Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression
Depression is one of the most common mental illnesses and is a well‐known risk factor for suicide, characterized by low overall efficacy (
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| Veröffentlicht in: | Advanced materials (Weinheim) 2024-07, Vol.36 (28), p.e2312755-n/a |
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| creator | Chen, Xiaonan Wang, Yun Pei, Congcong Li, Rongxin Shu, Weikang Qi, Ziheng Zhao, Yinbing Wang, Yanhui Lin, Yingying Zhao, Liang Peng, Daihui Wan, Jingjing |
| description | Depression is one of the most common mental illnesses and is a well‐known risk factor for suicide, characterized by low overall efficacy ( |
| doi_str_mv | 10.1002/adma.202312755 |
| format | Article |
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A high‐performance platform based on vacancy‐engineered Co3O4 for rapid diagnosis and therapeutic evaluation of depression is developed. The vacancy‐engineered nanoparticles enable up to 20‐fold signal amplification compared to commercial products, featured by superior photoelectric response and photothermal properties. This platform achieves superior diagnostic performance for depression (AUC: 0.941–0.980; Accuracy: 92%−94%), driving progress in advanced matrices toward precision medicine.</description><identifier>ISSN: 0935-9648</identifier><identifier>ISSN: 1521-4095</identifier><identifier>EISSN: 1521-4095</identifier><identifier>DOI: 10.1002/adma.202312755</identifier><identifier>PMID: 38692290</identifier><language>eng</language><publisher>Germany: Wiley Subscription Services, Inc</publisher><subject>Charge transfer ; Cobalt oxides ; depression ; Diagnosis ; laser desorption/ionization mass spectrometry (LDI‐MS) ; Machine learning ; Mental depression ; Metabolism ; Metabolites ; oxygen vacancies ; Performance evaluation ; Photothermal conversion ; therapeutic evaluation</subject><ispartof>Advanced materials (Weinheim), 2024-07, Vol.36 (28), p.e2312755-n/a</ispartof><rights>2024 Wiley‐VCH GmbH</rights><rights>This article is protected by copyright. All rights reserved.</rights><rights>2024 Wiley‐VCH GmbH.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3285-52d5e17af6b16c19861a8f092d5ba91da728a190315b079b92d0de7dc933c29e3</cites><orcidid>0009-0008-2799-905X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fadma.202312755$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fadma.202312755$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38692290$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Chen, Xiaonan</creatorcontrib><creatorcontrib>Wang, Yun</creatorcontrib><creatorcontrib>Pei, Congcong</creatorcontrib><creatorcontrib>Li, Rongxin</creatorcontrib><creatorcontrib>Shu, Weikang</creatorcontrib><creatorcontrib>Qi, Ziheng</creatorcontrib><creatorcontrib>Zhao, Yinbing</creatorcontrib><creatorcontrib>Wang, Yanhui</creatorcontrib><creatorcontrib>Lin, Yingying</creatorcontrib><creatorcontrib>Zhao, Liang</creatorcontrib><creatorcontrib>Peng, Daihui</creatorcontrib><creatorcontrib>Wan, Jingjing</creatorcontrib><title>Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression</title><title>Advanced materials (Weinheim)</title><addtitle>Adv Mater</addtitle><description>Depression is one of the most common mental illnesses and is a well‐known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high‐performance metabolite‐based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy‐engineered cobalt oxide (Vo‐Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy‐prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo‐Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high‐performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941–0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow‐up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.
A high‐performance platform based on vacancy‐engineered Co3O4 for rapid diagnosis and therapeutic evaluation of depression is developed. The vacancy‐engineered nanoparticles enable up to 20‐fold signal amplification compared to commercial products, featured by superior photoelectric response and photothermal properties. This platform achieves superior diagnostic performance for depression (AUC: 0.941–0.980; Accuracy: 92%−94%), driving progress in advanced matrices toward precision medicine.