Evidence map›Paper›PMID 41601983›Full record

ArticleFrontiers in pharmacology2025

Serum valeric acid stimulates lung epithelial cilia assembly and improves prognosis in patients with severe respiratory infections.

Yabin Zhang, Yuqing Zhang, Lin Wang, Beibei Liu, Jiajia Zheng, Jiabao Cao, Lixin Xie, Ning Shen, Jun Wang

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. 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. Article
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

9 authors.

Yabin ZhangShandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.
Yuqing ZhangCAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
Lin WangSchool of Medicine, Nankai University, Tianjin, China.
Beibei LiuDepartment of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Jiajia ZhengDepartment of Laboratory Medicine, Peking University Third Hospital, Beijing, China.
Jiabao CaoCAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
Lixin XieSchool of Medicine, Nankai University, Tianjin, China.
Ning ShenDepartment of Pulmonary and Critical Care Medicine, Peking University Third Hospital, Beijing, China.
Jun WangShandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Globally, respiratory infections remain a leading cause of mortality, with treatment efficacy increasingly challenged by antimicrobial resistance. This study aimed to investigate the role of serum metabolites in the prognosis of severe human pneumonia. Methods: Untargeted and targeted serum metabolomics were performed on intensive care unit (ICU) patients. Experimental validation was conducted in a murine bacterial infection model and cellular models. RNA sequencing was used for mechanistic exploration to identify the signaling pathways regulated by the key metabolite. Results: Valeric acid, a short-chain fatty acid, was significantly elevated in survivors compared with non-survivors of severe pneumonia. In the murine Discussion: The findings establish a protective role for the gut microbiome-derived valeric acid in respiratory infections via the novel HDAC-Wnt-FOXJ1 axis, revealing its potential as a therapeutic agent to improve clinical outcomes.

Indexed as

ciliametabolomicssevere pneumoniavaleric acidWnt pathway

Identifiers

PMID41601983
PMCPMC12832812

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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.