Evidence map›Paper›PMID 29023694›Full record

ReviewJournal of anatomy2018

Adaptive self-organization in the embryo: its importance to adult anatomy and to tissue engineering.

Jamie A Davies

Abstract readReview
In one paragraph

Review in Journal of anatomy, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. A morphometric and stereological study of the chorioallantoic membrane of the developing ostrich (Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 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

1 author.

Jamie A DaviesDeanery of Biomedical Sciences, University of Edinburgh Medical School, Edinburgh, UK.ORCID 0000-0001-6660-4032

Funding

Biotechnology and Biological Sciences Research Council BB/M018040/1Medical Research Council MR/K010735/1
6 · The paper itself

Abstract

The anatomy of healthy humans shows much minor variation, and twin-studies reveal at least some of this variation cannot be explained genetically. A plausible explanation is that fine-scale anatomy is not specified directly in a genetic programme, but emerges from self-organizing behaviours of cells that, for example, place a new capillary where it happens to be needed to prevent local hypoxia. Self-organizing behaviour can be identified by manipulating growing tissues (e.g. putting them under a spatial constraint) and observing an adaptive change that conserves the character of the normal tissue while altering its precise anatomy. Self-organization can be practically useful in tissue engineering but it is limited; generally, it is good for producing realistic small-scale anatomy but large-scale features will be missing. This is because self-organizing organoids miss critical symmetry-breaking influences present in the embryo: simulating these artificially, for example, with local signal sources, makes anatomy realistic even at large scales. A growing understanding of the mechanisms of self-organization is now allowing synthetic biologists to take their first tentative steps towards constructing artificial multicellular systems that spontaneously organize themselves into patterns, which may soon be extended into three-dimensional shapes.

Indexed as

Anatomic VariationAnimalsDogsEmbryo, MammalianEmbryonic DevelopmentFeedback, PhysiologicalHumansMiceOrganogenesisOrganoidsSynthetic BiologyTissue Engineeringmini-organorganoidsymmetrysymmetry-breakingsynthetic biologysynthetic morphology

Identifiers

PMID29023694
PMCPMC5835792

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.