Fucoxanthin improves functional recovery of orbitopathy in Graves’ disease by downregulating IL-17 mRNA expression in a mouse model

Purpose: To explore the efficacy of fucoxanthin (FX), a carotenoid, against inflammation via inhibition of IL-17 mRNA expression, and its anti-oxidant activity in Graves’ orbitopathy (GO)-induced mice model.Methods: The effects of FX on IL-6, IL-8, IL-17, MCP-1, and TNF-α, in orbital fibroblast tissues extracted from GO-induced BALB/c mice was  investigated. Anti-oxidative stress markers, 8-hydroxy-2’- deoxyguanosine (8-OHdG) and malondialdehyde (MDA) levels were quantified in tear samples collected from GO-induced FX treated mice.Results: FX administration in cultured human orbital fibroblast cells revealed almost complete cell viability and no cell apoptosis. FX resulted in IL-1β induced Beclin-1 and Atg-5 silencing, in cultured human orbital fibroblasts. BALB/c mice immunized with Ad-TSHR289 indicated elevated levels ofthyroid peroxidase and thyroglobulin antibodies in the serum sample. FX predominantly downregulated the mRNA expression of IL-17, and also reduced increased 8-OHdG and MDA in the tear secretion of GO-induced mice.Conclusion: FX may be an effective and useful molecule for the treatment of GO, through its antiinflammatory and anti-oxidative potential, but it requires further investigation to ascertain its therapeutic effectiveness. Keywords: Anti-inflammatory, Anti-oxidant, Fucoxanthin, Graves’ disease, Graves’ orbitopathy, IL-1

Dynamic characteristic analysis of two-stage quasi-zero stiffness vibration isolation system

A novel two-stage quasi-zero stiffness (QZS) vibration isolator was proposed for the purpose of low-frequency vibration isolation. Firstly, the dynamic model of the vibration isolation system was established; furthermore, the force transmissibility of the system under harmonic force excitation was derived by the averaging method; finally, the effects on the vibration isolation performance caused by excitation amplitude, mass ratio and damping ratio were discussed. Results show that, compared with the corresponding two-stage linear system, two-stage QZS system not only has better isolation performance, but also possesses a wider range of isolation frequency provided that the excitation amplitude, mass ratio and damping ratio is appropriate

Readiness of Malaysian on Sustainable Development in Solar Energy Application

Solar energy is a non-vanishing renewable source of energy that is eco-friendly. However, the solar energy adoption rate in Malaysia remains low due to the low readiness level among the Malaysian. This phenomenon should be emphasized where solutions need to be figured out to encourage the adoption of solar energy in buildings. Thus, this research was conducted to explore the readiness and challenges of the Malaysians in adopting solar energy. A mixed research method was adopted where a total of 200 sets of online questionnaire surveys were distributed to the public, and a semi-structured interview was carried out to obtain the opinions from the expert in the industry. Based on the findings of the questionnaires, the top 3 challenges in solar energy adoption are (1) high setting up cost of solar panels, (2) limited public awareness of solar energy and (3) efficiency of the solar panel depends on the weather. Next, the qualitative study found that 48.9% of 139 respondents were willing to adopt solar energy in the future. The findings show that that breaking down the barriers of high investment cost and long return investment would further enhance the solar energy adoption rates at the residential scale. Since this study had explored the challenges and solution to these barriers, the outcomes of this study can be used by the policy maker as the fundamental to encourage the adoption of solar energy in the existing buildings where the current adoption rate is low

Inpatient care burden due to cancers in Anhui, China: a cross-sectional household survey

Raw dataset of inpatient cancer care costs and related variables studied. (XLSX 32 kb

A Review of Deep Convolutional Neural Networks in Mobile Face Recognition

With the emergence of deep learning, Convolutional Neural Network (CNN) models have been proposed to advance the progress of various applications, including face recognition, object detection, pattern recognition, and number plate recognition. The utilization of CNNs in these areas has considerably improved security and surveillance capabilities by providing automated recognition solutions, such as traffic surveillance, access control devices, biometric security systems, and attendance systems. However, there is still room for improvement in this field. This paper discusses several classic CNN models, such as LeNet-5, AlexNet, VGGNet, GoogLeNet, and ResNet, as well as lightweight models for mobile-based applications, such as MobileNet, ShuffleNet, and EfficientNet. Additionally, deep CNN-based face recognition models, such as DeepFace, DeepID, FaceNet, and SphereFace, are explored, along with their architectural characteristics, advantages, disadvantages, and recognition accuracy. The results indicate that many scholars are researching lightweight face recognition, but applying it to mobile devices is impractical due to high computational costs. Furthermore, noise label learning is not robust in actual scenarios, and unlabeled face learning is expensive in manual labeling. Finally, this paper concludes with a discussion of the current problems faced by face recognition technology and its potential future directions for development