Evidence map›Paper›PMID 33849336›Full record

ArticleJournal of the Royal Society, Interface2021

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs.

A Alexiadis, M J H Simmons, K Stamatopoulos, H K Batchelor, I Moulitsas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
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.

A AlexiadisSchool of Chemical Engineering, University of Birmingham, Birmingham, Edgbaston B15 2TT, UK.
M J H SimmonsSchool of Chemical Engineering, University of Birmingham, Birmingham, Edgbaston B15 2TT, UK.
K StamatopoulosSchool of Chemical Engineering, University of Birmingham, Birmingham, Edgbaston B15 2TT, UK.
H K BatchelorStrathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, 161 Cathedral Street, Glasgow G4 0RE, UK.
I MoulitsasCentre for Computational Engineering Sciences, Cranfield University, Bedford MK43 0AL, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input-output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against

Indexed as

Enteric Nervous SystemComputer SimulationHumansIntestinesLearningNeural Networks, Computercoupling multiphysics with artificial intelligencemathematical modelling of the intestinemultiphysicsreinforcement learningvirtual human

Identifiers

PMID33849336
PMCPMC8086920

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.