed to external environmental contaminants. The skin is the body's first defense against harmful environmental stimuli, including ultraviolet B (UVB) rays and hazardous chemicals. Therefore, proper care of the skin is required to prevent skin-related diseases and age-related symptoms. In this study, we analyzed anti-aging and anti-oxidative effects of Breynia vitis-idaea ethanol extract (Bv-EE) in human keratinocytes and dermal fibroblasts. The Bv-EE had free radical scavenging activity and decreased the mRNA expression of MMPs and COX-2 in H2O2- or UVB-treated HaCaT cells. The Bv-EE also inhibited AP-1 transcriptional activity and phosphorylation of c-Jun N-terminal kinase, extracellular signal-regulated kinase, and mitogen-activated protein kinase 14 (p38), which are major AP-1 activators upon H2O2 or UVB exposure. Furthermore, the promoter activity and mRNA expression of collagen type I (Col1A1) increased in HDF cells treated with Bv-EE, and Bv-EE recovered the collagen mRNA expression decreased by H2O2 or UVB exposure. These results suggest that Bv-EE has anti-oxidative effects by inhibiting the AP-1 signaling pathway, and shows anti-aging effects by upregulating collagen synthesis. [ABSTRACT FROM AUTHOR]

(1) Background: Callerya atropurpurea is found in Laos, Thailand, and Vietnam. Although the anti-inflammatory action of C. atropurpurea has been investigated, the functions of this plant in allergic responses are not understood. Here, we explored the antiallergic mechanism of C. atropurpurea ethanol extract (Ca-EE) using in vitro assays and an in vivo atopic model. (2) Methods: The constituents of Ca-EE were analyzed using GC/MS. Inhibition of lipoxygenase and β-hexosaminidase activity was examined, and the expression of inflammatory genes was measured by quantitative real-time PCR. The regulatory roles of Ca-EE in IgE/FcεRI signaling were examined by Western blotting. The DNCB-induced atopic dermatitis mouse model was performed with histological analysis. (3) Results: Ca-EE comprised cis-raphasatin, lupeol, some sugars, and fatty acids. In RBL-2H3 cells, treatment with Ca-EE significantly reduced the activities of lipoxygenase and β-hexosaminidase, as well as cytokine gene expression. IgE-mediated signaling was downregulated by blocking Lyn kinases. Moreover, Ca-EE effectively inhibited allergic symptoms in the DNCB-induced atopic dermatitis model without toxicity. (4) Conclusions: Ca-EE displayed antiallergic activities through regulating IgE/Lyn signaling in RBL-2H3 cells and a contact dermatitis model. These results indicate that Ca-EE could be effective for allergic disease treatment. [ABSTRACT FROM AUTHOR]

Muscle atrophy, also known as muscle wasting, is the thinning of muscle mass due to muscle disuse, aging, or diseases such as cancer or neurological problems. Muscle atrophy is closely related to the quality of life and has high morbidity and mortality. However, therapeutic options for muscle atrophy are limited, so studies to develop therapeutic agents for muscle loss are always required. For this study, we investigated how orally administered specific collagen peptides (CP) affect muscle atrophy and elucidated its molecular mechanism using an in vivo model. We treated mice with dexamethasone (DEX) to induce a muscular atrophy phenotype and then administered CP (0.25 and 0.5 g/kg) for four weeks. In a microcomputed tomography analysis, CP (0.5 g/kg) intake significantly increased the volume of calf muscles in mice with DEX-induced muscle atrophy. In addition, the administration of CP (0.25 and 0.5 g/kg) restored the weight of the gluteus maximus and the fiber cross-sectional area (CSA) of the pectoralis major and calf muscles, which were reduced by DEX. CP significantly inhibited the mRNA expression of myostatin and the phosphorylation of Smad2, but it did not affect TGF-β, BDNF, or FNDC5 gene expression. In addition, AKT/mTOR, a central pathway for muscle protein synthesis and related to myostatin signaling, was enhanced in the groups that were administered CP. Finally, CP decreased serum albumin levels and increased TNF-α gene expression. Collectively, our in vivo results demonstrate that CP can alleviate muscle wasting through a multitude of mechanisms. Therefore, we propose CP as a supplement or treatment to prevent muscle atrophy. [ABSTRACT FROM AUTHOR]

