Understanding the characteristics of the vaginal microbiota of our patients allows us to carry out both a personalized therapeutic approach and a closer follow-up in those with microbiota susceptible to dysbiosis. This trial pursues the analysis of the vaginal microbiota of premenopausal women and its fluctuations within a four-week follow-up period. Vaginal samples of 76 fertile women were taken at a baseline visit and at a final visit (day 28 ± 5). To perform a phylogenetic study, we employed massive sequencing techniques to detect the 16S rRNA gene of the vaginal microbiota. The most prevalent vaginal microbial community was type I (34.87%), dominated by Lactobacillus crispatus. Vaginal microbial community types II (Lactobacillus gasseri) and V (Lactobacillus jensenii) were underrepresented in our population. When repeating the sampling process four weeks later, 75% of our patients maintained their initial bacterial community. In the follicular phase, the most recurrent microbiota was type III (Lactobacillus iners); in the periovulatory phase, types III and IV (microbial diversity); finally, in the luteal phase, the most frequent type was IV. The most prevalent vaginal bacterial community in our population was dominated by L. crispatus. The vaginal microbiota was resistant to changes in its bacterial community in 75% of our patients, even between consecutive menstrual cycles.

Early- and late-onset preeclampsia (EOPE and LOPE) pose serious maternal-fetal risks, yet non-invasive early prediction remains challenging. In a prospective cohort of 9,586 pregnancies, we analyze trimester-specific plasma cell-free RNA (cfRNA) profiles from 42 EOPE and 43 LOPE cases versus 131 normotensive controls. Organ-specific transcriptomic shifts distinguish EOPE from LOPE. Predictive models based on cfRNA signatures identify EOPE up to 18.0 weeks before clinical onset in the first-trimester (T1) (AUC = 0.88), and 8.5 weeks in the second trimester (T2) (AUC = 0.89). LOPE is predicted 14.9 weeks in advance using T2 data (AUC = 0.90), while T1 performance is lower (AUC = 0.68). External validation confirms robust EOPE prediction (AUC = 0.87 at T1; 0.81 at T2) and acceptable LOPE performance (AUC = 0.63 at T1; AUC = 0.77 at T2). EOPE models are enriched for decidual transcripts, suggesting early maternal involvement; LOPE models reflect broader tissue contributions. These findings offer a path to early, non-invasive, subtype-specific preeclampsia risk stratification and prevention.

Cor triatriatum sinistrum or cor triatriatum sinister is a rare congenital heart disease that accounts for approximately 0.1% of all cardiac abnormalities. It is defined as the presence of an anomalous septum that divides the left atrium into two cavities, and in most cases, it can be asymptomatic or less frequently very severe. A 37-year-old pregnant woman visited our hospital. In the first trimester scan, we detected signs of fluid in the pericardium (pericardial effusion) that reached the atriums. In the third trimester, an anomalous septum in the left atrium suspicious of cor triatriatum sinister was detected. Expectant management was decided, the pregnancy evolved normally and resulted in uncomplicated delivery of a healthy child. The findings in the prenatal scan were confirmed by echocardiography and the diagnosis of cor triatriatum sinister was confirmed. The newborn was asymptomatic at all times. We show expectant management of cor triatriatum sinister and suggest an association between this entity and early pericardial effusion.

Early-onset (EOPE) and late-onset preeclampsia (LOPE) pose significant challenges to maternal and child health, highlighting the need for early, non-invasive risk identification. In this prospective longitudinal study, we followed 9,586 pregnant women, collecting blood samples each trimester: 9-14 weeks (T1), 18-28 weeks (T2), and after 28 weeks or at preeclampsia diagnosis (T3). Plasma cell-free RNA (cfRNA) signatures were analyzed in women who developed EOPE (n=42) or LOPE (n=43) and compared to matched normotensive controls (n=75). Mapping cfRNA origins and performing differential abundance analysis provided insights into multi-organ impacts, revealing distinct transcriptional features of EOPE and LOPE. We developed a first-trimester EOPE predictive model using 36 transcripts, achieving 83% sensitivity, 88% specificity, and an AUC of 0.85, detecting risk 18.0 weeks before onset. A second-trimester model based on 87 cfRNA transcripts, predicted EOPE 8.5 weeks prior to onset with 87% sensitivity, 84% specificity, and an AUC of 0.85. For LOPE model, detecting risk 14.9 weeks before onset, used 92 cfRNAs, with 86% sensitivity, 89% specificity, and an AUC of 0.88. EOPE models were enriched for decidua-associated transcripts, highlighting the maternal involvement in this subtype, while LOPE models showed diverse tissue responses, paving the way for improved subtype differentiation and tailored interventions to mitigate preeclampsia risks.

Background: Early-onset preeclampsia (eoPE), defined as preeclampsia occurring before 34 weeks of gestation, remains one of the leading causes of severe maternal morbidity, iatrogenic prematurity, and maternal mortality worldwide. Its heterogeneous and rapidly evolving clinical course makes the prediction of adverse outcomes particularly challenging. Although machine learning (ML)–based models have shown promise in stratifying eoPE risk and supporting clinical decision-making, robust prospective external validation is still lacking. The present study aims to address this gap through a multicenter validation of three ML models previously developed by our group to: (1) predict maternal complications, (2) estimate the likelihood of delivery within ≤ 7 days of diagnosis, and (3) identify the risk of hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome or placental abruption. Methods: This prospective multicenter cohort study (2024–2027) recruits pregnant women diagnosed with eoPE after informed consent. Clinical, analytical, and ultrasound data, including all variables used in the models, are recorded from early pregnancy to delivery. The first 100 cases will undergo quantitative external validation of three ML models and a semi-quantitative evaluation by clinicians, after which these cases will be used to retrain the models. The updated models will then be prospectively applied to the next 100 participants. Model predictions and physician´s assessment of the clinical utility of such predictions are generated through a secure web application accessible only after delivery. Discussion: This study addresses a critical gap in the prospective validation of ML-based tools for eoPE, emphasizing methodological rigor, data quality, and external validation in a multicenter setting. By integrating quantitative performance assessment with clinician-centered evaluation, the project seeks to ensure both statistical robustness and real-world clinical relevance. If successfully validated, these models could support more individualized and timely decision-making in eoPE management.