Background: Public health nutritionists need accurate and feasible methods to assess vitamin A status and to evaluate efficacy of interventions, especially in children. The application of population-based designs to tracer kinetic data is an effective approach that reduces sample burden for each child.
Objectives: Objectives of the study were to use theoretical data to validate a population-based (super-child) approach for estimating group mean vitamin A total body stores (TBS) and retinol kinetics in children and to use population-based data to improve individual TBS predictions using retinol isotope dilution (RID).
Methods: We generated plasma retinol kinetic data from 6 h to 56 d for 50 theoretical children with high vitamin A intakes, assigning values within physiologically reasonable ranges for state variables and kinetic parameters ("known values"). Mean data sets for all subjects at extensive ( = 36) and reduced ( = 11) sampling times, plus 5 data sets for reduced numbers (5/time, except all at 4 d) and times, were analyzed using Simulation, Analysis and Modeling software. Results were compared with known values; population RID coefficients were used to calculate TBS for individuals.
Results: For extensive and reduced data sets including all subjects, population TBS predictions were within 1% of the known value. For 5 data sets reflecting numbers and times being used in ongoing super-child studies, predictions were within 1-17% of the known group value. Using RID equation coefficients from population modeling, TBS predictions at 4 d were within 25% of the known value for 66-80% of subjects and reflected the range of assigned values; when ranked, predicted and assigned values were significantly correlated ( = 0.93, < 0.0001). Results indicate that 7 d may be better than 4 d for applying RID in children. For all data sets, predictions for kinetic parameters reflected the range of known values.
Conclusion: The population-based (super-child) approach provides a feasible experimental design for quantifying retinol kinetics, accurately estimating group mean TBS, and predicting TBS for individuals reasonably well.
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http://dx.doi.org/10.1093/cdn/nzy071 | DOI Listing |
Lifetime Data Anal
January 2025
Institut Camille Jordan, UMR 5208, Université Claude Bernard Lyon 1, Bat. Braconnier, 43, blvd du 11 novembre 1918, F - 69622, Villeurbanne Cedex, France.
Based on the expectile loss function and the adaptive LASSO penalty, the paper proposes and studies the estimation methods for the accelerated failure time (AFT) model. In this approach, we need to estimate the survival function of the censoring variable by the Kaplan-Meier estimator. The AFT model parameters are first estimated by the expectile method and afterwards, when the number of explanatory variables can be large, by the adaptive LASSO expectile method which directly carries out the automatic selection of variables.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Urology, Ji'an Third People's Hospital, Ji'an 343000, Jiangxi, China.
As combination therapy becomes more common in clinical applications, predicting adverse effects of combination medications is a challenging task. However, there are three limitations of the existing prediction models. First, they rely on a single view of the drug and cannot fully utilize multiview information, resulting in limited performance when capturing complex structures.
View Article and Find Full Text PDFMol Ecol Resour
January 2025
Section for Molecular Ecology and Evolution, Globe Institute, University of Copenhagen, Copenhagen, Denmark.
Reduced representation sequencing (RRS) has proven to be a cost-effective solution for sequencing subsets of the genome in non-model species for large-scale studies. However, the targeted nature of RRS approaches commonly introduces large amounts of missing data, leading to reduced statistical power and biased estimates in downstream analyses. Genotype imputation, the statistical inference of missing sites across the genome, is a powerful alternative to overcome the caveats associated with missing sites.
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
School of Computed and Augmented Intelligence, Arizona State University, Tempe, AZ.
Objective: To report the development and performance of 2 distinct deep learning models trained exclusively on retinal color fundus photographs to classify Alzheimer disease (AD).
Patients And Methods: Two independent datasets (UK Biobank and our tertiary academic institution) of good-quality retinal photographs derived from patients with AD and controls were used to build 2 deep learning models, between April 1, 2021, and January 30, 2024. ADVAS is a U-Net-based architecture that uses retinal vessel segmentation.
J Adv Nurs
January 2025
Department of Nursing and Midwifery, College of Health Wellbeing & Life Sciences, Sheffield Hallam University, Sheffield, UK.
Aim: To highlight the use of corpus linguistics for analysing language data and to provide a worked example of this approach in nursing research.
Design: Methodology discussion paper.
Methods: This paper introduces corpus linguistics as a distinct approach to undertaking qualitative research in nursing.
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