Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations
© 2024 The Authors. Published by American Chemical Society..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:10 |
|---|---|
| Enthalten in: |
ACS central science - 10(2024), 2 vom: 28. Feb., Seite 331-343 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Su, Haiyang [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Revised 05.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1021/acscentsci.3c01201 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM369253906 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM369253906 | ||
| 003 | DE-627 | ||
| 005 | 20240305232753.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240304s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1021/acscentsci.3c01201 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1317.xml |
| 035 | |a (DE-627)NLM369253906 | ||
| 035 | |a (NLM)38435520 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Su, Haiyang |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 05.03.2024 | ||
| 500 | |a published: Electronic-eCollection | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © 2024 The Authors. Published by American Chemical Society. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 700 | 1 | |a Song, Yuanlin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Shouzhi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Ziyue |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Yao |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Lan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Shujing |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Lei |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Cuicui |e verfasserin |4 aut | |
| 700 | 1 | |a Hou, Dongni |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Xinping |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Xuedong |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Pengyu |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Dejun |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jian |e verfasserin |4 aut | |
| 700 | 1 | |a Qian, Kun |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t ACS central science |d 2015 |g 10(2024), 2 vom: 28. Feb., Seite 331-343 |w (DE-627)NLM251686884 |x 2374-7943 |7 nnns |
| 773 | 1 | 8 | |g volume:10 |g year:2024 |g number:2 |g day:28 |g month:02 |g pages:331-343 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1021/acscentsci.3c01201 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 10 |j 2024 |e 2 |b 28 |c 02 |h 331-343 | ||