Pregnancy trained decidual NK cells protect pregnancies from harmful Fusobacterium nucleatum infection.

Natural killer cells (NKs) found during pregnancy at the maternal-fetal interface named decidual (d)NKs, show signs of education following first pregnancy, resulting in better placentation and fetus-growth, hence termed pregnancy trained dNKs (PTdNKs). Here we show that PTdNKs provide increased protection of the fetus from Fusobacterium nucleatum (FN) infection. We demonstrate that PTdNKs secrete elevated amounts of the bacteriocidal protein granulysin (GNLY) upon incubation with FN compared to dNKs derived from first pregnancies, which leads to increased killing of FN. Furthermore, we showed mechanistically that the GNLY secretion is mediated through the interaction of the FN's Fap2 protein with Gal-GalNAc present on PTdNKs. Finally, we show in vivo, using GNLY-tg mice that enhanced protection of the fetuses from FN infection is observed, as compared to wild type and that this enhance protection is NK cell dependent. Altogether, we show a new function for PTdNKs as protectors of the fetus from bacterial infection.

Evaluating fetal tricuspid and mitral annular plane systolic excursion (TAPSE and MAPSE) using spatiotemporal image correlation (STIC) M-mode.

OBJECTIVES: This study aimed to evaluate the feasibility and reliability of tricuspid and mitral annular plane systolic excursion measurements (TAPSE and MAPSE) applying M-mode spatiotemporal image correlation (STIC) technology in low-risk pregnancies. METHODS: An initial retrospective pilot study was carried out to assess repeatability, followed by a larger mixed cross-sectional and prospective longitudinal study, both evaluating low-risk singleton pregnancies between 22+6 and 39+1 weeks. As only annuli capture was necessary, STIC acquisition parameters were set to the minimum volume angle of 15° and acquisition time of 7.5 s. RESULTS: A total of 330 volumes were analysed offline applying STIC M-mode. Acquisition rates were 96.9 % for TAPSE and 93.7 % MAPSE in the pilot study (n=32) and 98.0 % for both in the longitudinal study (n=102). Both study designs revealed good repeatability for both sides of the heart, with higher intraclass correlation coefficients (ICCs) for TAPSE (0.84-0.94) compared to MAPSE (0.80-0.88). Good repeatability was demonstrated for both sides of the heart, more so for TAPSE than MAPSE, with ICCs for TAPSE 0.84-0.91 and MAPSE 0.75-0.76, comparable to prior ICCs for STIC repeatability. CONCLUSIONS: Modified STIC acquisition settings specifically tailored for capturing the longitudinal annular displacement may improve STIC TAPSE and MAPSE acquisition rates, optimising image quality for precise measurement and potentially bringing these modalities closer to clinical application.

[NUCHAL TRANSLUCENCY CONCURRENT WITH EARLY ANOMALY SCAN: TIME TO RECONSIDER].

INTRODUCTION: During the last decades, a major achievement was reported in detecting Down's syndrome in the first trimester of pregnancy. This is attributed to the use of high-resolution accurate ultrasound machine allowing the detection of a "nuchal translucency" in the back of the fetus during 11-14 weeks' gestation. This is considered to be a physiologic finding, but when increased, may alert for chromosomal abnormality (mainly Down's syndrome), cardiac and other organ anomalies and other genetic syndromes. Later additional sonographic findings were found, including nasal bone assessment, and Doppler flow studies of the ductus venosus and tricuspid regurgitation Technology advancement accompanied by sonographers' skills enhancement allows (at the time frame of the nuchal scan) a detailed anomaly scan. Additional screening for pregnancy complication was achieved using first trimester multi marker assessment, alerting for preeclamptic toxemia or placenta accreta. Currently, many national and international professional organizations recommend performing the nuchal scan concurrent with an early anomaly scan both at the same time of gestation. This approach is different than the one performed in Israel, whereas the nuchal scan is conducted separately and 2-3 weeks later an anomaly scan is offered. We call for reconsideration of the sequential approach and performing all the tests in a comprehensive first trimester clinic.

Machine Learning in Electronic Health Records: Identifying High-Risk Obstetric Patients Pre and During Labor.

Our goal is to apply artificial intelligence (AI) and statistical analysis to understand the relationship between various factors and outcomes during pregnancy and labor and delivery, in order to personalize birth management and reduce complications for both mothers and newborns. We use a structured electronic health records database with data from approximately 130,000 births to train, test and validate our models. We apply machine learning (ML) methods to predict various obstetrical outcomes before and during labor, with the aim of improving patient care management in the delivery ward. Using a large cohort of data (∼180 million data points), we then demonstrated that ML models can predict successful vaginal delivery, in the general population as well as a sub-cohort of women attempting trial of labor after a cesarean delivery. The real-time dynamic model showed increasing rates of accuracy as the delivery process progressed and more data became available for analysis. Additionally, we developed a cross-facilities application of an AI model that predicts the need for an unplanned cesarean delivery, illuminating the challenges associated with inter-facility variation in reporting practices. Overall, these studies combine novel technologies with currently available data to predict and assist safe deliveries for mothers and babies, both locally and globally.

Maternal hybrid immunity and risk of infant COVID-19 hospitalizations: national case-control study in Israel.

Hybrid immunity, acquired through vaccination followed or preceded by a COVID-19 infection, elicits robust antibody augmentation. We hypothesize that maternal hybrid immunity will provide greater infant protection than other forms of COVID-19 immunity in the first 6 months of life. We conducted a case-control study in Israel, enrolling 661 infants up to 6 months of age, hospitalized with COVID-19 (cases) and 59,460 age-matched non-hospitalized infants (controls) between August 24, 2021, and March 15, 2022. Infants were grouped by maternal immunity status at delivery: Naïve (never vaccinated or tested positive, reference group), Hybrid-immunity (vaccinated and tested positive), Natural-immunity (tested positive before or during the study period), Full-vaccination (two-shot regimen plus 1 booster), and Partial-vaccination (less than full three shot regimen). Applying Cox proportional hazards models to estimate the hazard ratios, which was then converted to percent vaccine effectiveness, and using the Naïve group as the reference, maternal hybrid-immunity provided the highest protection (84% [95% CI 75-90]), followed by full-vaccination (66% [95% CI 56-74]), natural-immunity (56% [95% CI 39-68]), and partial-vaccination (29% [95% CI 15-41]). Maternal hybrid-immunity was associated with a reduced risk of infant hospitalization for Covid-19, as compared to natural-immunity, regardless of exposure timing or sequence. These findings emphasize the benefits of vaccinating previously infected individuals during pregnancy to reduce COVID-19 hospitalizations in early infancy.