Evidence map›Paper›PMID 42824806›Full record

ReviewFrontiers in bioengineering and biotechnology2026

Organ-on-a-chip platforms for disease modeling and in vitro diagnostic applications.

Po Hao, Jingrong Deng, Jian Xu

Abstract readReview
In one paragraph

Review in Frontiers in bioengineering and biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Po Hao *College of Medical Technology, Chongqing Three Gorges Medical College, Chongqing, China.
Jingrong Deng *College of Medical Technology, Chongqing Three Gorges Medical College, Chongqing, China.
Jian XuCollege of Medical Technology, Chongqing Three Gorges Medical College, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Organ-on-a-Chip (OoC) systems, as microphysiological models integrating microfluidic engineering, 3D tissue construction, and sensing technologies, can faithfully recapitulate the barrier functions, dynamic microenvironments, and inter-tissue crosstalk of human organs. They effectively address the inherent limitations of traditional 2D cell cultures and animal models in in vitro diagnostics (IVD), offering a novel avenue to tackle the high failure rate of candidate drugs and biomarkers in clinical translation. This review systematically summarizes the technological advances and application practices of OoC in the IVD field, covering core construction technologies (microfluidic regulation, cell and tissue engineering, material innovation), the integrated optimization of the "online real-time monitoring-offline in-depth analysis" dual-detection system, and typical applications in disease modeling, biomarker screening, drug toxicity evaluation, and personalized diagnosis. It further explores Multi-Organ-Chip (MOC) systems and Artificial Intelligence (AI) empowerment, analyzes core bottlenecks including insufficient physiological complexity, lack of standardization, and barriers to clinical translation and industrialization, and prospects future directions from technological optimization, standardization establishment, and regulatory pathway exploration. By focusing on the IVD-oriented perspective, this review aims to provide theoretical references and practical guidance for the transformation of OoC from laboratory research to clinical IVD tools.

Indexed as

biosensingin vitro diagnosticsmicrofluidicsmulti-organ integrationorgan-on-a-chip

Identifiers

PMID42824806
PMCPMC13627989

What OpenQuestion holds

Textmetadata
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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.