2 results match your criteria: "and Hypertension Department of Medicine Brigham and Women's Hospital Boston MA.[Affiliation]"

Article Synopsis
  • Accurate quantification of sodium intake via self-reported surveys has been problematic, prompting researchers to use machine-learning (ML) algorithms to better predict urinary sodium excretion based on questionnaire data.
  • The study involved 3,454 participants from major health studies and found ML predictions of sodium excretion were more reliable than traditional food frequency questionnaires, showing stronger correlations and better calibration.
  • However, while ML improved predictions overall, it was still heavily influenced by body size and did not significantly reduce measurement errors related to disease outcomes.
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Background In addition to its role on blood pressure, aldosterone (ALDO) also affects the hemostatic system leading to increased experimental thrombosis. Striatin is an intermediate in the rapid, nongenomic actions of ALDO. Striatin heterozygote knockout () mice have salt sensitivity of blood pressure and mildly chronically increased ALDO levels.

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