Ultra‐Fast Label‐Free Serum Metabolic Diagnosis of Coronary Heart Disease via a Deep Stabilizer

Abstract

Although mass spectrometry (MS) of metabolites has the potential to provide real-time monitoring of patient status for diagnostic purposes, the diagnostic application of MS is limited due to sample treatment and data quality/ reproducibility. Here, the generation of a deep stabilizer for ultra-fast, label-free MS detection and the application of this method for serum metabolic diagnosis of coronary heart disease (CHD) are reported. Nanoparticle-assisted laser desorption/ionization-MS is used to achieve direct metabolic analysis of trace unprocessed serum in seconds. Furthermore, a deep stabilizer is constructed to map native MS results to high-quality results obtained by established methods. Finally, using the newly developed protocol and diagnosis variation characteristic surface to characterize sensitivity/ specificity and variation, CHD is diagnosed with advanced accuracy in a high-throughput/speed manner. This work advances design of metabolic analysis tools for disease detection as it provides a direct label-free, ultra-fast, and stabilized platform for future protocol development in clinics.

DeepThy‐Net: A Multimodal Deep Learning Method for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer

“…Although deep learning has achieved exciting diagnosis results in many diseases, such as lung cancer and fundus diseases, doctors still prefer transparent, understandable, and interpretable diagnostic models to better gain the confidence of supervisors and patients. [34][35][36] In the future, we plan to introduce technologies such as explainable artificial intelligence (XAI) to improve the interpretability of our model. For example, we could use XAI and other technologies to analyze the changing rules of convolutional feature maps, and further obtain a more transparent and interpretable model.…”

Nanomaterials‐assisted metabolic analysis toward in vitro diagnostics

“…[ 89,109–114 ] Especially, Qian's group was dedicated to developing novel nanomaterials and their conjugation with LDI MS systems for the untargeted metabolic fingerprinting of blood samples. [ 15,89,91,109,115 ] For example, Vedarethinam et al. extracted the plasma metabolic fingerprints using LDI MS, which was assisted by V 2 O 5 nanorods for enhanced sensitivity.…”

“…[89,[109][110][111][112][113][114] Especially, Qian's group was dedicated to developing novel nanomaterials and their conjugation with LDI MS systems for the untargeted metabolic fingerprinting of blood samples. [15,89,91,109,115] For example, Vedarethinam et al extracted the plasma metabolic fingerprints using LDI MS, which was assisted by V 2 O 5 nanorods F I G U R E  (A) Metabolic changes inspected by vanadium core-shell nanorods fordiagnosing diabetic retinopathy. Particularly, vanadium core-shell nanorods acted as matrices of LDI MS to improve the detection sensitivity of plasma metabolites.…”

Sorption of Fulvic Acids onto Titanium Dioxide Nanoparticles Extracted from Commercial Sunscreens: ToF-SIMS and High-Dimensional Data Analysis

“…For instance, Aoyagi et al [47] conducted an interlaboratory study on the identification of the peptide sample ToF-SIMS data by machine learning where the spectra of the test peptide sample were predicted by random forest. Also, in medical studies, Zhang et al [48] used machine learning algorithms on serum blueprints extracted from LDI-MS for the diagnosis of coronary heart disease. In this study, we tried to elucidate the capability of ToF-SIMS supported by high-dimensional data analysis to fill the gap between the sorption of NOM onto model nanoparticles usually used in studies (e.g., P25) and the more complex particles found in commercial products like sunscreens.…”