Placenta and Reproductive Medicine

Placental histopathology according to amsterdam criteria and correlation with maternal and neonatal outcomes: A case-control study

2023-02-15T21:20:12+08:00

Objective: To assess the placental histopathology (as per the Amsterdam consensus criteria) with fetal placental Doppler and maternal and fetal outcomes in subjects with pre-eclampsia (PE) with severe features versus normotensive controlgroup. Methods: A case control study was conducted over a period of 1 year at Medical college, Baroda which evaluated 40 women who were divided into case group (20 PE with severe features) and control group (20 normotensives) after matching chronological age, gestational age and parity. Parametric tests such as independent t-test and sensitivity, specificity and odds ratio were calculated. Results: Placentae of subjects with PE with severe features were significantly small and having less weight than control group. Mean thickness of umbilical cord (UC) in case group was 0.70±0.23 cm and mean thickness in control group was 0.96±0.10 cm (P value < 0.0001). No significant difference was seen in Feto placental ratio. Infarction was present in 65% of case group and 5% of control group. Decidual vasculopathy was seen in 90% of case group and was not at all seen in control group. Increased syncytial knots were seen in 100% of case group and 65% of control group (P = 0.0040). Vascular sclerosis and stromal fibrosis were seen more in case group than in control group (P = 0.0196 and < 0.0001 respectively). Conclusion: The Amsterdam classification was useful in delineating histopathological findings such as syncytial knots, infraction, decidual vasculopathy and villous stromal vascular karyorrhexis which were found to correlate with abnormal Doppler velocimetry and adverse maternal and neonatal outcomes. However, it was found to be tedious and time consuming.

Effect of variable selection strategy on the predictive models for adverse pregnancy outcomes of pre-eclampsia: A retrospective study

2023-01-23T19:06:02+08:00

Objectives: The improvement of prediction for adverse pregnancy outcomes is quite essential to the women suffering from pre-eclampsia, while the collection of predictive indicators is the prerequisite. The traditional knowledge-based strategy for variable selection confronts challenge referring to dataset with high-dimensional or unfamiliar data. In this study, we employed five different automatic variable selection methods to screen out influential indicators, and evaluated the performance of constructed predictive models. Methods: Seven hundreds and thirty-three Han-Chinese women were enrolled and 56 clinical and laboratory variables were recorded. After grouping based on binary pregnancy outcomes, statistical description and analysis were performed. Then, utilizing forward stepwise logistic regression (FSLR) as the reference method, another four variable selection strategies were included for filtering contributing variables as the predictive subsets, respectively. Finally, the logistic regression prediction models were constructed by the five subsets and evaluated by the receiver operator characteristic curve. Results: The variables confirmed statistical significance between the adverse and satisfactory outcomes groups did not overlap with the variables selected by selection strategies. “Platelet” and “Creatinine clearance rate” were the most influential indicator to predict adverse maternal outcome, while “Birth weight of neonates” was the best indicator for predicting adverse neonatal outcome. In average, the predictive models for neonatal outcomes achieved better performance than models for maternal outcomes. “Mutual information” and “Recursive feature elimination” were the best strategy under current dataset and study design. Conclusions: Variable selection strategies may provide an alternative approach besides picking influential indicators by statistical significance. Future work will focus on applying different variable selection methods to the high-dimensional dataset, which includes novel or unfamiliar variables. This aims to identify the most appropriate collection of predictors that can enhance prediction ability and clinical decision-making.

Placental biomarkers of fetal-originated diseases

2024-01-09T09:45:14+08:00

Fetal-originated diseases refer to birth defects of offspring and multiple chronic diseases in adulthood caused by abnormal embryonic development. However, due to the vulnerability of the fetus/ neonate and technical limitations, it is very difficult to carry out effective development assessment and early warning of long-term diseases in the early stages of life. It is known that the placenta is the unique link between the mother and the fetus, and its role in the occurrence and progression of fetal-originated diseases cannot be ignored. Studies have found that a variety of adverse environmental factors (such as nanoparticle exposure) cannot pass through the placental barrier, but can lead to fetal dysplasia and multi-organ development programming changes by affecting placental development, and ultimately mediate the occurrence of fetal-originated diseases. Meanwhile, some environmental factors that can pass through the placental barrier can cause placenta-fetal co-exposure, resulting in similar signaling pathways and epigenetic changes. The placenta originates from the fetus and the mother, and its development is accompanied by changes in indicators that can be objectively and quantitatively detected. These factors can be used as a biomarker to assess maternal exposure, and placental function, and to predict the developmental status and long-term disease susceptibility of offspring. To date, researchers have discovered a variety of potential placental biomarkers, and show promising application prospects. This paper reviews the recent research on placenta-related mechanisms leading to fetal-originated diseases and placental biomarkers, to provide the theoretical and experimental basis for early warning, prevention, and treatment of fetal-originated diseases.