The Effect of Low-Dose Aspirin On Serum Placental Growth Factor Levels In a High-Risk PREDO Cohort

Objectives: Our first aim was to study the longitudinal changes of serum placental growth factor (PlGF) concentration between 12(+0) and 28(+0) weeks of gestation in the prospective PREDO cohort. Our second aim was to study the effect of low-dose acetylsalicylic acid (LDA; 100 mg/day), started before the 14th week of gestation, on PlGF concentration. Study design: Blood samples were collected at 12(+0)-14(+0), 18(+0)-20(+0) and 26(+0)-28(+0) weeks of gestation in 101 women without and 309 with clinical risk factors for pre-eclampsia. Risk-women were divided into two groups: to those who had medium risk for pre-eclampsia and to those who had high risk for pre-eclampsia. Finally there were seven groups according to risk, treatment (no prevention/placebo/LDA) and outcome measure pre-eclampsia. Longitudinal changes in the PlGF concentration between groups were compared. To investigate the effect of LDA on serum PlGF concentration, placebo (N = 62) and LDA (N = 61) groups were compared. A repeated measures ANOVA was used to analyze differences in PlGF levels between the groups. Results: The increase in serum PlGF concentration was higher in LDA than in placebo group (time x group effect, p = 0.046). The increase in serum PlGF concentration during pregnancy was lower in high-risk women who had placebo and developed pre-eclampsia and in medium-risk women who developed pre-eclampsia compared to the other women (time x group effect, p <0.001). There were no differences in PlGF change between low-risk women, medium-risk women who did not develop pre-eclampsia, high-risk women in the placebo group without pre-eclampsia and high-risk women in the LDA group with and without pre-eclampsia (p = 0.15). Conclusions: Our finding suggests an association between LDA started before 14 weeks of gestation and higher increase in serum PlGF concentration.Peer reviewe

Prediction of pre-eclampsia and its subtypes in high-risk cohort: hyperglycosylated human chorionic gonadotropin in multivariate models

Background: The proportion of hyperglycosylated human chorionic gonadotropin (hCG-h) to total human chorionic gonadotropin (%hCG-h) during the first trimester is a promising biomarker for prediction of early-onset pre-eclampsia. We wanted to evaluate the performance of clinical risk factors, mean arterial pressure (MAP), %hCG-h, hCG beta, pregnancy-associated plasma protein A (PAPP-A), placental growth factor (PIGF) and mean pulsatility index of the uterine artery (Uta-PI) in the first trimester in predicting pre-eclampsia (PE) and its subtypes early-onset, late-onset, severe and non-severe PE in a high-risk cohort.Methods: We studied a subcohort of 257 high-risk women in the prospectively collected Prediction and Prevention of Pre-eclampsia and Intrauterine Growth Restriction (PREDO) cohort Multivariate logistic regression was used to construct the prediction models. The first model included background variables and MAP. Additionally, biomarkers were included in the second model and mean Uta-PI was included in the third model. All variables that improved the model fit were included at each step. The area under the curve (AUC) was determined for all models.Results: We found that lower levels of serum PIGF concentration were associated with early-onset PE, whereas lower %hCG-h was associated with the late-onset PE. Serum PIGF was lower and hCG beta higher in severe PE, while %hCG-h and serum PAPP-A were lower in non-severe PE. By using multivariate regression analyses the best prediction for all PE was achieved with the third model: AUC was 0.66, and sensitivity 36% at 90% specificity. Third model also gave the highest prediction accuracy for late-onset, severe and non-severe PE: AUC 0.66 with 32% sensitivity, AUC 0.65, 24% sensitivity and AUC 0.60, 22% sensitivity at 90% specificity, respectively. The best prediction for early-onset PE was achieved using the second model: AUC 0.68 and 20% sensitivity at 90% specificity.Conclusions: Although the multivariate models did not meet the requirements to be clinically useful screening tools, our results indicate that the biomarker profile in women with risk factors for PE is different according to the subtype of PE. The heterogeneous nature of PE results in difficulty to find new, clinically useful biomarkers for prediction of PE in early pregnancy in high-risk cohorts

The use of artificial neural networks for the selection of the most appropriate formulation and processing variables in order to predict the in vitro dissolution of sustained release minitablets

The objective of this work was to apply artificial neural networks (ANNs) to examine the relative importance of various factors, both formulation and process, governing the in-vitro dissolution from enteric-coated sustained release (SR) minitablets. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of the various formulation and processing variables in determining minitablet dissolution rate. Both forward and backward stepwise algorithms were used as well as genetic algorithms. Networks were subsequently trained using the back propagation algorithm in order to check whether or not the IFS process had correctly located any unimportant inputs. IFS gave consistent rankings for the importance of the various formulation and processing variables in determining the release of drug from minitablets. Consistent ranking was achieved for both indices of the release process; ie, the time taken for release to commence through the enteric coat (Tlag) and that for the drug to diffuse through the SR matrix of the minitablet into the dissolution medium (T90-10). In the case of the Tlag phase, the main coating parameters, along with the original batch blend size and the blend time with lubricant, were found to have most influence. By contrast, with the T90-10 phase, the amounts of matrix forming polymer and direct compression filler were most important. In the subsequent training of the ANNs, removal of inputs regarded as less important led to improved network performance. ANNs were capable of ranking the relative importance of the various formulations and processing variables that influenced the release rate of the drug from minitablets. This could be done for all main stages of the release process. Subsequent training of the ANN verified that removal of less relevant inputs from the training process led to an improved performance from the ANN

Ectodysplasin regulates hormone-independent mammary ductal morphogenesis via NF-κB.

Ductal growth of the mammary gland occurs in two distinct stages. The first round of branching morphogenesis occurs during embryogenesis, and the second round commences at the onset of puberty. Currently, relatively little is known about the genetic networks that control the initial phases of ductal expansion, which, unlike pubertal development, proceeds independent of hormonal input in female mice. Here we identify NF-κB downstream of the TNF-like ligand ectodysplasin (Eda) as a unique regulator of embryonic and prepubertal ductal morphogenesis. Loss of Eda, or inhibition of NF-κB, led to smaller ductal trees with fewer branches. On the other hand, overexpression of Eda caused a dramatic NF-κB-dependent phenotype in both female and male mice characterized by precocious and highly increased ductal growth and branching that correlated with enhanced cell proliferation. We have identified several putative transcriptional target genes of Eda/NF-κB, including PTHrP, Wnt10a, and Wnt10b, as well as Egf family ligands amphiregulin and epigen. We developed a mammary bud culture system that allowed us to manipulate mammary development ex vivo and found that recombinant PTHrP, Wnt3A, and Egf family ligands stimulate embryonic branching morphogenesis, suggesting that these pathways may cooperatively mediate the effects of Eda