Evidence mapPaperPMID 41789675Full record

ReviewInternational journal of molecular medicine2026

Research progress in single‑cell omics technologies for kidney disease (Review).

Jia Xu, Ziwei Chen, Shengyao Li, Xiaoya Wang, Ming Chen

Abstract readReview
In one paragraph

Review in International journal of molecular medicine, 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

5 authors.

Jia Xu *Hospital of Chengdu University of Traditional Chinese Medicine, School of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610075, P.R. China.
Ziwei Chen *School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 611137, P.R. China.
Shengyao LiDepartment of Nephrology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, P.R. China.
Xiaoya WangDepartment of Nephrology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, P.R. China.
Ming ChenDepartment of Nephrology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan 610072, P.R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The kidney is a vital organ for maintaining metabolic balance within the body and facilitating excretion. Its complex tissue structure comprises diverse cell types, including glomerular, tubular, interstitial and immune cells. The highly differentiated nature of these cells presents challenges for investigating kidney disease mechanisms. In recent years, the rapid advancement of single‑cell omics technologies has provided novel perspectives for renal research. These techniques have revealed the diversity and heterogeneity of renal cells, enabling precise identification of multiple immune cell types within the kidney. These findings further elucidate the dynamic changes in renal immune cells during disease progression and their interactions with other renal cells, laying a foundation for in‑depth analysis of renal disease pathogenesis. The present review aims to summarize the current applications of single‑cell omics technologies in renal ageing and kidney diseases, providing crucial insights for deciphering disease mechanisms and identifying therapeutic targets.

Indexed as

GenomicsKidney DiseasesSingle-Cell AnalysisAnimalsBiomarkersHumansKidneyMultiomicsProteomicsBiomarkersbiomarkercellular senescenceimmune mechanismkidney diseasessingle‑cell omics technologiestargeted therapy

Identifiers

PMID41789675
PMCPMC12974334

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.