Evidence map›Paper›PMID 41938519›Full record

ArticleInternational journal of medical sciences2026

Single-cell Profiling Reveals Cooperative Participation of

Yingjiao Ju, Jingyi Yao, Song Zhang, Jiangxu Wu, Jiongao Xiang, Li Min, Mingyuan Liu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of medical sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Yingjiao JuResearch Center, Beijing Friendship Hospital, Capital Medical University, Beijing 101300, China.
Jingyi YaoResearch Center, Beijing Friendship Hospital, Capital Medical University, Beijing 101300, China.
Song ZhangResearch Center, Beijing Friendship Hospital, Capital Medical University, Beijing 101300, China.
Jiangxu WuBiobank, Beijing Friendship Hospital, Capital Medical University, Beijing 101300, China.
Jiongao XiangDepartment of Vascular Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.
Li MinResearch Center, Beijing Friendship Hospital, Capital Medical University, Beijing 101300, China.
Mingyuan LiuDepartment of Vascular Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thoracic aortic aneurysm (TAA) is a life-threatening condition characterized by aortic dilation, inflammation, and extracellular matrix degradation. Despite advances in surgical management, effective pharmacological therapies are still lacking, largely due to an incomplete understanding of the cellular mechanisms driving disease progression. Although recent single-cell RNA sequencing (scRNA-seq) studies have revealed diverse cell types in TAA, the intercellular communication driving pathological remodeling is still poorly defined. Here, we performed integrated scRNA-seq analysis of human TAA (n = 8) and healthy aorta (n = 8) to construct a comprehensive cellular landscape. We identified a disease-associated crosstalk between

Indexed as

Aortic Aneurysm, ThoracicInflammationOsteopontinCell Adhesion MoleculesCell CommunicationFibroblastsHumansMyeloid CellsPeriostinSignal TransductionSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisCell Adhesion MoleculesOsteopontinPeriostinPOSTN protein, humanSPP1 protein, humanaortic wall remodelingmyeloid-fibroblast crosstalksingle-cell RNA sequencingSPP1/MK signalingThoracic aortic aneurysmtranscriptional regulation

Identifiers

PMID41938519
PMCPMC13048888

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.