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Abstract: Importance: SARS-CoV-2 infection can result in ongoing, relapsing, or new symptoms or other health effects after the acute phase of infection; termed post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID. The characteristics, prevalence, trajectory and mechanisms of PASC are ill-defined. The objectives of the Researching COVID to Enhance Recovery (RECOVER) Multi-site Observational Study of PASC in Adults (RECOVER-Adult) are to: (1) characterize PASC prevalence; (2) characterize the symptoms, organ dysfunction, natural history, and distinct phenotypes of PASC; (3) identify demographic, social and clinical risk factors for PASC onset and recovery; and (4) define the biological mechanisms underlying PASC pathogenesis. Methods: RECOVER-Adult is a combined prospective/retrospective cohort currently planned to enroll 14,880 adults aged ≥18 years. Eligible participants either must meet WHO criteria for suspected, probable, or confirmed infection; or must have evidence of no prior infection. Recruitment occurs at 86 sites in 33 U.S. states, Washington, DC and Puerto Rico, via facility– and community-based outreach. Participants complete quarterly questionnaires about symptoms, social determinants, vaccination status, and interim SARS-CoV-2 infections. In addition, participants contribute biospecimens and undergo physical and laboratory examinations at approximately 0, 90 and 180 days from infection or negative test date, and yearly thereafter. Some participants undergo additional testing based on specific criteria or random sampling. Patient representatives provide input on all study processes. The primary study outcome is onset of PASC, measured by signs and symptoms. A paradigm for identifying PASC cases will be defined and updated using supervised and unsupervised learning approaches with cross– validation. Logistic regression and proportional hazards regression will be conducted to investigate associations between risk factors, onset, and resolution of PASC symptoms. Discussion: RECOVER-Adult is the first national, prospective, longitudinal cohort of PASC among US adults. Results of this study are intended to inform public health, spur clinical trials, and expand treatment options.

IntroductionWomen with hypertensive disorders of pregnancy (HDP) have an increased risk of cardiovascular disease. Whether HDP is also associated with later-life dementia has not been fully explored.MethodsUsing the Utah Population Database, we performed an 80-year retrospective cohort study of 59,668 parous women.ResultsWomen with, versus without, HDP, had a 1.37 higher risk of all-cause dementia (95% confidence interval [CI]: 1.26, 1.50) after adjustment for maternal age at index birth, birth year, and parity. HDP was associated with a 1.64 higher risk of vascular dementia (95% CI: 1.19, 2.26) and 1.49 higher risk of other dementia (95% CI: 1.34, 1.65) but not Alzheimer's disease dementia (adjusted hazard ratio = 1.04; 95% CI: 0.87, 1.24). Gestational hypertension and preeclampsia/eclampsia showed similar increased dementia risk. Nine mid-life cardiometabolic and mental health conditions explained 61% of HDP's effect on subsequent dementia risk.DiscussionImproved HDP and mid-life care could reduce the risk of dementia.

In this work we implement a new methodology to study structural and mechanical properties of systems having spherical and planar symmetries throughout Molecular Dynamics simulations. This methodology is applied here to a drug delivery system based in polymersomes, as an example. The chosen model drug was the local anesthetic prilocaine due to previous parameterization within the used coarse grain scheme. In our approach, mass density profiles (MDPs) are used to obtain key structural parameters of the systems, and pressure profiles are used to estimate the curvature elastic parameters. The calculation of pressure profiles and radial MPDs required the development of specific methods, which were implemented in an in-house built version of the GROMACS 2018 code. The methodology presented in this work is applied to characterize poly(ethylene oxide)-poly(butadiene) polymersomes and bilayers loaded with the model drug prilocaine. Our results show that structural properties of the polymersome membrane could be obtained from bilayer simulations, with significantly lower computational cost compared to whole polymersome simulations, but the bilayer simulations are insufficient to get insights on their mechanical aspects, since the elastic parameters are canceled out for the complete bilayer (as consequence of the symmetry). The simulations of entire polymersomes, although more complex, offer a complementary approach to get insights on the mechanical behavior of the systems. [ABSTRACT FROM AUTHOR]

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

The use of Artificial Intelligence (AI) in medicine has attracted a great deal of attention in the medical literature, but less is known about how to assess the uncertainty of individual predictions in clinical applications. This paper demonstrates the use of Conformal Prediction (CP) to provide insight on racial stratification of uncertainty quantification for breast cancer risk prediction. The results presented here show that CP methods provide important information about the diminished quality of predictions for individuals of minority racial backgrounds. [ABSTRACT FROM AUTHOR]

A unique approach with rare resources was used to identify candidate variants predisposing to familial nonsquamous nonsmall‐cell lung cancers (NSNSCLC). We analyzed sequence data from NSNSCLC‐affected cousin pairs belonging to high‐risk lung cancer pedigrees identified in a genealogy of Utah linked to statewide cancer records to identify rare, shared candidate predisposition variants. Variants were tested for association with lung cancer risk in UK Biobank. Evidence for linkage with lung cancer was also reviewed in families from the Genetic Epidemiology of Lung Cancer Consortium. Protein prediction modeling compared the mutation with reference. We sequenced NSNSCLC‐affected cousin pairs from eight high‐risk lung cancer pedigrees and identified 66 rare candidate variants shared in the cousin pairs. One variant in the FGF5 gene also showed significant association with lung cancer in UKBiobank. This variant was observed in 3/163 additional sampled Utah lung cancer cases, 2 of whom were related in another independent pedigree. Modeling of the predicted protein predicted a second binding site for SO4 that may indicate binding differences. This unique study identified multiple candidate predisposition variants for NSNSCLC, including a rare variant in FGF5 that was significantly associated with lung cancer risk and that segregated with lung cancer in the two pedigrees in which it was observed. FGF5 is an oncogenic factor in several human cancers, and the mutation found here (W81C) changes the binding ability of heparan sulfate to FGF5, which might lead to its deregulation. These results support FGF5 as a potential NSNSCLC predisposition gene and present additional candidate predisposition variants. [ABSTRACT FROM AUTHOR]