Ovarian cancer (OC) is characterized by silent progression and late-stage diagnosis. It is critical to detect and accurately diagnose the disease early to improve survival rates. Tumor markers have emerged as valuable tools in the diagnosis and management of OC, offering non-invasive and cost-effective options for screening, monitoring, and prognosis. Purpose: This paper explores the diagnostic importance of various tumor markers including CA-125, CA15-3, CA 19-9, HE4,hCG, inhibin, AFP, and LDH, and their impact on disease monitoring and treatment response assessment. Methods: Article searches were performed on PubMed, Scopus, and Google Scholar. Keywords used for the searching process were “Ovarian cancer”, “Cancer biomarkers”, “Early detection”, “Cancer diagnosis”, “CA-125”,“CA 15-3”,“CA 19-9”, “HE4”,“hCG”, “inhibin”, “AFP”, “LDH”, and others. Results: HE4, when combined with CA-125, shows improved sensitivity and specificity, particularly in early-stage detection. Additionally, hCG holds promise as a prognostic marker, aiding treatment response prediction and outcome assessment. Novel markers like microRNAs, DNA methylation patterns, and circulating tumor cells offer potential for enhanced diagnostic accuracy and personalized management. Integrating these markers into a comprehensive panel may improve sensitivity and specificity in ovarian cancer diagnosis. However, careful interpretation of tumor marker results is necessary, considering factors such as age, menopausal status, and comorbidities. Further research is needed to validate and refine diagnostic algorithms, optimizing the clinical significance of tumor markers in ovarian cancer management. In conclusion, tumor markers such as CA-125, CA15-3, CA 19-9, HE4, and hCG provide valuable insights into ovarian cancer diagnosis, monitoring, and prognosis, with the potential to enhance early detection.

Fetal brain is extremely plastic and vulnerable to environmental influences that may have long-term impact on health and development of the offspring. Both the Hypothalamic-Pituitary-Adrenal (HPA) and the Hypothalamic-Pituitary-Thyroid (HPT) axes are involved in stress responses, whereas, their final effectors, the Glucocorticoids (GCs) and the Thyroid Hormones (TH s), mediate several fundamental processes involved in neurodevelopment. The effects of these hormones on brain development are found to be time and dose-dependent. Regarding THs, the developing fetus depends on maternal supply of hormones, especially in the first half of pregnancy. It is acknowledged that inadequate or excess concentrations of both GCs and THs can separately cause abnormalities in the neuronal and glial structures and functions, with subsequent detrimental effects on postnatal neurocognitive function. Studies are focused on the direct impact of maternal stress and GC excess on growth and neurodevelopment of the offspring. Of particular interest, as results from recent literature data, is building understanding on how chronic stress and alterations of the HPA axis interacts and influences HPT axis and TH production. Animal studies have shown that increased GC concentrations related to maternal stress, most likely reduce maternal and thus fetal circulating THs, either directly or through modifications in the expression of placental enzymes responsible for regulating hormone levels in fetal microenvironment. The purpose of this review is to provide an update on data regarding maternal stress and its impact on fetal neurodevelopment, giving particular emphasis in the interaction of two axes and the subsequent thyroid dysfunction resulting from such circumstances.

Preeclampsia is a multisystemic clinical syndrome characterized by the appearance of new-onset hypertension and proteinuria or hypertension and end organ dysfunction even without proteinuria after 20 weeks of pregnancy or postpartum. Residing at the severe end of the spectrum of the hypertensive disorders of pregnancy, preeclampsia occurs in 3 to 8% of pregnancies worldwide and is a major cause of maternal and perinatal morbidity and mortality, accounting for 8–10% of all preterm births. The mechanism whereby preeclampsia increases the risk of the neurodevelopmental, cardiovascular, and metabolic morbidity of the mother’s offspring is not well known, but it is possible that the preeclamptic environment induces epigenetic changes that adversely affect developmental plasticity. These developmental changes are crucial for optimal fetal growth and survival but may lead to an increased risk of chronic morbidity in childhood and even later in life. The aim of this review is to summarize both the short- and long-term effects of preeclampsia on offspring based on the current literature.

Introduction: The identification of pregnant women with Gestational Diabetes Mellitus (GDM) who will require insulin therapy, may modify their management to closer monitoring and probable early interventions. The aim of the study was to develop a predictive model for the necessity of insulin treatment in women with GDM. Materials and methods: This was a prospective cohort study. Data from 775 women diagnosed with GDM per the IADPSG criteria were analyzed using logistic regression and a machine learning algorithm, the Classification and Regression Trees (CART). Potential predictors routinely recorded at follow-up visits were tested and used for the development of the model. The resultant model was externally validated using the data from two different perinatology clinics. Results: Preconceptional maternal BMI and morning fasting blood glucose levels at baseline and at 1 h during an Oral Glucose Tolerance Test (OGTT) were independent significant predictors for the treatment modality of GDM. Baseline blood glucose greater than 98 mg/dl and preconceptional maternal Body Mass Index (BMI) between 26 and 31 kg/height² increased substantially the probability of insulin therapy (odds ratio [OR] 4.04, 95% confidence interval [CI] CI 2.65–6.17 and 2.21, 95%CI 1.42–3.43, respectively). The area under the curve (AUC) for the internal and external validation of the predictive model was 0.74 and 0.77, respectively. Conclusions: A simple model based on maternal characteristics and the values of an OGTT can predict the need for insulin treatment with accuracy. Overweight women with an abnormal baseline blood glucose at OGTT are at high likelihood for insulin treatment. Key message: Fifteen to 30% of women with Gestational Diabetes Mellitus (GDM) require insulin therapy. Overweight women with baseline blood glucose greater than 98 mg/dl at OGTT are at increased risk for insulin treatment and close monitoring and increased physical exercise are required.