Sex Differences in Hypertension and Its Management Throughout Life.

Background: The prevalence of hypertension and uncontrolled hypertension may differ by age and sex.

Methods: We included participants in the Atherosclerosis Risk in Communities study at seven study visits over 33 years (visit 1: 15 636 participants; mean age, 54 years; 55% women), estimating sex differences in prevalence of hypertension (systolic blood pressure ≥130 mm Hg; diastolic blood pressure ≥80 mm Hg; or self-reported antihypertension medication use) and uncontrolled hypertension (systolic blood pressure ≥140 mm Hg or diastolic blood pressure ≥90 mm Hg) using unadjusted and comorbidity-adjusted models.

Results: The prevalence of hypertension increased with age from 40% (ages, 43-46 years) to 93% (ages, 91-94 years).

Plasma proteomics of acute tubular injury.

The kidney tubules constitute two-thirds of the cells of the kidney and account for the majority of the organ's metabolic energy expenditure. Acute tubular injury (ATI) is observed across various types of kidney diseases and may significantly contribute to progression to kidney failure. Non-invasive biomarkers of ATI may allow for early detection and drug development.

Serum and Urine Metabolites and Kidney Function.

Key Points: We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort. Metabolic profiling in serum and urine provides distinct and complementary insights into disease.

Background: Metabolites represent a read-out of cellular processes underlying states of health and disease.

Associations of Urine and Plasma Metabolites With Kidney Failure and Death in a Chronic Kidney Disease Cohort.

Rationale & Objective: Biomarkers that enable better identification of persons with chronic kidney disease (CKD) who are at higher risk for disease progression and adverse events are needed. This study sought to identify urine and plasma metabolites associated with progression of kidney disease.

Study Design: Prospective metabolome-wide association study.

A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging.

Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section.

The effect of spatial sampling on the resolution of the magnetostatic inverse problem.

In magnetoencephalography, linear minimum norm inverse methods are commonly employed when a solution with minimal a priori assumptions is desirable. These methods typically produce spatially extended inverse solutions, even when the generating source is focal. Various reasons have been proposed for this effect, including intrisic properties of the minimum norm solution, effects of regularization, noise, and limitations of the sensor array.

Effects of head modeling errors on the spatial frequency representation of MEG.

We aim to investigate the effects of head model inaccuracies on signal and source reconstruction accuracies for various sensor array distances to the head. This allows for the assessment of the importance of head modeling for next-generation magnetoencephalography (MEG) sensors, optically-pumped magnetometers (OPM).A 1-shell boundary element method (BEM) spherical head model with 642 vertices of radius 9 cm and conductivity of 0.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods.