Pre-eclampsia screening in Denmark (PRESIDE): national validation study.

OBJECTIVES: To investigate the predictive performance of the Fetal Medicine Foundation (FMF) first-trimester screening algorithm for pre-eclampsia in a Danish population and compare screening performance with that of the current Danish strategy, which is based on maternal risk factors. METHODS: This was a prospective study of women with a singleton pregnancy attending for their first-trimester ultrasound scan and screening for aneuploidies at six Danish university hospitals between May 2019 and December 2020. Prenatal data on maternal characteristics and medical history were recorded, and measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI), serum pregnancy-associated plasma protein-A (PAPP-A) and serum placental growth factor (PlGF) were collected without performing a risk assessment for pre-eclampsia. Information on acetylsalicylic acid use was recorded. After delivery, pregnancy outcome, including gestational age at delivery and pre-eclampsia diagnosis, was recorded. Pre-eclampsia risk assessment for each woman was calculated blinded to outcome using the FMF screening algorithm following adjustment to the Danish population. Detection rates (DRs) of the FMF algorithm were calculated for a fixed screen-positive rate (SPR) of 10% and for the SPR achieved in the current Danish screening. RESULTS: A total of 8783 pregnant women were included, with a median age of 30.8 (interquartile range (IQR), 28.1-33.9) years. The majority were white (95%), naturally conceiving (90%), non-smokers (97%) and had no family history of pre-eclampsia (96%). The median body mass index was 23.4 (IQR, 21.2-26.6) kg/m2 . A complete risk assessment including maternal characteristics, MAP, UtA-PI, PlGF and PAPP-A was available for 8156 women (92.9%). In these women, UtA-PI was measured bilaterally with a median value of 1.58 (IQR, 1.27-1.94) and the median resting MAP of 80.5 (IQR, 76.1-85.4) mmHg in two consecutive measurements. Among these, 303 (3.7%) developed pre-eclampsia, including 55 (0.7%) cases of pre-eclampsia with delivery < 37 weeks of gestation and 16 (0.2%) cases of pre-eclampsia with delivery < 34 weeks. At a SPR of 10%, combined screening using the FMF algorithm based on maternal characteristics, MAP, UtA-PI, PlGF and PAPP-A had a DR of 77.4% (95% CI, 57.6-97.2%) for pre-eclampsia with delivery < 34 weeks, 66.8% (95% CI, 54.4-79.1%) for pre-eclampsia with delivery < 37 weeks and 44.1% (95% CI, 38.5-49.7%) for pre-eclampsia with delivery at any gestational age. The current Danish screening strategy using maternal risk factors detected 25.0% of women with pre-eclampsia with delivery < 34 weeks and 19.6% of women with pre-eclampsia with delivery < 37 weeks at a SPR of 3.4%. When applying the FMF algorithm including maternal characteristics, MAP, UtA-PI and PlGF at the fixed SPR of 3.4%, the DRs were 60.5% (95% CI, 36.9-84.1%) for PE with delivery < 34 weeks and 45.2% (95% CI, 32.0-58.5%) for PE with delivery < 37 weeks. CONCLUSION: In this large Danish multicenter study, the FMF algorithm based on maternal characteristics, MAP, UtA-PI, PlGF and PAPP-A predicted 77.4% of cases with pre-eclampsia with delivery < 34 weeks and 66.8% of cases with pre-eclampsia with delivery < 37 weeks of gestation at a SPR of 10%, suggesting that the performance of the algorithm in a Danish cohort matches that in other populations. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Novel method to identify the optimal antimicrobial peptide in a combination matrix, using anoplin as an example.

Microbial resistance is an increasing health concern and a true danger to human well-being. A worldwide search for new compounds is ongoing, and antimicrobial peptides are promising lead candidates for tomorrow's antibiotics. The decapeptide anoplin (GLLKRIKTLL-NH2) is an especially interesting candidate because of its small size as well as its antimicrobial and nonhemolytic properties. Optimization of the properties of an antimicrobial peptide such as anoplin requires multidimensional searching in a complex chemical space. Typically, such optimization is performed by labor-intensive and costly trial-and-error methods. In this study, we show the benefit of fractional factorial design for identification of the optimal antimicrobial peptide in a combination matrix. We synthesized and analyzed a training set of 12 anoplin analogs, representative of 64 analogs in total. Using MIC, hemolysis, and high-performance liquid chromatography retention time data, we constructed analysis-of-variance models that describe the relationship between these properties and the structural characteristics of the analogs. We show that the mathematical models derived from the training set data can be used to predict the properties of other analogs in the chemical space. Hence, this method provides an efficient means of identification of the optimal peptide in the searched chemical space.