Vacancy‐Driven High‐Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

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Abstract
Depression is one of the most common mental illnesses and is a well‐known risk factor for suicide, characterized by low overall efficacy (3O4) 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.
Funding Information
  • National Natural Science Foundation of China (22074044, 22122404)
  • National Key Research and Development Program of China (2022YFC2502801)

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