Evidence map›Paper›PMID 35509094›Full record

ArticleBMC pulmonary medicine2022

Disturbance of serum lipid metabolites and potential biomarkers in the Bleomycin model of pulmonary fibrosis in young mice.

Xiao-Hui Yang, Fang-Fang Wang, Xiao-Sa Chi, Xiao-Meng Wang, Jin-Peng Cong, Yi Hu, Yu-Zhu Zhang

Open access · goldAbstract read
In one paragraph

Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

6 citing papers in PubMed, 21 citations in OpenAlex.

  1. Machine Learning-Driven Discovery of TRIM Genes as Diagnostic Biomarkers for Idiopathic Pulmonary Fibrosis.Medical science monitor : international medical journal of experimental and clinical research · 2025
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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 at 2 institutions in 1 country.

Xiao-Hui YangDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Fang-Fang WangDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Xiao-Sa ChiDepartment of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, 266000, People's Republic of China.
Xiao-Meng WangDepartment of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, 266000, People's Republic of China.
Jin-Peng CongDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital of Qingdao University, Qingdao, People's Republic of China.
Yi HuDepartment of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, 266000, People's Republic of China.
Yu-Zhu ZhangDepartment of Geriatrics, Affiliated Hospital of Qingdao University, Qingdao, 266000, People's Republic of China. zhangyz0901@qdu.edu.cn.
Affiliated Hospital of Qingdao University · CNQingdao University · CN

Funding

National Natural Science Foundation of China 81901321National Natural Science Foundation of China 82000268Natural Science Foundation of Shandong Province ZR202103030823
6 · The paper itself

Abstract

backgroundAltered metabolic pathways have recently been considered as potential drivers of idiopathic pulmonary fibrosis (IPF) for the study of drug therapeutic targets. However, our understanding of the metabolite profile during IPF formation is lacking.

methodsTo comprehensively characterize the metabolic disorders of IPF, a mouse IPF model was constructed by intratracheal injection of bleomycin into C57BL/6J male mice, and lung tissues from IPF mice at 7 days, 14 days, and controls were analyzed by pathology, immunohistochemistry, and Western Blots. Meanwhile, serum metabolite detections were conducted in IPF mice using LC-ESI-MS/MS, KEGG metabolic pathway analysis was applied to the differential metabolites, and biomarkers were screened using machine learning algorithms.

resultsWe analyzed the levels of 1465 metabolites and found that more than one-third of the metabolites were altered during IPF formation. There were 504 and 565 metabolites that differed between M7 and M14 and controls, respectively, while 201 differential metabolites were found between M7 and M14. In IPF mouse sera, about 80% of differential metabolite expression was downregulated. Lipids accounted for more than 80% of the differential metabolite species with down-regulated expression. The KEGG pathway enrichment analysis of differential metabolites was mainly enriched to pathways such as the metabolism of glycerolipids and glycerophospholipids. Eight metabolites were screened by a machine learning random forest model, and receiver operating characteristic curves (ROC) assessed them as ideal diagnostic tools.

conclusionsIn conclusion, we have identified disturbances in serum lipid metabolism associated with the formation of pulmonary fibrosis, contributing to the understanding of the pathogenesis of pulmonary fibrosis.

Indexed as

BleomycinIdiopathic Pulmonary FibrosisAnimalsBiomarkersDisease Models, AnimalGlycerophospholipidsHumansMaleMiceMice, Inbred C57BLTandem Mass SpectrometryBiomarkersBleomycinGlycerophospholipidsBiomarkersMetabolitesMicePulmonary fibrosis

Identifiers

PMID35509094
PMCPMC9066762
OpenAlexW4225397332

What OpenQuestion holds

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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.