Evidence map›Paper›PMID 42367033›Full record

ArticleDisease models & mechanisms2026

Mesoscale maladaptation in disease organoids.

Masashi Okamoto, Takanori Takebe

Abstract read
In one paragraph

Article in Disease models & mechanisms, 2026. 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. 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

2 authors.

Masashi OkamotoDepartment of Genome Biology, Graduate School of Medicine, and Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), the University of Osaka, Suita, Osaka 565-0871, Japan.ORCID 0000-0001-6636-9837
Takanori TakebeDepartment of Genome Biology, Graduate School of Medicine, and Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), the University of Osaka, Suita, Osaka 565-0871, Japan.ORCID 0000-0002-6989-3041

Funding

Japan Agency for Medical Research and Development 26bk0104202Japan Agency for Medical Research and Development 26bm1123064Japan Agency for Medical Research and Development 26fk0210165Japan Agency for Medical Research and Development 26ym0126177Japan Agency for Medical Research and Development JP20bm0704025Japan Agency for Medical Research and Development JP20fk0210037,Japan Agency for Medical Research and Development JP21bm0404045Japan Agency for Medical Research and Development JP24bm1223006Japan Agency for Medical Research and Development JP25bm1223006Japan Agency for Medical Research and Development JP25gm1210012Japan Agency for Medical Research and Development JP26fk0210150Japan Agency for Medical Research and Development JP26fk0210167,Japan Agency for Medical Research and Development JP26gm1610005Japan Society for the Promotion of Science 25H01051Japan Society for the Promotion of Science 25K00152Japan Society for the Promotion of Science JP18H02800Japan Society for the Promotion of Science JRP20251503Moonshot Research and Development Program JPMJMS2022-1112Moonshot Research and Development Program JPMJMS2033
6 · The paper itself

Abstract

In chronic diseases, multiple tissue components at a shared interface often deteriorate concurrently, and disease progression may depend on interactions among these failures rather than on any single defect. Multi-tissue organoid models can mimic disease-relevant pathology in vitro, but the field lacks a simple framework for comprehensive interrogation. Here, we propose mesoscale maladaptation as an operational concept for multi-tissue disease modelling, defined as a synergistic decline in interdependent functions that can occur between two or more tissue elements. To detect maladaptation, we introduce a stepwise workflow that identifies the failing tissue elements, defines directionality of potential interdependence between these elements and compares combined perturbations with single perturbations to evaluate synergistic decline. We apply this framework to intestinal neuromuscular, hepatic sinusoidal, blood-brain barrier and tumour-neural interfaces, but it can be extrapolated to other organs and systems. This framework will illuminate future directions for in vitro complex disease modelling by enriching biological insights to disentangle progressive pathology, shifting the focus from localised biological failures to concurrent multi-tissue failures that produce synergistic pathology.

Indexed as

DiseaseOrganoidsAnimalsHumansModels, Biological

Identifiers

PMID42367033
PMCPMC13382832

What Socratic 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 Socratic graph.