</description><subject>Charge transfer</subject><subject>Cobalt oxides</subject><subject>depression</subject><subject>Diagnosis</subject><subject>laser desorption/ionization mass spectrometry (LDI‐MS)</subject><subject>Machine learning</subject><subject>Mental depression</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>oxygen vacancies</subject><subject>Performance evaluation</subject><subject>Photothermal conversion</subject><subject>therapeutic evaluation</subject><issn>0935-9648</issn><issn>1521-4095</issn><issn>1521-4095</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNqFkcFu1DAQhi1ERZfClSOyxIVLlrG9duLjqttSpFbtoeUaTZxJ61USL_am1d54hD4jT4KrLUXiwmk083_zazQ_Yx8EzAWA_ILtgHMJUglZav2KzYSWoliA1a_ZDKzShTWL6pC9TWkNANaAecMOVWWslBZmbP0dHY5u9-vn4yr6exr5mb-9y90VxS7EIWvEL2iLTei948uUcMezwFceb8eQfOI4tvz6jiJuaNpm5uQe-wm3Pow8dHxFm0gp5e4dO-iwT_T-uR6xm9OT6-Oz4vzy67fj5XnhlKx0oWWrSZTYmUYYJ2xlBFYd2Dxu0IoWS1mhsKCEbqC0TRagpbJ1ViknLakj9nnvu4nhx0RpWw8-Oep7HClMqVagQZTSWJPRT_-g6zDFMV-XqbIqrTSwyNR8T7kYUorU1ZvoB4y7WkD9lEL9lEL9kkJe-PhsOzUDtS_4n7dnwO6BB9_T7j929XJ1sfxr_hu34pVt</recordid><startdate>20240701</startdate><enddate>20240701</enddate><creator>Chen, Xiaonan</creator><creator>Wang, Yun</creator><creator>Pei, Congcong</creator><creator>Li, Rongxin</creator><creator>Shu, Weikang</creator><creator>Qi, Ziheng</creator><creator>Zhao, Yinbing</creator><creator>Wang, Yanhui</creator><creator>Lin, Yingying</creator><creator>Zhao, Liang</creator><creator>Peng, Daihui</creator><creator>Wan, Jingjing</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SR</scope><scope>8BQ</scope><scope>8FD</scope><scope>JG9</scope><scope>7X8</scope><orcidid>https://orcid.org/0009-0008-2799-905X</orcidid></search><sort><creationdate>20240701</creationdate><title>Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression</title><author>Chen, Xiaonan ; Wang, Yun ; Pei, Congcong ; Li, Rongxin ; Shu, Weikang ; Qi, Ziheng ; Zhao, Yinbing ; Wang, Yanhui ; Lin, Yingying ; Zhao, Liang ; Peng, Daihui ; Wan, Jingjing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3285-52d5e17af6b16c19861a8f092d5ba91da728a190315b079b92d0de7dc933c29e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Charge transfer</topic><topic>Cobalt oxides</topic><topic>depression</topic><topic>Diagnosis</topic><topic>laser desorption/ionization mass spectrometry (LDI‐MS)</topic><topic>Machine learning</topic><topic>Mental depression</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>oxygen vacancies</topic><topic>Performance evaluation</topic><topic>Photothermal conversion</topic><topic>therapeutic evaluation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Xiaonan</creatorcontrib><creatorcontrib>Wang, Yun</creatorcontrib><creatorcontrib>Pei, Congcong</creatorcontrib><creatorcontrib>Li, Rongxin</creatorcontrib><creatorcontrib>Shu, Weikang</creatorcontrib><creatorcontrib>Qi, Ziheng</creatorcontrib><creatorcontrib>Zhao, Yinbing</creatorcontrib><creatorcontrib>Wang, Yanhui</creatorcontrib><creatorcontrib>Lin, Yingying</creatorcontrib><creatorcontrib>Zhao, Liang</creatorcontrib><creatorcontrib>Peng, Daihui</creatorcontrib><creatorcontrib>Wan, Jingjing</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Engineered Materials Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>MEDLINE - Academic</collection><jtitle>Advanced materials (Weinheim)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Xiaonan</au><au>Wang, Yun</au><au>Pei, Congcong</au><au>Li, Rongxin</au><au>Shu, Weikang</au><au>Qi, Ziheng</au><au>Zhao, Yinbing</au><au>Wang, Yanhui</au><au>Lin, Yingying</au><au>Zhao, Liang</au><au>Peng, Daihui</au><au>Wan, Jingjing</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression</atitle><jtitle>Advanced materials (Weinheim)</jtitle><addtitle>Adv Mater</addtitle><date>2024-07-01</date><risdate>2024</risdate><volume>36</volume><issue>28</issue><spage>e2312755</spage><epage>n/a</epage><pages>e2312755-n/a</pages><issn>0935-9648</issn><issn>1521-4095</issn><eissn>1521-4095</eissn><abstract>Depression is one of the most common mental illnesses and is a well‐known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high‐performance metabolite‐based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy‐engineered cobalt oxide (Vo‐Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy‐prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo‐Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high‐performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941–0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow‐up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.
A high‐performance platform based on vacancy‐engineered Co3O4 for rapid diagnosis and therapeutic evaluation of depression is developed. The vacancy‐engineered nanoparticles enable up to 20‐fold signal amplification compared to commercial products, featured by superior photoelectric response and photothermal properties. This platform achieves superior diagnostic performance for depression (AUC: 0.941–0.980; Accuracy: 92%−94%), driving progress in advanced matrices toward precision medicine.</abstract><cop>Germany</cop><pub>Wiley Subscription Services, Inc</pub><pmid>38692290</pmid><doi>10.1002/adma.202312755</doi><tpages>13</tpages><orcidid>https://orcid.org/0009-0008-2799-905X</orcidid></addata></record> |
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| subjects | Charge transfer Cobalt oxides depression Diagnosis laser desorption/ionization mass spectrometry (LDI‐MS) Machine learning Mental depression Metabolism Metabolites oxygen vacancies Performance evaluation Photothermal conversion therapeutic evaluation |
| title | Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression |
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