Evidence map›Paper›PMID 38079086›Full record

ReviewStem cell reviews and reports2024

Retinal Organoids: A Next-Generation Platform for High-Throughput Drug Discovery.

Hongkun Zhao, Fei Yan

Open access · hybridAbstract readReview
In one paragraph

Review in Stem cell reviews and reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 4% 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

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.

  1. Pooled it
  2. Retinoic acid in health and disease.Signal transduction and targeted therapy · 2026
    Review
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  12. International journal of molecular sciences · 2025
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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 at 2 institutions in 1 country.

Hongkun ZhaoKey Laboratory of Yunnan Province, Yunnan Eye Institute, Affiliated Hospital of Yunnan University, Yunnan University, Kunming, Yunnan, China.
Fei YanDepartment of Pathology and Pathophysiology, Faculty of Basic Medicine School, Kunming Medical University, 1168 Yuhua Street, Chunrong West Road, Chenggong District, Kunming, Yunnan, 650500, China. yanfei@kmmu.edu.cn.ORCID 0009-0002-5612-9041
Kunming Medical University · CNYunnan University · CN

Funding

National Natural Science Foundation of China 82360535
6 · The paper itself

Abstract

Retinal diseases are leading causes of blindness globally. Developing new drugs is of great significance for preventing vision loss. Current drug discovery relies mainly on two-dimensional in vitro models and animal models, but translation to human efficacy and safety is biased. In recent years, the emergence of retinal organoid technology platforms, utilizing three-dimensional microenvironments to better mimic retinal structure and function, has provided new platforms for exploring pathogenic mechanisms and drug screening. This review summarizes the latest advances in retinal organoid technology, emphasizing its application advantages in high-throughput drug screening, efficacy and toxicity evaluation, and translational medicine research. The review also prospects the combination of emerging technologies such as organ-on-a-chip, 3D bioprinting, single cell sequencing, gene editing with retinal organoid technology, which is expected to further optimize retinal organoid models and advance the diagnosis and treatment of retinal diseases.

Indexed as

OrganoidsRetinal DiseasesAnimalsDrug DiscoveryDrug Evaluation, PreclinicalHumansRetinaBioengineeringDrug DiscoveryOrgan-on-a-chipRetinal OrganoidStem Cells

Identifiers

PMID38079086
PMCPMC10837228
OpenAlexW4389574947

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.