Publications by authors named "J R De Sanchez"

Background: The impact of referral type and socioeconomic status on completion of the bariatric surgery process is not well understood.

Objectives: This study aims to 1) describe how sociodemographic characteristics influence referral type and 2) identify predictors of completion of surgery.

Setting: Large multihospital health care system, including a large academic medical center.

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High-energy nuclear collisions create a quark-gluon plasma, whose initial condition and subsequent expansion vary from event to event, impacting the distribution of the eventwise average transverse momentum [P([p_{T}])]. Disentangling the contributions from fluctuations in the nuclear overlap size (geometrical component) and other sources at a fixed size (intrinsic component) remains a challenge. This problem is addressed by measuring the mean, variance, and skewness of P([p_{T}]) in ^{208}Pb+^{208}Pb and ^{129}Xe+^{129}Xe collisions at sqrt[s_{NN}]=5.

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Article Synopsis
  • Atopic dermatitis (AD) greatly affects patients' quality of life, and existing guidelines often target specialists instead of primary care physicians (PCPs).
  • A study aimed to create a consensus document specifically for PCPs, offering evidence-based recommendations to enhance AD patient diagnosis and management.
  • The document identifies obstacles PCPs face in implementing guidelines and provides consensus solutions to improve care for AD patients across different medical specialties.
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Background: Prior studies have shown differences in the genetic etiology and clinical presentation of Alzheimer's Disease across populations. For example, for multiple genetic loci associated with AD, effect sizes can vary drastically between individuals of different ancestral backgrounds. Few investigations into differences in epigenetic features like DNA methylation have been conducted in AD, particularly in diverse individuals.

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Background: Polygenic Risk Scores (PRS) are important in predicting disease risk and are usually rely on markers selected by thresholding p-values from genome-wide association studies (GWAS). In traditional approaches, one single model is built to calculate risk scores, employing effect size to determine additive risk. However, this traditional method overlooks potential interactions between genetic loci resulting in reduced prediction power.

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