Many species in the genus Guettarda are known to exert anti-inflammatory effects and are used as traditional medicinal plants to treat various inflammatory symptoms. However, no studies on the inflammatory activities of Guettarda crispiflora Vahl have been reported. The aim of the study was to investigate in vitro and in vivo the anti-inflammatory effects of a methanol extract of Guettarda crispiflora Vahl (Gc-ME). To determine the anti-inflammatory activity of Gc-ME, lipopolysaccharide (LPS)-, poly(I:C)-, or Pam3CSK4-treated RAW264.7 cells, HCl/EtOH- and LPS-treated mice were employed for in vitro and in vivo tests. LPS-induced nitric oxide production in RAW264.7 cells was determined by Griess assays and cytokine gene expression in LPS-activated RAW264.7 cells, confirmed by RT- and real-time PCR. Transcriptional activation was evaluated by luciferase reporter gene assay. Target protein validation was assessed by Western blot analysis and cellular thermal shift assays (CETSA) with LPS-treated RAW264.7 and gene-transfected HEK293 cells. Using both a HCl/EtOH-induced gastritis model and an LPS-induced lung injury model, inflammatory states were checked by scoring or evaluating gastric lesions, lung edema, and lung histology. Phytochemical fingerprinting of Gc-ME was observed by using liquid chromatography–mass spectrometry. Nitric oxide production induced by LPS and Pam3CSK4 in RAW264.7 cells was revealed to be reduced by Gc-ME. The LPS-induced upregulation of iNOS, COX-2, IL-6, and IL-1β was also suppressed by Gc-ME treatment. Gc-ME downregulated the promotor activities of AP-1 and NF-κB triggered by MyD88- and TRIF induction. Upstream signaling proteins for NF-κB activation, namely, p-p50, p-p65, p-IκBα, and p-Src were all downregulated by Ch-EE. Moreover, Src was revealed to be directly targeted by Gc-ME. This extract, orally treated strongly, attenuated the inflammatory symptoms in HCl/EtOH-treated stomachs and LPS-treated lungs. Therefore, these results strongly imply that Guettarda crispiflora can be developed as a promising anti-inflammatory remedy with Src-suppressive properties. [ABSTRACT FROM AUTHOR]

Grewia tomentosa Juss. is a deciduous shrub that mainly grows in Asia. Despite studies of other Grewia species for treatment of various diseases, Grewia tomentosa Juss. has not been studied as a medicinal herb. This study evaluates the anti-allergic and anti-topic dermatitis activity of Grewia tomentosa Juss. ethanol extract (Gt-EE). The results show that Gt-EE suppressed IgE–antigen-induced β-hexosaminidase release. The mRNA expression of IL-1β, IL-4, IL-5, IL-6, IL-13, TNF-α, MCP-1, and TSLP, which are involved in allergic responses, was inhibited by Gt-EE in IgE-stimulated RBL-2H3 cells. In addition, the phosphorylation of Syk, PLCγ1, PKCδ, PI3K, AKT, NF-κB p65, NF-κB p50, p38, JNK, and ERK1/2 was decreased by Gt-EE in these cells. Gt-EE also showed anti-inflammatory effects in in vivo mouse models. In passive cutaneous anaphylaxis (PCA), a commonly used mouse model, Gt-EE decreased the allergic response, infiltration of mast cells, and mRNA level of IL-4. Furthermore, Gt-EE ameliorated symptoms of DNCB-induced atopic dermatitis (AD). In DNCB-induced AD, Gt-EE suppressed the increase in mast cells, serum IgE level, expression of allergic mediators (IL-1β, IL-4, IL-5, IL-6, TNF-α), and phosphorylation of proteins (IκBα, NF-κB p65, NF-κB p50, p38, JNK, and ERK1/2) implicated in allergic reactions [ABSTRACT FROM AUTHOR]

This article reports the development of a pure deep learning method for segmenting concrete cracks in images. The objectives are to achieve the real-time performance while effectively negating a wide range of various complex backgrounds and crack-like features. To achieve the goals, an original convolutional neural network is proposed. The model consists of standard convolutions, densely connected separable convolution modules, a modified atrous spatial pyramid pooling module, and a decoder module. The semantic damage detection network (SDDNet) is trained on a manually created crack dataset, and the trained network records the mean intersection-over-union of 0.846 on the test set. Each test image is analyzed, and the representative segmentation results are presented. The results show that the SDDNet segments cracks effectively unless the features are too faint. The proposed model is also compared with the most recent models, which show that it returns better evaluation metrics even though its number of parameters is 88 times less than in the compared models. In addition, the model processes in real-time (36 FPS) images at 1025 × 512 pixels, which is 46 times faster than in a recent work. [ABSTRACT FROM AUTHOR]

