Anthropogenic endocrine-disrupting chemicals (EDCs) can contaminate air, soil, and water. Human exposures to EDCs occur through inhalation, absorption, and ingestion. EDCs act by disrupting various pathways in the endocrine system. When the hypothalamic–pituitary–gonadal (HPG) axis is disrupted by EDCs, there can be effects on fertility in both men and women. Not only can fertility be indirectly affected by EDC disruptions of the HPG axis, but EDCs can also directly affect the menstrual cycle and sperm morphology. In this review, we will discuss the current findings on EDCs that can be inhaled. This review examines effects of exposure to prominent EDCs: brominated and organophosphate flame retardants, diesel exhaust, polycyclic aromatic hydrocarbons, cadmium and lead, TCDD, and polychlorinated biphenyls on fertility through alterations that disrupt the HPG axis and fertility through inhalation. Although the studies included herein include multiple exposure routes, all the studies indicate receptor interactions that can occur from inhalation and the associated effects of all compounds on the HPG axis and subsequent fertility.

The epigenome of an individual can be altered by endogenous hormones, environment, age, diet, and exposure to endocrine disrupting chemicals (EDCs), and the effects of these modifications can be seen across generations. Epigenetic modifications to the genome can alter the phenotype of the individual without altering the DNA sequence itself. Epigenetic modifications include DNA methylation, histone modification, and aberrant microRNA (miRNA) expression; they begin during germ cell development and embryogenesis and continue until death. Hormone modulation occurs during the ageing process due to epigenetic modifications. Maternal overnutrition or undernutrition can affect the epigenome of the fetus, and the effects can be seen throughout life. Furthermore, maternal care during the childhood of the offspring can lead to different phenotypes seen in adulthood. Diseases controlled by the endocrine system, such as obesity and diabetes, as well as infertility in females can be associated with epigenetic changes. Not only can these phenotypes be seen in F1, but also some chemical effects can be passed through the germline and have effects transgenerationally, and the phenotypes are seen in F3. The following literature review expands upon these topics and discusses the state of the science related to epigenetic effects of age, diet, and EDCs on the endocrine system.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 – 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.