Anti-FOS monoclonal antibody, clone 3E23

Nelumbo nucifera, more commonly known as the Indian lotus, is an important plant that has been incorporated into traditional herbal remedies along the years. Even today, lotus leaves are considered reservoirs for bioactive compounds that can be used as nutritional supplements to treat various human diseases. However, despite the wide ranging biological activities of lotus polysaccharides, limited information is available regarding the antiosteoporotic effects of these substances. The aim of this study was to investigate the beneficial effects of pectinase-assisted extractable polysaccharides from lotus leaves (LLEP) on estrogen deficiency-induced bone loss and osteoclast differentiation in bone marrow-derived macrophages. We found that LLEP markedly inhibited receptor activator of the nuclear factor-kappa B ligand (RANKL)-induced osteoclast differentiation in a dose-dependent manner. It also revoked RANKL-induced activation of osteoclastogenic signals such as the expression of key transcription factors (i.e., c-Fos and nuclear factor of activated T cells cytoplasmic 1), resulting in a decrement in osteoclast-specific marker gene expressions. Microcomputed tomography and morphometric analysis revealed that a four-week oral administration of LLEP notably decreased trabecular bone loss. Taken together, our results suggest that LLEP can mitigate estrogen deficiency-induced bone loss by suppressing osteoclastogenesis, which makes it an excellent candidate for combating osteoporosis. (C) 2020 Published by Elsevier B.V.

In this paper, Rayleigh backscattering sensors (RBS) are used to realize shape sensing of beam-like structures. Compared to conventional shape sensing systems based on fibre Bragg grating (FBG) sensors, RBS are capable of continuous lateral sensing. Compared to other types of distributed fibre optic sensors (FOS), RBS have a higher spatial resolution. First, the RBS's strain sensing accuracy is validated by an experiment comparing it with strain gauge response. After that, two shape sensing algorithms (the coordinate transformation method (CTM) and the strain-deflection equation method (SDEM)) based on the distributed FOS' input strain data are derived. The algorithms are then optimized according to the distributed FOS' features, to make it applicable to complex and/or combine loading situations while maintaining high reliability in case of sensing part malfunction. Numerical simulations are carried out to validate the algorithms' accuracy and compare their accuracy. The simulation shows that compared to the FBG-based system, the RBS system has a better performance in configuring the shape when the structure is under complex loading. Finally, a validation experiment is conducted in which the RBS-based shape sensing system is used to configure the shape of a composite cantilever-beam-like specimen under concentrated loading. The result is then compared with the optical camera-measured shape. The experimental results show that both shape sensing algorithms predict the shape with high accuracy comparable with the optical camera result.