HAI-178 antibody-conjugated fluorescent magnetic nanoparticles for targeted imaging and simultaneous therapy of gastric cancer

The successful development of safe and highly effective nanoprobes for targeted imaging and simultaneous therapy of in vivo gastric cancer is a great challenge. Herein we reported for the first time that anti-α-subunit of ATP synthase antibody, HAI-178 monoclonal antibody-conjugated fluorescent magnetic nanoparticles, was successfully used for targeted imaging and simultaneous therapy of in vivo gastric cancer. A total of 172 specimens of gastric cancer tissues were collected, and the expression of α-subunit of ATP synthase in gastric cancer tissues was investigated by immunohistochemistry method. Fluorescent magnetic nanoparticles were prepared and conjugated with HAI-178 monoclonal antibody, and the resultant HAI-178 antibody-conjugated fluorescent magnetic nanoparticles (HAI-178-FMNPs) were co-incubated with gastric cancer MGC803 cells and gastric mucous GES-1 cells. Gastric cancer-bearing nude mice models were established, were injected with prepared HAI-178-FMNPs via tail vein, and were imaged by magnetic resonance imaging and small animal fluorescent imaging system. The results showed that the α-subunit of ATP synthase exhibited high expression in 94.7% of the gastric cancer tissues. The prepared HAI-178-FMNPs could target actively MGC803 cells, realized fluorescent imaging and magnetic resonance imaging of in vivo gastric cancer, and actively inhibited growth of gastric cancer cells. In conclusion, HAI-178 antibody-conjugated fluorescent magnetic nanoparticles have a great potential in applications such as targeted imaging and simultaneous therapy of in vivo early gastric cancer cells in the near future

The Correlation between Chemical Composition, as Determined by UPLC-TOF-MS, and Acute Toxicity of Veratrum nigrum

The eighteen incompatible medicaments is an important theory in traditional Chinese medicine. The theory suggests that drugs in the eighteen incompatible medicaments can be toxic when used together. Veratrum nigrum L. and Radix paeoniae alba belong to the eighteen incompatible medicaments and have been prohibited for thousands of years. This study offers preliminary insight into the mechanism and chemical constituents responsible for the incompatibility and toxicity of these two agents. Specifically, we performed toxicology studies to identify and quantify the constituent substances of the two agents. Experiments revealed that acute toxicity increases when the dose of V. nigrum L. is higher than, or equal to, RPA. UPLC-TOF-MS analysis showed that, although the volumes of V. nigrum L. were the same, the content of some veratrum alkaloids changed significantly and had a trend toward a highly positive correlation r≥0.8 with toxicity. This suggests that the increased toxicity of the V. nigrum L. and RPA combination was due mainly to increased content of the special veratrum alkaloids. The cytotoxicity of veratridine in SH-SY5Y cells was decreased with increasing paeoniflorin concentrations. This study provides insight into the mechanism behind the incompatibility theory of TCM

The Bone Marrow Edema Links to an Osteoclastic Environment and Precedes Synovitis During the Development of Collagen Induced Arthritis

Objectives: To determine the relationship between bone marrow edema (BME), synovitis, and bone erosion longitudinally using a collagen induced arthritis mice (CIA) model and to explore the potential pathogenic role of BME in bone erosion.Methods: CIA was induced in DBA/1J mice. BME and corresponding clinical symptoms of arthritis and synovitis during the different time points of CIA development were assayed by magnetic resonance imaging (MRI), arthritis sore, and histologic analyses. The expression of osteoclasts (OCs), OCs-related cytokines, and immune cells in bone marrow were determined by flow cytometry, immunohistochemistry, immunofluorescence staining, and real-time PCR. The OCs formation was estimated using in vitro assays.Results: MRI detected BME could emerge at day 25 in 70% mice after the first immunization (n = 10), when there were not any arthritic symptoms, histological or MRI synovitis. At day 28, BME occurred in 90% mice whereas the arthritic symptom and histological synovitis were only presented in 30 and 20% CIA mice at that time (n = 10). The emergence of BME was associated with an increased bone marrow OCs number and an altered distribution of OCs adherent to subchondral bone surface, which resulted in increased subchondral erosion and decreased trabecular bone number during the CIA process. Obvious marrow environment changes were identified after BME emergence, consisting of multiple OCs related signals, including highly expressed RANKL, increased proinflammatory cytokines and chemokines, and highly activated T cells and monocytes.Conclusions: BME reflects a unique marrow “osteoclastic environment,” preceding the arthritic symptoms and synovitis during the development of CIA

A Crack Identification Method for Concrete Structures Using Improved U-Net Convolutional Neural Networks

The traditional method for detecting cracks in concrete bridges has the disadvantages of low accuracy and weak robustness. Combined with the crack digital image data obtained from bending test of reinforced concrete beams, a crack identification method for concrete structures based on improved U-net convolutional neural networks is proposed to improve the accuracy of crack identification in this article. Firstly, a bending test of concrete beams is conducted to collect crack images. Secondly, datasets of crack images are obtained using the data augmentation technology. Selected cracks are marked. Thirdly, based on the U-net neural networks, an improved inception module and an Atrous Spatial Pyramid Pooling module are added in the improved U-net model. Finally, the widths of cracks are identified using the concrete crack binary images obtained from the improved U-net model. The average precision of the test set of the proposed model is 11.7% higher than that of the U-net neural network segmentation model. The average relative error of the crack width of the proposed model is 13.2%, which is 18.6% less than that measured by using the ACTIS system. The results indicate that the proposed method is accurate, robust, and suitable for crack identification in concrete structures