Evidence map›Paper›PMID 38435520›Full record

ArticleACS central science2024

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations.

Haiyang Su, Yuanlin Song, Shouzhi Yang, Ziyue Zhang, Yao Shen, Lan Yu, Shujing Chen, Lei Gao, Cuicui Chen, Dongni Hou and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS central science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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  4. Review
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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

16 authors.

Haiyang SuState Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
Yuanlin SongDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.
Shouzhi YangState Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
Ziyue ZhangState Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.
Yao ShenDepartment of Respiratory and Critical Care Medicine, Shanghai Pudong Hospital, Fudan University, Shanghai 201399, P. R. China.
Lan YuClinical Medical Research Center, Inner Mongolia People's Hospital, Hohhot 010017, Inner Mongolia, P. R. China.
Shujing ChenDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.
Lei GaoDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.
Cuicui ChenDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.
Dongni HouDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.
Xinping WeiShanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai 201102, P. R. China.
Xuedong MaShanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai 201102, P. R. China.
Pengyu HuangShanghai Minhang District Gumei Community Health Center affiliated with Fudan University, Shanghai 201102, P. R. China.
Dejun SunInner Mongolia Key Laboratory of Gene Regulation of The Metabolic Disease, Inner Mongolia People's Hospital, Hohhot 010017, Inner Mongolia, P. R. China.
Jian ZhouDepartment of Pulmonary and Critical Care Medicine, Shanghai Respiratory Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, P. R. China.ORCID https://orcid.org/0000-0002-0119-8990
Kun QianState Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200030, P. R. China.ORCID https://orcid.org/0000-0003-1666-1965

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

Identifiers

PMID38435520
PMCPMC10906255

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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.