Evidence map›Paper›PMID 35401841›Full record

ArticleCellular and molecular bioengineering2022

Analysis of Regional Variations of the Interstitial Cells of Cajal in the Murine Distal Stomach Informed by Confocal Imaging and Machine Learning Methods.

Sue Ann Mah, Peng Du, Recep Avci, Jean-Marie Vanderwinden, Leo K Cheng

Open access · bronzeAbstract read
In one paragraph

Article in Cellular and molecular bioengineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.4field-weighted citation impact, top 23% of its field
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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Morphological remodeling of canine lymphocytes in Ehrlichia canis infection: quantitative analysis by scanning electron microscopy and fractal dimension.Revista brasileira de parasitologia veterinaria = Brazilian journal of veterinary parasitology : Orgao Oficial do Colegio Brasileiro de Parasitologia Veterinaria · 2026
    Article
  2. Feature tracking-based motility analysis of excised intestinal tissue using video recordings.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025
    Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. 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 at 2 institutions in 3 countries.

Sue Ann MahAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.ORCID 0000-0003-0835-1860
Peng DuAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Recep AvciAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Jean-Marie VanderwindenLaboratoire de Neurophysiologie, Faculté de Médecine, Université Libre de Bruxelles, Brussels, Belgium.
Leo K ChengAuckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
University of Auckland · NZUniversité Libre de Bruxelles · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The network of Interstitial Cells of Cajal (ICC) plays a plethora of key roles in maintaining, coordinating, and regulating the contractions of the gastrointestinal (GI) smooth muscles. Several GI functional motility disorders have been associated with ICC degradation. This study extended a previously reported 2D morphological analysis and applied it to 3D spatial quantification of three different types of ICC networks in the distal stomach guided by confocal imaging and machine learning methods. The characterization of the complex changes in spatial structure of the ICC network architecture contributes to our understanding of the roles that different types of ICC may play in post-prandial physiology, pathogenesis, and/or amelioration of GI dsymotility- bridging structure and function. Methods: A validated classification method using Trainable Weka Segmentation was applied to segment the ICC from a confocal dataset of the gastric antrum of a transgenic mouse, followed by structural analysis of the segmented images. Results: The machine learning model performance was compared to manually segmented subfields, achieving an area under the receiver-operating characteristic (AUROC) of 0.973 and 0.995 for myenteric ICC (ICC-MP; Conclusions: The segmentation and analysis methods provide a high-throughput framework of investigating the structural changes in extended ICC networks and their associated physiological functions in animal models.

Indexed as

2D morphological analysisFractal-based metricsGut tissueICC networkImage analysisNeurogastroenterology & motilityPacemaker cellsSlow wavesTrainable Weka Segmentation

Identifiers

PMID35401841
PMCPMC8938532
OpenAlexW4205587718

What OpenQuestion holds

Textmetadata
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.