Evidence map›Paper›PMID 38937834›Full record

ReviewStem cell research & therapy2024

Ameliorating and refining islet organoids to illuminate treatment and pathogenesis of diabetes mellitus.

Yushan Li, Meiqi Xu, Jiali Chen, Jiansong Huang, Jiaying Cao, Huajing Chen, Jiayi Zhang, Yukun Luo, Yazhuo Wang, Jia Sun

Abstract readReview
In one paragraph

Review in Stem cell research & therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

10 authors.

Yushan Li *Department of Endocrinology, Zhujiang Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Meiqi Xu *Department of Biomedical Engineering, Shanxi Medical University, Taiyuan, Shanxi, 030001, China.
Jiali Chen *Department of Endocrinology, Zhujiang Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Jiansong Huang *Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Jiaying CaoNanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Huajing ChenNanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Jiayi ZhangNanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Yukun LuoDepartment of Endocrinology, Zhujiang Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China.
Yazhuo WangTsinghua-Peking Center for Life Sciences, School of Basic Medical Sciences, Tsinghua University, Beijing, China. yz-wang23@mails.tsinghua.edu.cn.ORCID 0000-0002-0484-4185
Jia SunDepartment of Endocrinology, Zhujiang Hospital, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China. sunjia@smu.edu.cn.

Funding

Basic and Applied Basic Research Fund of Guangdong Province No. 2019A1515111113China Postdoctoral Science Foundation No. 2020 M682626Frontier Research Program of Guangzhou Regenerative Medicine and Health Guangdong Laboratory No.2018GZR110105014Guangdong Basic and Applied Basic Research Foundation 2022A1515010244Guangdong Students' Platform for Innovation and Entrepreneurship Training Program 202212121023, S202212121074High-level University Construction Funding of Guangdong Provincial Department of Education No.LC2016ZD027National Key Research and Development Program of China No.2017YFA0105503National Natural Science Foundation of China 82270941Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation No. pdjh2021b0101Special Funds for the Cultivation of Guangdong College Students' Scientific and Technological Innovation (No. pdjh2021b0101 and No. pdjh2022b0096
6 · The paper itself

Abstract

Diabetes mellitus, a significant global public health challenge, severely impacts human health worldwide. The organoid, an innovative in vitro three-dimensional (3D) culture model, closely mimics tissues or organs in vivo. Insulin-secreting islet organoid, derived from stem cells induced in vitro with 3D structures, has emerged as a potential alternative for islet transplantation and as a possible disease model that mirrors the human body's in vivo environment, eliminating species difference. This technology has gained considerable attention for its potential in diabetes treatment. Despite advances, the process of stem cell differentiation into islet organoid and its cultivation demonstrates deficiencies, prompting ongoing efforts to develop more efficient differentiation protocols and 3D biomimetic materials. At present, the constructed islet organoid exhibit limitations in their composition, structure, and functionality when compared to natural islets. Consequently, further research is imperative to achieve a multi-tissue system composition and improved insulin secretion functionality in islet organoid, while addressing transplantation-related safety concerns, such as tumorigenicity, immune rejection, infection, and thrombosis. This review delves into the methodologies and strategies for constructing the islet organoid, its application in diabetes treatment, and the pivotal scientific challenges within organoid research, offering fresh perspectives for a deeper understanding of diabetes pathogenesis and the development of therapeutic interventions.

Indexed as

Islets of LangerhansIslets of Langerhans TransplantationOrganoidsAnimalsCell DifferentiationDiabetes MellitusHumans3D cultureDiabetesOrganoidOrgan transplantationStem cells

Identifiers

PMID38937834
PMCPMC11210168

What OpenQuestion holds

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Registered trials

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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.