Effects of Environmental Endocrine-Disrupting Chemicals on Female Reproductive Health

Pollutants to pathogens: The role of heavy metals in modulating TGF-β signaling and lung cancer risk

Lung cancer is a malignant tumor that develops in the lungs due to the uncontrolled growth of aberrant cells. Heavy metals, such as arsenic, cadmium, mercury, and lead, are metallic elements characterized by their high atomic weights and densities. Anthropogenic activities, such as industrial operations and pollution, have the potential to discharge heavy metals into the environment, hence presenting hazards to ecosystems and human well-being. The TGF-β signalling pathways have a crucial function in controlling several cellular processes, with the ability to both prevent and promote tumor growth. TGF-β regulates cellular responses by interacting in both canonical and non-canonical signalling pathways. Research employing both in vitro and in vivo models has shown that heavy metals may trigger TGF-β signalling via complex molecular pathways. Experiments conducted in a controlled laboratory environment show that heavy metals like cadmium and arsenic may directly bind to TGF-β receptors, leading to alterations in their structure that enable the receptor to be phosphorylated. Activation of this route sets in motion subsequent signalling cascades, most notably the canonical Smad pathway. The development of lung cancer has been linked to heavy metals, which are ubiquitous environmental pollutants. To grasp the underlying processes, it is necessary to comprehend their molecular effect on TGF-β pathways. With a particular emphasis on its consequences for lung cancer, this abstract delves into the complex connection between exposure to heavy metals and the stimulation of TGF-β signalling.

Predicting chemicals' toxicity pathway of female reproductive disorders using AOP7 and deep neural networks

Experimental evidence shows that certain chemicals, particularly endocrine disrupting chemicals, may negatively affect the female reproductive system, thereby lowering women's fertility. However, humans are constantly exposed to a number of different chemicals with limited or no experimental data regarding their effect and the mechanism of action in the female reproductive system. To predict chemical hazards to the female reproductive system, we used a previously defined adverse outcome pathway (AOP) that links activation of the peroxisome proliferator-activated receptor γ to the reproductive toxicity in adult females (AOP7) and the Convolutional Deep Neural Network models that produce meaningful predictions when trained on a significant amount of data. The models trained using CompTox assays with intended molecular and biological targets corresponding to AOP7 achieved high performance (over 90% validation accuracy). The integration of AOP7 and Deep Neural Network identified chemicals that could negatively affect female reproduction through the mechanism described in AOP7. We provide a solution to quickly analyze the data and produce machine learning models to identify potentially active chemicals in the female reproductive system. Although we focused on the female reproductive system, this approach could be valid for a number of other chemicals and AOPs if the right data exist.

Comprehensive Review of Uterine Fibroids: Developmental Origin, Pathogenesis, and Treatment

Uterine fibroids are benign monoclonal neoplasms of the myometrium, representing the most common tumors in women worldwide. To date, no long-term or noninvasive treatment option exists for hormone-dependent uterine fibroids, due to the limited knowledge about the molecular mechanisms underlying the initiation and development of uterine fibroids. This paper comprehensively summarizes the recent research advances on uterine fibroids, focusing on risk factors, development origin, pathogenetic mechanisms, and treatment options. Additionally, we describe the current treatment interventions for uterine fibroids. Finally, future perspectives on uterine fibroids studies are summarized. Deeper mechanistic insights into tumor etiology and the complexity of uterine fibroids can contribute to the progress of newer targeted therapies.