Giuseppe Familiari

It is known that exposure to heavy metal such as lead (Pb) and cadmium (Cd) has several adverse effects, particularly on the human reproductive system. Pb and Cd have been associated with infertility in both men and women. In pregnant women, they have been associated with spontaneous abortion, preterm birth, and impairment of the development of the fetus. Since these heavy metals come from both natural and anthropogenic activities and their harmful effects have been observed even at low levels of exposure, exposure to them remains a public health issue, especially for the reproductive system. Given this, the present study aimed to investigate the potential reproductive effects of Pb and Cd levels in the follicular fluid (FF) of infertile women and non-smokers exposed to heavy metals for professional reasons or as a result of living in rural areas near landfills and waste disposal areas in order to correlate the intrafollicular presence of these metals with possible alterations in the ultrastructure of human cumulus-oocyte complexes (COCs), which are probably responsible for infertility. Blood and FF metals were measured using atomic absorption spectrometry. COCs corresponding to each FF analyzed were subjected to ultrastructural analyses using transmission electron microscopy. We demonstrated for the first time that intrafollicular levels of Pb (0.66 µg/dL–0.85 µg/dL) and Cd (0.26 µg/L–0.41 µg/L) could be associated with morphological alterations of both the oocyte and cumulus cells’ (CCs) ultrastructure. Since blood Cd levels (0.54 µg/L–1.87 µg/L) were above the current reference values established by the guidelines of the Agency for Toxic Substances and Disease Registry (ATSDR) and the Environmental Protection Agency (EPA) (0.4 µg/L), whereas blood Pb levels (1.28 µg/dL–3.98 µg/dL) were below the ATSDR reference values (≤5 µg/dL), we believe that these alterations could be due especially to Cd, even if we cannot exclude a possible additional effect of Pb. Our results highlighted that oocytes were affected in maturation and quality, whereas CCs showed scarcely active steroidogenic elements. Regressing CCs, with cytoplasmic alterations, were also numerous. According to Cd’s endocrine-disrupting activity, the poor steroidogenic activity of CCs might correlate with delayed oocyte cytoplasmic maturation. So, we conclude that levels of heavy metals in the blood and the FF might negatively affect fertilization, embryo development, and pregnancy, compromising oocyte competence in fertilization both directly and indirectly, impairing CC steroidogenic activity, and inducing CC apoptosis.

Glioblastoma is an aggressive brain tumor with an average life expectancy between 14 and 16 months after diagnosis. The Ki-67 labeling index (LI), a measure of cellular proliferation, is emerging as a prognostic marker in GBM. In this study, we investigated the ultrastructure of glioblastoma tissue from 9 patients with the same molecular profile (adult IDH wild-type glioblastoma, wild-type ATRX, and positive for TP53 expression, GFAP expression, and EGFR overexpression) to find possible ultrastructural features to be used as biomarkers and correlated with the only parameter that differs among our samples, the Ki-67 LI. Our main results were the visualization of the anatomical basis of astrocyte-endothelial cells crosstalk; the ultrastructural in situ imaging of clusters of hyperactivated microglia cells (MsEVs); the ultrastructural in situ imaging of microglia cells storing lipid vesicles (MsLVs); the ultrastructural in situ imaging of neoplastic cells mitophagy (NCsM). The statistical analysis of our data indicated that MsEVs and MsLVs correlate with the Ki-67 LI value. We can thus assume they are good candidates to be considered morphological biomarkers correlating to Ki-67 LI. The role of NCsM instead must be further evaluated. Our study findings demonstrate that by combining ultrastructural characteristics with molecular information, we can discover biomarkers that have the potential to enhance diagnostic precision, aid in treatment decision-making, identify targets for therapy, and enable personalized treatment plans tailored to each patient. However, further research with larger sample sizes is needed to validate these findings and fully utilize the potential of ultrastructural analysis in managing glioblastoma.