Many damage detection methods that use data obtained from contact sensors physically attached to structures have been developed. However, damage-sensitive features such as the modal properties of steel and reinforced concrete are sensitive to environmental conditions such as temperature and humidity. These uncertainties are difficult to address with a regression model or any other temperature compensation method, and these uncertainties are the primary causes of false alarms. A vision-based remote sensing system can be an option for addressing some of the challenges inherent in traditional sensing systems because it provides information about structural conditions. Using bolted connections is a common engineering practice, but very few vision-based techniques have been developed for loosened bolt detection. Thus, this article proposes a fully automated vision-based method for detecting loosened civil structural bolts using the Viola–Jones algorithm and support vector machines. Images of bolt connections for training were taken with a smartphone camera. The Viola–Jones algorithm was trained on two datasets of images with and without bolts to localize all the bolts in the images. The localized bolts were automatically cropped and binarized to calculate the bolt head dimensions and the exposed shank length. The calculated features were fed into a support vector machine to generate a decision boundary separating loosened and tight bolts. We tested our method on images taken with a digital single-lens reflex camera. [ABSTRACT FROM AUTHOR]

Abstract: Computer vision‐based techniques were developed to overcome the limitations of visual inspection by trained human resources and to detect structural damage in images remotely, but most methods detect only specific types of damage, such as concrete or steel cracks. To provide quasi real‐time simultaneous detection of multiple types of damages, a Faster Region‐based Convolutional Neural Network (Faster R‐CNN)‐based structural visual inspection method is proposed. To realize this, a database including 2,366 images (with 500 × 375 pixels) labeled for five types of damages—concrete crack, steel corrosion with two levels (medium and high), bolt corrosion, and steel delamination—is developed. Then, the architecture of the Faster R‐CNN is modified, trained, validated, and tested using this database. Results show 90.6%, 83.4%, 82.1%, 98.1%, and 84.7% average precision (AP) ratings for the five damage types, respectively, with a mean AP of 87.8%. The robustness of the trained Faster R‐CNN is evaluated and demonstrated using 11 new 6,000 × 4,000‐pixel images taken of different structures. Its performance is also compared to that of the traditional CNN‐based method. Considering that the proposed method provides a remarkably fast test speed (0.03 seconds per image with 500 × 375 resolution), a framework for quasi real‐time damage detection on video using the trained networks is developed. [ABSTRACT FROM AUTHOR]

A number of image processing techniques (IPTs) have been implemented for detecting civil infrastructure defects to partially replace human-conducted onsite inspections. These IPTs are primarily used to manipulate images to extract defect features, such as cracks in concrete and steel surfaces. However, the extensively varying real-world situations (e.g., lighting and shadow changes) can lead to challenges to the wide adoption of IPTs. To overcome these challenges, this article proposes a vision-based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features. As CNNs are capable of learning image features automatically, the proposed method works without the conjugation of IPTs for extracting features. The designed CNN is trained on 40 K images of 256 × 256 pixel resolutions and, consequently, records with about 98% accuracy. The trained CNN is combined with a sliding window technique to scan any image size larger than 256 × 256 pixel resolutions. The robustness and adaptability of the proposed approach are tested on 55 images of 5,888 × 3,584 pixel resolutions taken from a different structure which is not used for training and validation processes under various conditions (e.g., strong light spot, shadows, and very thin cracks). Comparative studies are conducted to examine the performance of the proposed CNN using traditional Canny and Sobel edge detection methods. The results show that the proposed method shows quite better performances and can indeed find concrete cracks in realistic situations. [ABSTRACT FROM AUTHOR]

The importance of methylation in the tumorigenic responses of nonhistone proteins, such as TP53, PTEN, RB1, AKT, and STAT3, has been emphasized in numerous studies. In parallel, the corresponding nonhistone protein methyltransferases have been acknowledged in the pathophysiology of cancer. Thus, this study aimed to explore the pathological role of a nonhistone methyltransferase in gastric cancer (GC), identify nonhistone substrate protein, and understand the underlying mechanism. Interestingly, among the 24 methyltransferases and methyltransferase family 16 (MTF16) proteins, EEF1AKMT3 (METTL21B) expression was prominently lower in GC tissues than in normal adjacent tissues and was associated with a worse prognosis. In addition, EEF1AKMT3-knockdown induced gastric tumor invasiveness and migration. Through gain and loss-of-function studies, mass spectrometry analysis, RNA-seq, and phospho-antibody array, we identified EEF1AKMT3 as a novel tumor-suppressive methyltransferase that catalyzes the monomethylation of MAP2K7 (MKK7) at K296, thereby decreasing the phosphorylation, ubiquitination, and degradation of TP53. Furthermore, EEF1AKMT3, p-MAP2K7, and TP53 protein levels were positively correlated in GC tissues. Collectively, our results delineate the tumor-suppressive function of the EEF1AKMT3/MAP2K7/TP53 signaling axis and suggest the dysregulation of the signaling axis as potential targeted therapy in GC. [ABSTRACT FROM AUTHOR]