Evidence map›Paper›PMID 40093027›Full record

ReviewKidney diseases (Basel, Switzerland)

Progress of Induced Pluripotent Stem Cell-Derived Renal Organoids in Clinical Application.

Na Ning, Zhiting Liu, Xinyu Li, Yi Liu, Wei Song

Abstract readReview
In one paragraph

Review in Kidney diseases (Basel, Switzerland). 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. Review
  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

5 authors.

Na NingDepartment of Minimally Invasive Comprehensive Treatment of Cancer, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Zhiting LiuDepartment of Minimally Invasive Comprehensive Treatment of Cancer, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Xinyu LiDepartment of Minimally Invasive Comprehensive Treatment of Cancer, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Yi LiuPediatric Research Institute, Children's Hospital Affiliated to Shandong University, Jinan, China.
Wei SongDepartment of Minimally Invasive Comprehensive Treatment of Cancer, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Kidney disease has become a growing public health problem worldwide, and there is an urgent need to develop reliable models for investigating novel and effective treatment strategies. In recent years, kidney organoids, as novel models different from traditional two-dimensional cells and model animals, have attracted more and more attention. Current advances have allowed the generation of kidney organoids from the directed differentiation of induced pluripotent stem cells (iPSCs), which possess similar characteristics to embryonic stem cells, but bypass ethical constraints and have a wide range of sources. Summary: Herein, the methods of generating renal organoids from iPSCs, the applications of iPSC-derived renal organoids in disease modeling, drug effectiveness detection, and regenerative medicine as well as the challenges were reviewed. Key Messages: iPSC-derived renal organoids can be used to model kidney diseases and are great models for studying kidney injury and toxicity. Many efforts are needed to finally apply organoids into clinical application.

Indexed as

Drug screenHuman-induced pluripotent stem cellsKidney organoidsThree-dimensional model

Identifiers

PMID40093027
PMCPMC11908814

What OpenQuestion holds

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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.