ArticleFrontiers in physiology2019
Computational Modeling of Glucose Uptake in the Enterocyte.
Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04206579 (Oral 10% Dextrose Versus Sodium Dextrose Formula in Performance of Soccer Athlete. A Double-Blind Cross Over Randomized Controlled Trial), which is not on this map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Oral 10% Dextrose Versus Sodium Dextrose Formula in Performance of Soccer Athlete. A Double-Blind Cross Over Randomized Controlled Trial
Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Effects of 120 g/h of Carbohydrates Intake during a Mountain Marathon on Exercise-Induced Muscle Damage in Elite Runners.Nutrients · 2020Trial
- Experimental and computational investigation of the kinetic evolution of the glutaminolysis pathway and its interplay with the glycolysis pathway.FEBS open bio · 2024Article
- Brain microvascular endothelial cell metabolism and its ties to barrier function.Vitamins and hormones · 2024Article
- Mechanistic Computational Models of Epithelial Cell Transporters-the Adorned Heroes of Pharmacokinetics.Frontiers in pharmacology · 2021Review
- Computational Modelling of Glucose Uptake by SGLT1 and Apical GLUT2 in the Enterocyte.Frontiers in physiology · 2021Article
- Glucose transporters in the small intestine in health and disease.Pflugers Archiv : European journal of physiology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Absorption of glucose across the epithelial cells of the small intestine is a key process in human nutrition and initiates signaling cascades that regulate metabolic homeostasis. Validated and predictive mathematical models of glucose transport in intestinal epithelial cells are essential for interpreting experimental data, generating hypotheses, and understanding the contributions of and interactions between transport pathways. Here we report on the development of such a model that, in contrast to existing models, incorporates mechanistic descriptions of all relevant transport proteins and is implemented in the CellML framework. The model is validated against experimental and simulation data from the literature. It is then used to elucidate the relative contributions of the sodium-glucose cotransporter (SGLT1) and the glucose transporter type 2 (GLUT2) proteins in published measurements of glucose absorption from human intestinal epithelial cell lines. The model predicts that the contribution of SGLT1 dominates at low extracellular glucose concentrations (<20 mM) and short exposure times (<60 s) while the GLUT2 contribution is more significant at high glucose concentrations and long durations. Implementation in CellML permitted a modular structure in which the model was composed by reusing existing models of the individual transporters. The final structure also permits transparent changes of the model components and parameter values in order to facilitate model reuse, extension, and customization (for example, to simplify, or add complexity to specific transporter/pathway models, or reuse the model as a component of a larger framework) and carry out parameter sensitivity studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.