Toxicity of Nanoparticles on the Reproductive System in Animal Models: A Review

In the last two decades, nanotechnologies demonstrated various applications in different fields, including detection, sensing, catalysis, electronics, and biomedical sciences. However, public concerns regarding the well-being of human may hinder the wide utilization of this promising innovation. Although, humans are exposed to airborne nanosized particles from an early age, exposure to such particles has risen dramatically within the last century due to anthropogenic sources of nanoparticles. The wide application of nanomaterials in industry, consumer products, and medicine has raised concerns regarding the potential toxicity of nanoparticles in humans. In this review, the effects of nanomaterials on the reproductive system in animal models are discussed. Females are particularly more vulnerable to nanoparticle toxicity, and toxicity in this population may affect reproductivity and fetal development. Moreover, various types of nanoparticles have negative impacts on male germ cells, fetal development, and the female reproductive system. These impacts are associated with nanoparticle modification, composition, concentration, route of administration, and the species of the animal. Therefore, understanding the impacts of nanoparticles on animal growth and reproduction is essential. Many studies have examined the effects of nanoparticles on primary and secondary target organs, with a concentration on the in vivo and in vitro effects of nanoparticles on the male and female reproductive systems at the clinical, cellular, and molecular levels. This review provides important information regarding organism safety and the potential hazards of nanoparticle use and supports the application of nanotechnologies by minimizing the adverse effects of nanoparticles in vulnerable populations

Breast Cancer Detection in the Equivocal Mammograms by AMAN Method

Breast cancer is a primary cause of human deaths among gynecological cancers around the globe. Though it can occur in both genders, it is far more common in women. It is a disease in which the patient’s body cells in the breast start growing abnormally. It has various kinds (e.g., invasive ductal carcinoma, invasive lobular carcinoma, medullary, and mucinous), which depend on which cells in the breast turn into cancer. Traditional manual methods used to detect breast cancer are not only time consuming but may also be expensive due to the shortage of experts, especially in developing countries. To contribute to this concern, this study proposed a cost-effective and efficient scheme called AMAN. It is based on deep learning techniques to diagnose breast cancer in its initial stages using X-ray mammograms. This system classifies breast cancer into two stages. In the first stage, it uses a well-trained deep learning model (Xception) while extracting the most crucial features from the patient’s X-ray mammographs. The Xception is a pertained model that is well retrained by this study on the new breast cancer data using the transfer learning approach. In the second stage, it involves the gradient boost scheme to classify the clinical data using a specified set of characteristics. Notably, the experimental results of the proposed scheme are satisfactory. It attained an accuracy, an area under the curve (AUC), and recall of 87%, 95%, and 86%, respectively, for the mammography classification. For the clinical data classification, it achieved an AUC of 97% and a balanced accuracy of 92%. Following these results, the proposed model can be utilized to detect and classify this disease in the relevant patients with high confidence