Evidence map›Paper›PMID 38775877›Full record

ArticleBulletin of mathematical biology2024

A Model of Gastric Mucosal pH Regulation: Extending Sensitivity Analysis Using Sobol' Indices to Understand Higher Moments.

Manu Aggarwal, Owen Lewis, Angela Jarrett, M Y Hussaini, N G Cogan

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. SENSITIVITY BASED MODEL AGNOSTIC SCALABLE EXPLANATIONS OF DEEP LEARNING.bioRxiv : the preprint server for biology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Manu Aggarwal *Department of Mathematics, Florida State University, Tallahassee, USA. manu.aggarwal@nih.gov.ORCID 0000-0002-8262-9341
Owen Lewis *Department of Mathematics & Statistics, University of New Mexico, Albuquerque, New Mexico, USA.
Angela JarrettInstitute for Computational Engineering and Sciences, University of Texas at Austin, Austin, Texas, USA.
M Y HussainiDepartment of Mathematics, Florida State University, Tallahassee, USA.
N G CoganDepartment of Mathematics, Florida State University, Tallahassee, USA.

Funding

Modeling gastric mucus layer physiologyR01GM131408 · NIGMS · UNIVERSITY OF UTAH · PI FOGELSON, AARON L · 2018 to 2021
$1.5M
NIGMS NIH HHS R01 GM131408
6 · The paper itself

Abstract

Several recent theoretical studies have indicated that a relatively simple secretion control mechanism in the epithelial cells lining the stomach may be responsible for maintaining a neutral (healthy) pH adjacent to the stomach wall, even in the face of enormous electrodiffusive acid transport from the interior of the stomach. Subsequent work used Sobol' Indices (SIs) to quantify the degree to which this secretion mechanism is "self-regulating" i.e. the degree to which the wall pH is held neutral as mathematical parameters vary. However, questions remain regarding the nature of the control that specific parameters exert over the maintenance of a healthy stomach wall pH. Studying the sensitivity of higher moments of the statistical distribution of a model output can provide useful information, for example, how one parameter may skew the distribution towards or away from a physiologically advantageous regime. In this work, we prove a relationship between SIs and the higher moments and show how it can potentially reduce the cost of computing sensitivity of said moments. We define

Indexed as

Gastric MucosaMathematical ConceptsModels, BiologicalComputer SimulationGastric AcidHumansHydrogen-Ion ConcentrationElectrodiffusionGastric mucusSensitivity analysisSobol indicesSystems biology

Identifiers

PMID38775877
PMCPMC11629775

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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.