COVID-19 Not Hypertension or Diabetes Increases the Risk of Preeclampsia among a High-Risk Population.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection has been associated with greater morbidity and increased mortality in certain populations, such as those with chronic medical conditions, the elderly, and pregnant women. Our goal was to determine if COVID-19 infection during pregnancy increased the risk of preeclampsia in a population of women with increased risk factors for preeclampsia. We present a prospective observational matched case-control study of 100 deliveries with confirmed SARS-CoV2. Specifically, we investigated the maternal and neonatal outcomes in a high-risk population of pregnant women. Among women with COVID-19, the severity of symptoms was associated with the incidence of preeclampsia, but not with pre-existing diabetes or hypertension. Women with more severe symptoms were more likely to delivery pre-term with smaller babies. After adjusting for diabetes, hypertensive women with COVID-19 had an increased risk of preeclampsia aOR4.3 [1.5,12.4] compared to non-hypertensive women with COVID-19. After adjusting for hypertension, women with diabetes and COVID-19 had an increased risk of preeclampsia aOR3.9 [1.2,12.5]. This relationship was not seen among women without COVID-19. For women who had pre-existing diabetes or hypertension, the risk of developing preeclampsia was only increased if they were also diagnosed with COVID-19, suggesting that in our population of women the risk of preeclampsia is not associated with pre-existing diabetes or hypertension.

The power of quantum neural networks.

It is unknown whether near-term quantum computers are advantageous for machine learning tasks. In this work we address this question by trying to understand how powerful and trainable quantum machine learning models are in relation to popular classical neural networks. We propose the effective dimension-a measure that captures these qualities-and prove that it can be used to assess any statistical model's ability to generalize on new data. Crucially, the effective dimension is a data-dependent measure that depends on the Fisher information, which allows us to gauge the ability of a model to train. We demonstrate numerically that a class of quantum neural networks is able to achieve a considerably better effective dimension than comparable feedforward networks and train faster, suggesting an advantage for quantum machine learning, which we verify on real quantum hardware.

Comparative effects of estrogen and angiotensin-converting enzyme inhibition on plasminogen activator inhibitor-1 in healthy postmenopausal women.

BACKGROUND: This study compares the effect of estrogens and ACE inhibition on plasminogen activator inhibitor-1 (PAI-1) concentrations in healthy postmenopausal women, genotyped for a 4G/5G polymorphism in the PAI-1 promoter, a polymorphism shown to influence PAI-1 concentrations. Methods and Results- Morning estradiol, PAI-1, tissue plasminogen activator, plasma renin activity, angiotensin II, and aldosterone were measured in 19 postmenopausal women (5G/5G:4G/5G:4G4G=5:10:4, respectively) at baseline and during randomized, single-blind, crossover treatment with conjugated equine estrogens 0.625 mg per os per day, ramipril 10 mg per os per day, and combination estrogens and ramipril. Estradiol (P<0.005) and angiotensin II (P<0.01) were significantly higher during estrogens. Plasma renin activity was significantly increased during ACE inhibition (P<0.05). Both conjugated estrogens [PAI-1 antigen from 12.5 (7.6, 17.4) [mean (95% CI)] baseline to 6.6 (2.6, 10.7) ng/mL, P<0.01] and ACE inhibition [8.3 (4.9, 11.7) ng/mL, P<0.005] decreased PAI-1 without decreasing tissue plasminogen activator. The effect of combined therapy on PAI-1 [5.6 (2.3, 8.8) ng/mL] was significantly greater than that of ramipril alone (P<0.05). There was a significant effect of PAI-1 4G/5G genotype on baseline PAI-1 concentrations (P=0.001) and a significant interactive effect of 4G/5G genotype and treatment, such that genotype influenced the change in PAI-1 during ramipril (P=0.011) or combined therapy (P=0.006) but not during estrogens (P=0.715). CONCLUSIONS: ACE inhibition with ramipril and conjugated estrogens similarly decrease PAI-1 antigen concentrations in postmenopausal women. Larger studies that use clinical outcomes are needed to determine whether PAI-1 4G/5G genotype should influence the choice of conjugated estrogens or ACE inhibition for the treatment of healthy postmenopausal women.