Reproductive toxicants in the environment and their role in some human diseases (Сlinic)

In recent decades, there has been a fairly active debate on the role of genetic predisposition, average factors, adverse effects of reproductive toxins, infections, special behaviour that includes harmful habits for human diseases and not to implement their reproductive plans. Environmental disasters, the Covid-19 pandemic, the incorporation of the modern human and industrial waste, forest fires, excessive radiation following the national tragedy of Chernobyl has become global, not only the age of dependent diseases, but the disorders of the main functional systems of the female and male organism that result in reproductive function, reduce the quality of life and duration. Unfavourable medium effects, disturb the process of interacting with the genotype of the organism in shaping the phenotype. This information is needed by doctors to develop periconceptology, which is the task of this clinical lecture.

Cellular and molecular features of endometrial hyperplasia under the influence of industrial toxic factors

The features of biomechanisms of endometrial hyperplasia in subjects exposed to reproductive toxicants were inflammation and oxidative stress. An association of Ki67 expression with 8-hydroxydeoxyguanosine, length of service, CD34 expression with 8-isoprostane and both Ki67 and CD34 expression with transforming growth factor B1 and lead exposure established.

Automated identification of multinucleated germ cells with U-Net

Phthalic acid esters (phthalates) are male reproductive toxicants, which exert their most potent toxicity during a critical window of sensitivity in fetal development. In the fetal rat, exposure to phthalates reduces testosterone biosynthesis, alters the development of seminiferous cords and other male reproductive tissues, and induces the formation of abnormal multinucleated germ cells (MNGs). Identification of MNGs is a time-intensive process, and it requires specialized training to identify MNGs in histological sections. As a result, MNGs are not routinely quantified in phthalate toxicity experiments. In order to speed up and standardize this process, we have developed an improved method for automated detection of MNGs. Using hand-labeled histological section images with human-identified MNGs, we trained a convolutional neural network with a U-Net architecture to identify MNGs on unlabeled images. With unseen hand-labeled images not used in model training, we assessed the performance of the model, using five different configurations of the data. On average, the model reached near human accuracy, and in the best model, it exceeded it. The use of automated image analysis will allow data on this histopathological endpoint to be more readily collected for analysis of phthalate toxicity. Our trained model application code is available for download at github.com/brown-ccv/mngcount.