Human chorionic gonadotropin (hCG) is produced by the placenta and its roles have been studied for over a century, being the first known pregnancy-related protein. Although its main role is to stimulate the production of progesterone by corpus luteal cells, hCG does not represent just one biologically active molecule, but a group of at least five variants, produced by different cells and each with different functions. The hyperglycosylated variant of hCG (H-hCG) plays a key role in trophoblast invasion, placental development and fetal growth. During trophoblast invasion, H-hCG promotes extravillous cytotrophoblast cells to infiltrate the decidua, and also to colonize and remodel the spiral arteries in to low resistance, larger-diameter vessels. As fetal growth is heavily reliant on nutrient availability, impaired trophoblast invasion and remodeling of the uterine arteries, leads to a defective perfusion of the placenta and fetal growth restriction. Understanding the function of H-hCG in the evolution of the placenta might unveil new ways to manage and treat fetal growth restriction.

Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and those without PPH (control group). The PPH group was further stratified into four classes following the Advanced Trauma Life Support guidelines. Clinical data collected from these patients was included in four machine learning-based algorithms whose predictive performance was assessed. Results: The Naïve Bayes (NB) algorithm exhibited the highest accuracy in predicting PPH, boasting a sensitivity of 96.3% and an accuracy of 98.6%, with a false negative rate of 3.7%. Following closely were the Decision Tree (DT) and Random Forest (RF) algorithms, each achieving sensitivities exceeding 94% with a false negative rate of 5.9%. Regarding severity classification I, the NB and Support Vector Machine (SVM) algorithms demonstrated superior predictive capabilities, achieving a sensitivity of 96.4%, an accuracy of 92.1%, and a false negative rate of 3.6%. The most severe manifestations of HPP were most accurately predicted by the NB algorithm, with a sensitivity of 89.3%, an accuracy of 82.4%, and a false negative rate of 10.7%. Conclusions: The NB algorithm demonstrated the highest accuracy in predicting PPH. A notable discrepancy in algorithm performance was observed between mild and severe forms, with the NB and SVM algorithms displaying superior sensitivity and lower rates of false negatives, particularly for mild forms.

Background/Objectives: The diagnostic performance of CINtec^® PLUS can be influenced by numerous patient characteristics and risk factors. The aim of this retrospective study was to evaluate and model the risk factors associated with CINtec^® PLUS test positivity in patients undergoing cervical cancer screening and to assess their predictive performance for the prediction of cervical intraepithelial neoplasia (CIN) 2/3 using an unsupervised machine learning-based model. Methods: Medical data of 134 patients with human papillomavirus (HPV) infection who underwent CINtec^® PLUS testing were used to model the impact of risk factors on dual-stain cytology positivity and to evaluate the predictive performance for CIN2/3. Results: The gradient boosting classifier for the prediction of CINtec^® PLUS positivity using clinical risk factors had a precision of 75%, an overall accuracy of 0.62, and an area under the curve (AUC) value of 0.77. Body mass index and age were the most important variables in this model. HSIL, ASC-US, and other high-risk HPV strains increased the likelihood of a positive outcome. Overall AUC values for a positive test alone were 0.74 and 0.69 for CIN2 and CIN3 prediction, respectively. For CIN2 prediction, the XGBoost model performed well, with 71% sensitivity, 85% specificity, and an AUC value of 0.90. However, the model had 96% sensitivity, 25% specificity, and 0.58 AUC for CIN3 prediction. Conclusions: Patient characteristics and risk factors can influence CINtec^® PLUS positivity rates and they need to be carefully considered before choosing a specific management.

