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Background COVID-19 causes hypercoagulability, but the association between coagulopathy and hypoxemia in critically ill patients has not been thoroughly explored. This study hypothesized that severity of coagulopathy would be associated with acute respiratory distress syndrome severity, major thrombotic events, and mortality in patients requiring intensive care unit–level care. Methods Viscoelastic testing by rotational thromboelastometry and coagulation factor biomarker analyses were performed in this prospective observational cohort study of critically ill COVID-19 patients from April 2020 to October 2020. Statistical analyses were performed to identify significant coagulopathic biomarkers such as fibrinolysis-inhibiting plasminogen activator inhibitor 1 and their associations with clinical outcomes such as mortality, extracorporeal membrane oxygenation requirement, occurrence of major thrombotic events, and severity of hypoxemia (arterial partial pressure of oxygen/fraction of inspired oxygen categorized into mild, moderate, and severe per the Berlin criteria). Results In total, 53 of 55 (96%) of the cohort required mechanical ventilation and 9 of 55 (16%) required extracorporeal membrane oxygenation. Extracorporeal membrane oxygenation–naïve patients demonstrated lysis indices at 30 min indicative of fibrinolytic suppression on rotational thromboelastometry. Survivors demonstrated fewer procoagulate acute phase reactants, such as microparticle-bound tissue factor levels (odds ratio, 0.14 [0.02, 0.99]; P = 0.049). Those who did not experience significant bleeding events had smaller changes in ADAMTS13 levels compared to those who did (odds ratio, 0.05 [0, 0.7]; P = 0.026). Elevations in plasminogen activator inhibitor 1 (odds ratio, 1.95 [1.21, 3.14]; P = 0.006), d-dimer (odds ratio, 3.52 [0.99, 12.48]; P = 0.05), and factor VIII (no clot, 1.15 ± 0.28 vs. clot, 1.42 ± 0.31; P = 0.003) were also demonstrated in extracorporeal membrane oxygenation–naïve patients who experienced major thrombotic events. Plasminogen activator inhibitor 1 levels were significantly elevated during periods of severe compared to mild and moderate acute respiratory distress syndrome (severe, 44.2 ± 14.9 ng/ml vs. mild, 31.8 ± 14.7 ng/ml and moderate, 33.1 ± 15.9 ng/ml; P = 0.029 and 0.039, respectively). Conclusions Increased inflammatory and procoagulant markers such as plasminogen activator inhibitor 1, microparticle-bound tissue factor, and von Willebrand factor levels are associated with severe hypoxemia and major thrombotic events, implicating fibrinolytic suppression in the microcirculatory system and subsequent micro- and macrovascular thrombosis in severe COVID-19. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

A wide variety of dietary supplements are available for the treatment of post-menopausal symptoms in women. Many women choose these supplements to avoid the potential unwanted side effects and long-term complications associated with traditional hormone therapy. In this study, we evaluated the in vitro estrogen activity of extracts from eleven supplements targeted for post-menopausal women to test the hypothesis that some of these supplements will induce estrogen activity in human breast cancer cell bioassays. Ethanol extracts (1 g: 1 mL) were prepared from the supplements: Amberen, Nature's Bounty Black Cohosh, Nature's Bounty St. John's Wort, Swanson Chasteberry Fruit, Nature's Way Red Clover, Promensil Real Health, Estroven, Relizen, Remifemin, and Equelle. Black cohosh, St. John's Wort, Chasteberry fruit, and red clover are standalone products that are not sold under a brand name. Promensil, Estroven, and Equelle contain isoflavones that have been reported to be estrogenic. Estroven and Remifemin contain black cohosh. Relizen contains a purified Swedish flower pollen extract and Amberen contains succinates, amino acids, minerals, and vitamin E. The estrogen activity of each extract was measured using both the T47D-kb-Luc estrogen responsive reporter gene and MCF-7 E3 proliferation estrogen bio-assays. The extracts from Nature's Way Red Clover, Promensil Real Health, and Equelle induced full estrogen agonist efficacy in the MCF-7 proliferation assay and more than full efficacy in the T47D-kb-Luc assay compared to estradiol. The agonist activity of these extracts in both bio-assays was inhibited by fulvestrant. Extracts from Nature's Bounty Black Cohosh, Nature's Bounty St. John's Wort, Swanson Chasteberry Fruit and Relizen induced less than full agonist estrogen activity only in the MCF-7 proliferation assay that was not, or not completely inhibited by fulvestrant. Thus, some supplements in this study likely target the estrogen receptor as agonists while others may be only weak estrogens that may also activate other proliferative pathways. While estrogen therapy is known to provide relief of some post-menopausal symptoms, estrogen use is associated with increased breast cancer risk. Consumers may benefit from information about the estrogen activity of supplements targeted for post-menopausal women. The results of this study may allow women to make more informed decisions about the use of these supplements. This study may be a prototype for evaluating the estrogen activity of dietary supplements for the purpose of informing consumers. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

