CaSR Induces Osteoclast Differentiation and Promotes Bone Metastasis in Lung Adenocarcinoma

Objective: Explore the mechanism of CaSR's involvement in bone metastasis in lung adenocarcinoma. Methods: Immunohistochemistry (IHC) was used to detect the expression of calcium-sensing receptor (CaSR) in 120 cases of lung adenocarcinoma with bone metastasis. Stably transfected cell lines with CaSR overexpression and knockdown based on A549 cells were constructed. The expression of CaSR was verified by western blot and qPCR. The proliferation and migration abilities of A549 cells were tested using cholecystokinin-8 (CCK-8) and Transwell assays, respectively. Western blotting was used to detect the expression of matrix metalloproteinases MMP2, MMP9, CaSR, and NF-κB. The supernatant from each cell culture group was collected as a conditional co-culture solution to study the induction of osteoclast precursor cells and osteoblasts. Western blot and qPCR were used to validate the expression of bone matrix degradation-related enzymes cathepsin K and hormone calcitonin receptor (CTR) and osteoblast-induced osteoclast maturation and differentiation enzyme receptor activator of nuclear factor-κB ligand (RANKL), macrophage colony-stimulating factor (M-CSF), osteoprotegerin (OPG), and PTHrP. Immunofluorescent staining was used to detect F-actin ring formation and osteocalcin expression. Western blot results for NF-κB expression identified a regulatory relationship between NF-κB and CaSR. Results: CaSR expression in lung cancer tissues was significantly higher than that in adjacent and normal lung tissues. The expression of CaSR in lung cancer tissues with bone metastasis was higher than that in non-metastatic lung cancer tissues. The proliferation and migration ability of A549 cells increased significantly with overexpressed CaSR. The co-culture solution directly induced osteoclast precursor cells and the expression of bone matrix degradation-related enzymes significantly increased. Osteoblasts were significantly inhibited and osteoblast-induced osteoclast maturation and differentiation enzymes were significantly downregulated. It was found that the expression of NF-κB and PTHrP increased when CaSR was overexpressed. Osteoclast differentiation factor expression was also significantly increased, which directly induces osteoclast differentiation and maturation. These results were reversed when CaSR was knocked down. Conclusions: CaSR can positively regulate NF-κB and PTHrP expression in A549 cells with a high metastatic potential, thereby promoting osteoclast differentiation and maturation, and facilitating the occurrence and development of bone metastasis in lung adenocarcinoma

Road layout understanding by generative adversarial inpainting

Autonomous driving is becoming a reality, yet vehicles still need to rely on complex sensor fusion to understand the scene they act in. The ability to discern static environment and dynamic entities provides a comprehension of the road layout that poses constraints to the reasoning process about moving objects. We pursue this through a GAN-based semantic segmentation inpainting model to remove all dynamic objects from the scene and focus on understanding its static components such as streets, sidewalks and buildings. We evaluate this task on the Cityscapes dataset and on a novel synthetically generated dataset obtained with the CARLA simulator and specifically designed to quantitatively evaluate semantic segmentation inpaintings. We compare our methods with a variety of baselines working both in the RGB and segmentation domains

Cationic lipid-assisted nanoparticles for simultaneous delivery of CD47 siRNA and R848 to promote antitumor immune responses

Introduction: Triple-negative breast cancer (TNBC) usually has a poor prognosis. Although the immunotherapy of TNBC has achieved great progress, only a few patients can benefit from the current treatment. CD47 is widely expressed on the surface of TNBC cells and may become an immune checkpoint for TNBC treatment. Nevertheless, increasingly more attention has been paid to systemic side effects since the ubiquitous expression of CD47 on normal cells. The toll-like receptor (TLR) agonist resiquimod (R848) can activate dendritic cells (DCs) and promote the maturation of immune cells in the tumor microenvironment, which further enhances the tumor inhibition ability of the immune system and synergizes with CD47 small interfering RNA (siRNA) for TNBC therapy. However, ideal delivery platforms such as nanocarriers are still needed because its weakness of hydrophobicity.Methods: In order to improve efficacy and reduce toxicity, R848 and siCD47 were entrapped in amphiphilic PEG-PLGA nanoparticles by double emulsification and stable nanoparticles NP/R848/siCD47 were generated to investigate their anti-tumor effects in a TNBC tumor-bearing mouse model.Results: Here, we show that PEG-PLGA nanoparticles are effective nanocarriers that can safely and effectively deliver siCD47 and R848 to tumor tissue, as demonstrated by retarded tumor growth. Mechanistically, downregulation of CD47 expression and activation of DCs took part in promoting the immune response of cytotoxic T cells (CTLs). Meanwhile, a decrease of myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) indicated the modulating of the tumor immune microenvironment.Discussion: To our best knowledge, our study pioneered co-delivery system for hydrophilic siCD47 and hydrophobic R848. It can maximize break tumor immune escape caused by CD47 and simultaneously enhance antigen presentation by activating DCs for effector T cell killing while regulating the tumor microenvironment as expected. Not only does it conform to the reports of previous basic research, but also it can break the bottleneck of their clinical application hopefully. Collectively, our findings could lay the foundation for future therapeutic strategies of TNBC

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed

The Human Activity Radar Challenge: benchmarking based on the ‘Radar signatures of human activities’ dataset from Glasgow University

Radar is an extremely valuable sensing technology for detecting moving targets and measuring their range, velocity, and angular positions. When people are monitored at home, radar is more likely to be accepted by end-users, as they already use WiFi, is perceived as privacy-preserving compared to cameras, and does not require user compliance as wearable sensors do. Furthermore, it is not affected by lighting condi-tions nor requires artificial lights that could cause discomfort in the home environment. So, radar-based human activities classification in the context of assisted living can empower an aging society to live at home independently longer. However, challenges remain as to the formulation of the most effective algorithms for radar-based human activities classification and their validation. To promote the exploration and cross-evaluation of different algorithms, our dataset released in 2019 was used to benchmark various classification approaches. The challenge was open from February 2020 to December 2020. A total of 23 organizations worldwide, forming 12 teams from academia and industry, participated in the inaugural Radar Challenge, and submitted 188 valid entries to the challenge. This paper presents an overview and evaluation of the approaches used for all primary contributions in this inaugural challenge. The proposed algorithms are summarized, and the main parameters affecting their performances are analyzed