Adjunctive chemotherapy with bisphosphonates has been reported to delay bone metastasis and improve overall survival in breast cancer. Aside from its antiresorptive effect, bisphosphonates exhibit antitumor activities, in vitro and in vivo, via several mechanisms, including antiangiogenesis. In this study, we investigated the potential molecular mechanisms underlying the antiangiogenic effect of non-nitrogen-containing and nitrogen-containing bisphosphonates, clodronate and pamidronate, respectively, in insulin-like growth factor (IGF)-1 responsive human breast cancer cells. We tested whether bisphosphonates had any effects on hypoxia-inducible factor (HIF)-1a/vascular endothelial growth factor (VEGF) axis that plays a pivotal role in tumor angiogenesis, and our results showed that both pamidronate and clodronate significantly suppressed IGF-1-induced HIF-1a protein accumulation and VEGF expression in MCF-7 cells. Mechanistically, we found that either pamidronate or clodronate did not affect mRNA expression of HIF-1a, but they apparently promoted the degradation of IGF-1-induced HIF-1a protein. Meanwhile, we found that the presence of pamidronate and clodronate led to a dose-dependent decease in the newly-synthesized HIF-1a protein induced by IGF-1 in breast cancer cells after proteasomal inhibition, thus, indirectly reflecting the inhibition of protein synthesis. In addition, our results indicated that the inhibitory effects of bisphosphonates on the HIF-1a/VEGF axis are associated with the inhibition of the phosphoinositide 3-kinase/AKT/ mammalian target of rapamycin signaling pathways. Consistently, we demonstrated that pamidronate and clodronate functionally abrogated both in vitro and in vivo tumor angiogenesis induced by IGF-1-stimulated MCF-7 cells. These findings have highlighted an important mechanism of the pharmacological action of bisphosphonates in the inhibition of tumor angiogenesis in breast cancer cells.Breast cancer is the leading cancer affecting millions of women worldwide with aggressive osteolytic bone metastases in the advanced diseases 1,2 and longstanding morbidity or skeletal complications, including bone pain, pathological fracture, hypercalcemia, spinal cord or nerve root compression syndrome. 3 Bisphosphonates are synthetic analogs of inorganic pyrophosphate, containing a phosphorus-carbon-phosphorus (P-C-P) backbone and variable side chains that determine the specific potency for inhibition of bone resorption. 4,5 Bisphosphonates that lack a nitrogen functional group in the R2 side chain (such as clodronate) condense with an

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Five Chinese herbal medicines--matrine, oxymatrine, sophoridine, artemisinin, and dihydroartemisinin--were investigated using vibrational circular dichroism (VCD) experiments and density functional theory calculations to extract their stereochemical information. The three matrine-type alkaloids are available from the dry roots of Sophora flavescens and have long been used in various traditional Chinese herbal medicines to combat diseases such as cancer and cardiac arrhythmia. Artemisinin and the related dihydroartemisinin, discovered in 1979 by Professor Youyou Tu, a 2015 Nobel laureate in medicine, are effective drugs for the treatment of malaria. The VCD measurements were carried out in CDCl3 and DMSO-d6, two solvents with different dielectric constants and hydrogen-bonding characteristics. A "clusters-in-a-liquid" approach was used to model both explicit and implicit solvent effects. The studies show that effectively accounting for solvent effects is critical to using IR and VCD spectroscopy to provide unique spectroscopic features to differentiate the potential stereoisomers of these Chinese herbal medicines.

BackgroundCinobufacin injection, also known as huachansu, is a preparation form of Cinobufacini made from Cinobufacin extract liquid. Despite that Cinobufacin injection is shown to shrink liver and gastric tumors, improving patient survival and life quality, the effective components in Cinobufacin remain elusive. In this study, we aim to screen antitumor components from Cinobufacin injection to elucidate the most effective antitumor components for treatment of liver and gastric cancers.Materials and MethodsHigh performance liquid chromatography (HPLC) and LC-MS/MS analysis were used to separate and determine the components in Cinobufacin injection. Inhibition rates of various components in Cinobufacin injection on liver and gastric cancer cells were determined with MTT assay; Hepatocellular carcinoma and gastric cancer models were used to assess the antitumor effect of the compounds in vivo.ResultsThe major constituents in Cinobufacin injection include peptides, nucleic acids, tryptamines and bufotalins. MTT assay revealed that bufadienolides had the best antitumor activity, with peptides being the second most effective components. Bufadienolides showed significant inhibition rates on gastric and hepatocellular tumour growth in vivo.ConclusionBufadienolides are the most effective components in Cinobufacini injection for the treatment of liver and gastric cancers. This discovery can greatly facilitate further research in improving the therapeutic effects of Cinobufacin injection, meanwhile reducing its adverse reaction.