What factors affect listeners’ emotional perception of music? Ali and Peynircioğlu (2006) conducted a series of experiments on the listener's emotional response to melodies and lyrics in songs. Here we present a pre-registered replication and extension of this line of research with new adapted stimuli and several musical covariates using the Goldsmiths Musical Sophistication Index (Gold-MSI). Using a within-subjects design, participants (n = 104) were asked to rate the perceived emotions to unfamiliar happy, sad, calm, and angry songs with and without lyrics to model the extent to which each factor contributed to listener ratings. We failed to replicate the results of the original paper, but did find significant results in the opposite direction of several variables. Our extension of the analysis found evidence supporting the idea that emotional perception can and should be divorced from aspects associated with musical training. The mixed-effects model showed a significant effect of the emotional engagement subscale of the Gold-MSI above and beyond our replication analysis. While the results we detail in this research report conflict with the original findings, this conceptual replication and extension serve to highlight the importance of replicating findings reported within the music psychology literature.

The timing of the wane in heavy meteorite bombardment of the inner planets is debated. Its timing determines the onset of crustal conditions consistently below the thermal and shock pressure limits for microbiota survival, and so bounds the occurrence of conditions that allow planets to be habitable. Here we determine this timing for Mars by examining the metamorphic histories of the oldest known Martian minerals, 4.476–4.429-Gyr-old zircon and baddeleyite grains in meteorites derived from the southern highlands. We use electron microscopy and atom probe tomography to show that none of these grains were exposed to the life-limiting shock pressure of 78 GPa. 97% of the grains exhibit weak-to-no shock metamorphic features and no thermal overprints from shock-induced melting. By contrast, about 80% of the studied grains from bombarded crust on Earth and the Moon show such features. The giant impact proposed to have created Mars’ hemispheric dichotomy must, therefore, have taken place more than 4.48 Gyr ago, with no later cataclysmic bombardments. Considering thermal habitability models, we conclude that portions of Mars’ crust reached habitable pressures and temperatures by 4.2 Gyr ago, the onset of the Martian ‘wet’ period, about 0.5 Gyr earlier than the earliest known record of life on Earth. Early abiogenesis by 4.2 Gyr ago, is now tenable for both planets. The oldest known minerals from Mars have no strong shock features, indicating early cessation of giant impacts there, according to microanalysis of zircon and baddeleyite grains in meteorites.

Using structured elements from Electronic Health Records (EHR), we seek to:i) build predictive models to stratify patients tested for COVID-19 by their likelihood for hospitalization, ICU admission, mechanical ventilation and inpatient mortality, andii) identify the most important EHR-based features driving the predictions. We leveraged EHR data from the Duke University Health System tested for COVID-19 or hospitalized between March 11, 2020 and August 24, 2020, to build models to predict hospital admissions within 4 weeks. Models were also created for ICU admissions, need for mechanical ventilation and mortality following admission. Models were developed on a cohort of 86,355 patients with 112,392 outpatient COVID-19 tests or any-cause hospital admissions between March 11, 2020 and June 4, 2020. The four models considered resulted in AUROC=0.838 (CI: 0.832-0.844) and AP=0.272 (CI: 0.260-0.287) for hospital admissions, AUROC=0.847 (CI: 0.839-855) and AP=0.585 (CI: 0.565-0.603) for ICU admissions, AUROC=0.858 (CI: 0.846-0.871) and AP=0.434 (CI: 0.403-0.467) for mechanical ventilation, and AUROC=0.0.856 (CI: 0.842-0.872) and AP=0.243 (CI: 0.205-0.282) for inpatient mortality. Patient history abstracted from the EHR has the potential for being used to stratify patients tested for COVID-19 in terms of utilization and mortality. The dominant EHR features for hospital admissions and inpatient outcomes are different. For the former, age, social indicators and previous utilization are the most important predictive features. For the latter, age and physiological summaries (pulse and blood pressure) are the main drivers.

Combining the increasing availability and abundance of healthcare data and the current advances in machine learning methods have created renewed opportunities to improve clinical decision support systems. However, in healthcare risk prediction applications, the proportion of cases with the condition (label) of interest is often very low relative to the available sample size. Though very prevalent in healthcare, such imbalanced classification settings are also common and challenging in many other scenarios. So motivated, we propose a variational disentanglement approach to semi-parametrically learn from rare events in heavily imbalanced classification problems. Specifically, we leverage the imposed extreme-distribution behavior on a latent space to extract information from low-prevalence events, and develop a robust prediction arm that joins the merits of the generalized additive model and isotonic neural nets. Results on synthetic studies and diverse real-world datasets, including mortality prediction on a COVID-19 cohort, demonstrate that the proposed approach outperforms existing alternatives., Comment: Accepted to AAAI2021

We prove that for a sequence of nested sets $$\{U_n\}$$ with $$\Lambda = \cap _n U_n$$ a measure zero set, the localized escape rate converges to the extremal index of $$\Lambda $$ , provided that the dynamical system is $$\phi $$ -mixing at polynomial speed. We also establish the general equivalence between the local escape rate for entry times and the local escape rate for returns. Examples include a dichotomy for periodic and non-periodic points, Cantor sets on the interval, and submanifolds of Anosov diffeomorphisms on surfaces.