Background: Premature birth remains a public health problem worldwide, involving a broader context and a multidisciplinary team aimed at combating this phenomenon as much as possible. The consumption of addictive substances by women who are pregnant can occur in different social contexts and at different stages of their lives, which modulate its extent. Obstetricians and anesthetists should consider the anesthetic maternal risks that may arise due to these addictive behaviors. The maternal anesthetic risk is higher in women who are pregnant with a medium-level of education, imbalanced nutrition, stress associated with physical or mental activity, affected sleep hygiene, and failed marriages. Objectives: The objectives of the study refer to analyzing the impact of exogenous factors and the anesthetic risk on premature birth for women who were pregnant during the pandemic period and in women who were pregnant without COVID-19 infection. The authors studied a significant sample of 3588 women who were pregnant without COVID-19 infection, among whom 3291 gave birth at term and 297 gave birth prematurely. Methods: The methods analyzed consist of studying the specialized literature regarding the impact of exogenous factors and parturient’s anesthetic risk on premature birth and identifying the regional risk profile of women who are pregnant in the southeast region of Romania compared to that identified in the specialized literature. In the analytical methods, we used a linear regression to study the incidence of exogenous risk factors on anesthetic risk in women who were pregnant with premature births compared to those with full-term births. Results: The results confirm the significant impact of exogenous factors on anesthetic risk and the significant impact of anesthetic risk on premature births. The novelty of the study lies in highlighting the modification of the regional exogenous risk profile during the pandemic period in southeast Romania due to unfavorable socio-economic causes and the translation of grade I and II prematurity events to higher frequencies with an increased level of maternal anesthetic risk. Conclusions: The study findings show that the anesthetic risk is maximized in parturients with a middle school education. Additionally, the anesthetic risk of patients who are pregnant increases with the intensification of smoking adherence and its maintenance throughout the pregnancy at the same intensity. Our study aims to provide a basis for the diversification and development of community intervention programs in the post-COVID-19 era, considering the reshaping of social models and the repositioning of social principles and values. Obstetricians and anesthetists must know and promote family values to harmonize the lives of family members and provide a better life for the mother and child.

Background and Objectives: Retinopathy of prematurity (ROP) remains a leading cause of preventable childhood blindness, with its severity influenced by a complex interaction between nutritional status, metabolic maturation, and systemic vulnerability. This study aimed to evaluate whether early nutritional patterns and serum metabolic parameters, including hepatic and renal biomarkers, are associated with ROP severity and whether they may serve as potential predictors of disease progression. Materials and Methods: We conducted a retrospective study on 140 preterm infants, totaling 280 eyes, admitted between 2021 and 2024 in two neonatal intensive care units (NICU). Each eye was analyzed independently according to International Classification of Retinopathy of Prematurity (ICROP) criteria. Data on the timing of enteral feeding, duration and type of nutrition, and serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein, blood glucose, urea and creatinine were collected throughout the first 28 days of life. Statistical analysis included Kruskal–Wallis and Chi-square tests, with a significance threshold of p < 0.05. Results: ROP was identified in 53.57% of cases. Enteral feeding began earlier in infants without ROP, whereas delayed initiation and prolonged parenteral nutrition were associated with more advanced stages. Natural feeding decreased with increasing severity and was absent in aggressive retinopathy of prematurity (A-ROP). Severe disease stages showed higher AST, ALT, urea and creatinine levels, along with lower early total protein values. Glycemic instability was observed more frequently in stage 2 and stage 3. Conclusions: Early nutritional support, especially early enteral feeding and natural feeding, appears protective against ROP progression. Hepatic, renal and glycemic metabolic changes are closely correlated with disease severity, indicating that metabolic balance reflects overall vulnerability in preterm infants. Incorporating nutritional and metabolic assessment into routine screening may enhance early risk identification and optimize clinical monitoring.

Background/Objectives: This research investigates the impact of exogenous and endogenous factors on fetal health in pregnant women under the age of 18, with a special focus on the influence of educational level, adherence to vices (smoking and alcohol), comorbidities (diabetes and hypertension), and poor sanitary conditions. Methods: The study uses retrospective data collected from a medical institution in the Southeast region of Romania, including a sample of 3639 births during the post-pandemic period (2022–2023). This period was considered to be a reference period for the study because, as a result of measures to combat the spread of COVID-19 disease in the pandemic, there was an increase in the birth rate among patients under 18 years of age. The APGAR clinical score was evaluated at 5, 10, and 20 min after birth and measured on an inverted scale to reflect the increased risk to fetal health. Results: The results indicate that lack of education is a significant exogenous factor associated with an increased risk of preterm births and a lower APGAR score. Additionally, adherence to vices is more pronounced among pregnant women with low educational levels and smoking and alcohol consumption negatively impact fetal health. Regarding comorbidities, diabetes did not significantly affect the short-term APGAR score, while hypertension had a complex effect, though medical interventions mitigated the associated risks. Conclusions: The conclusions of the research emphasize the need for appropriate educational and medical interventions to reduce the risks associated with preterm births and newborn health in adolescent pregnancies, especially in disadvantaged environments. The study suggests future research directions to expand the analysis to other geographical regions and for long-term monitoring of newborn health.