Bax inhibitor-1 (BI-1), a newly identified apoptosis inhibitor, has recently been found to be overexpressed in several human carcinomas and its specific down-regulation by RNA interference (RNAi) could lead to cell death. The purpose of this study is to investigate the role of BI-1 in apoptosis-resistance and the underlying mechanisms in human nasopharyngeal carcinoma (NPC) cells. Our results showed that BI-1 was expressed in two different human NPC cell lines, CNE-2Z and CNE-1, and specific inhibition of BI-1 expression by siRNA caused a significant increase in spontaneous apoptosis in both cell lines. Mechanistically, we demonstrated that down-regulation of BI-1 protein expression decreased the ratio of Bcl-X(L)/Bcl-2 with Bax protein as determined by Western blot and increased the activity of caspase-3 by colorimetric analysis, thus leading to the activation of the associated cell death pathways. Taken together, these results have provided evidence that BI-1 could serve as an important molecular target gene for the development of new therapeutic strategy against human NPCs.

The accurate and automatic detection of pavement cracks is essential for pavement maintenance. However, automatic crack detection remains a challenging problem due to the inconspicuous visual features of cracks in complex pavement backgrounds, the complicated shapes and structures of cracks, and the influences of weather changes and noise. In recent years, with the development of artificial intelligence technology, crack detection methods based on classification and semantic segmentation have laid a good foundation for the automation of pavement crack detection. However, there remain shortcomings in the comprehensive acquisition of pavement crack attribute information and detection accuracy. To solve these problems, this paper proposes an instance segmentation network for pavement crack detection. The network can simultaneously obtain the crack category, position, and mask, and can realize end-to-end pixel-level crack detection. A semantic segmentation branch is first added to Mask R-CNN. This branch can extract the bottom-level detail information of the cracks and ultimately improves the accuracy of crack mask prediction. An adaptive feature fusion module is then designed. During feature fusion, this module highlights the attribute information and location information of cracks according to the channel attention mechanism and the spatial attention mechanism. Finally, these two modules are integrated to form an automatic pixel-level crack detection network, namely APLCNet. Without any embellishment, APLCNet achieves a precision of 92.21%, a recall of 94.89%, and an F1-score of 93.53% on the challenging public CFD dataset, thereby outperforming CrackForest and MFCD for pixel-wise crack detection. Moreover, APLCNet achieves a 16.5% mask AP on the self-captured GDPH dataset, thereby surpassing Mask R-CNN and PANet.

A practical face recognition system demands not only high recognition performance, but also the capability of detecting spoofing attacks. While emerging approaches of face anti-spoofing have been proposed in recent years, most of them do not generalize well to new database. The generalization ability of face anti-spoofing needs to be significantly improved before they can be adopted by practical application systems. The main reason for the poor generalization of current approaches is the variety of materials among the spoofing devices. As the attacks are produced by putting a spoofing display (e.t., paper, electronic screen, forged mask) in front of a camera, the variety of spoofing materials can make the spoofing attacks quite different. Furthermore, the background/lighting condition of a new environment can make both the real accesses and spoofing attacks different. Another reason for the poor generalization is that limited labeled data is available for training in face anti-spoofing. In this paper, we focus on improving the generalization ability across different kinds of datasets. We propose a CNN framework using sparsely labeled data from the target domain to learn features that are invariant across domains for face anti-spoofing. Experiments on public-domain face spoofing databases show that the proposed method significantly improve the cross-dataset testing performance only with a small number of labeled samples from the target domain.