Bimetallic nanoparticles offer unique chemical, physical and optical properties that are not available for monometallic nanoparticles. Bimetallic nanoparticles play a major role in various therapeutic, industrial and energy fields. Recently, nanoparticles of Copper/Zinc bimetallic nanoparticles have attracted attention in various fields, especially medicine. In this study, bimetallic CuO/ZnO nanostructures were biosynthesized using plant extracts. The plant-mediated synthesis nanoparticles were characterized by Transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), Field Emission Scanning Electron Microscopy (FESEM) and Energy-Dispersive Spectroscopy (EDAX). The cytotoxicity of plant-mediated synthesis bimetallic nanoparticles and the synergistic effects of these nanoparticles in combination with the anticancer drug doxorubicin on MCF-7 cancer cells were evaluated by MTT assay.

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Ovarian cancer (OCa) is characterized as one of the common reasons for cancer-associated death in women globally. This gynecological disorder is chiefly named the “silent killer” due to lacking an association between disease manifestations in the early stages and OCa. Because of the disease recurrence and resistance to common therapies, discovering an effective therapeutic way against the disease is a challenge. According to documents, some popular herbal formulations, such as curcumin, quercetin, and resveratrol, can serve as an anti-cancer agent through different mechanisms. However, these herbal products may be accompanied by some pharmacological limitations, such as poor bioavailability, instability, and weak water solubility. On the contrary, using nano-based material, e.g., nanoparticles (NPs), micelles, liposomes, can significantly solve these limitations. Therefore, in the present study, we will summarize the anti-cancer aspects of these herbal and-nano-based herbal formulations with a focus on their mechanisms against OCa.

Skin cancer is one of the most common types of cancer in the world, accounting for at least 40% of all cancers. Melanoma is considered as the 19th most commonly occurring cancer among the other cancers in the human society, such that about 300,000 new cases were found in 2018. While cancer diagnosis is based on interventional methods such as surgery, radiotherapy, and chemotherapy, studies show that the use of new computer technologies such as image processing mechanisms in processes related to early diagnosis of this cancer can help the physicians heal this cancer. This paper proposes an automatic method for diagnosis of skin cancer from dermoscopy images. The proposed model is based on an improved XceptionNet, which utilized swish activation function and depthwise separable convolutions. This system shows an improvement in the classification accuracy of the network compared to the original Xception and other dome architectures. Simulations of the proposed method are compared with some other related skin cancer diagnosis state-of-the-art solutions, and the results show that the suggested method achieves higher accuracy compared to the other comparative methods.