Evidence map›Paper›PMID 41209382›Full record

ArticleJournal of inflammation research2025

Retinoic Acid-Loaded Cartilage Organoids Attenuate Chondrocyte Senescence in Osteoarthritis.

Liang Xi, Yongfeng Chen, Zhuojing Luo, Dawei Zhang

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. 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

4 authors.

Liang Xi *Department of Orthopaedics, Xijing Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.
Yongfeng Chen *Department of Orthopaedics, Xijing Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.
Zhuojing LuoDepartment of Orthopaedics, Xijing Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.
Dawei ZhangDepartment of Orthopaedics, Xijing Hospital, Fourth Military Medical University, Xi'an, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Osteoarthritis (OA) is a debilitating degenerative joint disease characterized by chondrocyte senescence and cartilage degradation. Despite extensive research, effective therapeutic strategies targeting the underlying mechanisms of chondrocyte senescence remain limited. Methods: We employed an integrated multi-omics approach combining weighted gene co-expression network analysis (WGCNA) and machine learning algorithms with the SenMayo gene set to identify key senescence-associated genes in OA. Single-cell RNA sequencing was used to characterize distinct chondrocyte subpopulations. Computational screening, molecular docking, and dynamics simulations identified potential therapeutic compounds. We engineered a triphasic gelatin methacryloyl/hyaluronic acid methacryloyl (GelMA/HAMA) cartilage organoid system for controlled delivery of retinoic acid (RA) and evaluated its efficacy in vitro and in a rat destabilization of the medial meniscus (DMM) model of OA. Results: Our bioinformatic analysis identified Conclusion: Our findings establish RA delivered via biomimetic cartilage organoids as a promising therapeutic strategy that addresses the cellular mechanisms underlying OA progression. This approach may represent a paradigm shift from symptom management to disease modification by targeting chondrocyte senescence and promoting cartilage regeneration, offering new avenues for developing effective treatments for OA.

Indexed as

cartilage organoidosteoarthritisretinoic acidsenescenceTGFβ

Identifiers

PMID41209382
PMCPMC12595992

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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.