Hollow Crystallization COF Capsuled MOF Hybrids Depict Serum Metabolic Profiling for Precise Early Diagnosis and Risk Stratification of Acute Coronary Syndrome

Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management

Acute coronary syndrome (ACS) is a leading cause of death worldwide. Prompt and accurate diagnosis of acute myocardial infarction (AMI) or ACS is crucial for improved management and prognosis of patients. The rapid growth of machine learning (ML) research has significantly enhanced our understanding of ACS. Most studies have focused on applying ML to detect ACS, predict prognosis, manage treatment, identify risk factors, and discover potential biomarkers, particularly using data from electrocardiograms (ECGs), electronic medical records (EMRs), imaging, and omics as the main data modality. Additionally, integrating ML with smart devices such as wearables, smartphones, and sensor technology enables real-time dynamic assessments, enhancing clinical care for patients with ACS. This review provided an overview of the workflow and key concepts of ML as they relate to ACS. It then provides an overview of current ML algorithms used for ACS diagnosis, prognosis, identification of potential risk biomarkers, and management. Furthermore, we discuss the current challenges faced by ML algorithms in this field and how they might be addressed in the future, especially in the context of medicine.

Gold-Modified Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry for Analysis of Metabolites Induced by Triclosan Exposure

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) holds great promise for the rapid and sensitive detection of biomolecules, but its precise detection of small molecule metabolites is hindered by severe background interference from the organic matrix in the low molecular weight range. To address this issue, nanomaterials have commonly been utilized as substrates in LDI-MS. Among them, covalent organic frameworks (COFs), known for their unique optical absorption and structural properties, have garnered significant attention. Despite these advantages, their low ionization efficiency remains a challenge. Herein, a composite material of COF-S@Au nanoparticles (NPs), by incorporating Au NPs into a sulfur-functionalized COF (COF-S) through postsynthetic modification, was designed and adopted as substrates. This hybrid material leverages the synergistic effects of COF-S and Au NPs to improve the desorption/ionization efficiency and minimize background interference. The COF-S@Au NPs demonstrated a 5-16-fold improvement in MS signals of small biomolecules along with a clean background and excellent resistance to salt and protein interference. Their corresponding limits of detection (LODs) were achieved at ∼pmol. Furthermore, the COF-S@Au NPs were applied to analyze metabolites in a triclosan (TCS)-exposed mouse model, successfully identifying 10 differential metabolites associated with TCS toxicity. This work provides a foundation for developing advanced LDI-MS materials for high-performance metabolic analysis and offers valuable insights into TCS metabolic toxicity with potential applications in environmental toxicology.

Hierarchical Porous Microspheres-Assisted Serum Metabolic Profile for the Early Diagnosis and Surveillance of Postmenopausal Osteoporosis

With the aging global population, the incidence of osteoporosis (OP) is increasing, putting more individuals at risk. Since postmenopausal osteoporosis (PMOP) often remains asymptomatic until a fracture occurs, making the early clinical diagnosis of PMOP particularly challenging. In this work, the AuNPs-anchored hierarchical porous ZrO2 microspheres (Au/HPZOMs) is designed to assist laser desorption/ionization mass spectrometry (LDI-MS) for the requirement of serum metabolic fingerprints of PMOP, postmenopausal osteopenia (PMON), and healthy controls (HC) and realize the early diagnosis and surveillance of PMOP. With its large surface area, suitable surface roughness, and enhanced UV absorbance, the LDI efficiency of Au/HPZOMs is significantly enhanced. Combining machine learning, PMOP and non-PMOP (HC and PMON) are clearly distinguished with the area under the receiver operating characteristic curves reaching up to 1.000. Furthermore, seven key m/z features are identified, facilitating the specific detection of PMON and two stages of PMOP. The precision of distinguishing PMON and PMOP at different stages based on these features exceeds 86.5% in both the training and validation sets, aiding in the early diagnosis and monitoring of PMOP. This work sheds light on the metabolic profile for large-scale screening, early detection, and monitoring of PMOP, which will promote the application of fluid metabolism-driven precision medicine into practical clinical use.

Group IV Bimetallic MOFs Engineering Enhanced Metabolic Profiles Co-Predict Liposarcoma Recognition and Classification

The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis and management. In this work, a series of metal-organic frameworks (MOFs) engineering is designed and implemented. Through comprehensive characterization and performance evaluations, such as stability, thermal-driven desorption efficiency, as well as energy- and charge-transfer capacity, the engineering of group IV bimetallic MOFs emerges as particularly noteworthy. This is especially true for their derivative products, which exhibit superior performance across a range of laser desorption/ionization mass spectrometry (LDI MS) performance tests, including those involving practical sample assessments. The top-performing product is utilized to enable high-throughput recording of LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both the LPSrecognizer and LPSclassifier are developed, achieving accurate recognition and classification of LPS with area under the curves (AUCs) of 0.900-1.000. Simplified versions are also developed of the LPSrecognizer and LPSclassifier by screening metabolic biomarker panels, achieving considerable predictive performance, and conducting basic pathway exploration. The work highlights the MOFs engineering for the matrix design and their potential application in developing metabolic analysis and screening tools for rare diseases in clinical settings.

Spherical covalent-organic framework-assisted laser desorption ionization mass spectrometry reveals the promotional effect of triphenyl phosphate on breast cancer in mice

Triphenyl phosphate (TPP), a wide-used organophosphate flame retardants (OPFRs), is suspected to be a risk factor for the female-specific cancers, but underlying toxicity mechanisms of environmentally relevant dose exposure remain unclear. Herein, a strategy of spherical covalent organic framework (TPB-BPTP-COF)-assisted laser desorption ionization mass spectrometry (LDI-MS), which benefited from fast analysis speed, facile sample preprocessing, and high throughput, was proposed for unveiling the biomarkers of breast cancer (BC) and the relationship between TPP exposure and progression of BC in mice by serum metabolism analysis. The results displayed that 13 metabolites associated with BC development were up-regulated in experimental group versus healthy control mice. Moreover, long-term exposure to environmentally relevant doses of TPP was found to promote BC, mainly by affecting glycolysis/gluconeogenesis, pyrimidine metabolism, pantothenic acid and CoA biosynthesis, and β-alanine metabolism. This work proved the potential application of COFs as LDI-MS substrates in analyzing complex biological samples, and also revealed the risk of long-term low-dose exposure to TPP in the development of BC.

Automated Diagnosis and Phenotyping of Tuberculosis Using Serum Metabolic Fingerprints

Tuberculosis (TB) stands as the second most fatal infectious disease after COVID-19, the effective treatment of which depends on accurate diagnosis and phenotyping. Metabolomics provides valuable insights into the identification of differential metabolites for disease diagnosis and phenotyping. However, TB diagnosis and phenotyping remain great challenges due to the lack of a satisfactory metabolic approach. Here, a metabolomics-based diagnostic method for rapid TB detection is reported. Serum metabolic fingerprints are examined via an automated nanoparticle-enhanced laser desorption/ionization mass spectrometry platform outstanding by its rapid detection speed (measured in seconds), minimal sample consumption (in nanoliters), and cost-effectiveness (approximately $3). A panel of 14 m z-1 features is identified as biomarkers for TB diagnosis and a panel of 4 m z-1 features for TB phenotyping. Based on the acquired biomarkers, TB metabolic models are constructed through advanced machine learning algorithms. The robust metabolic model yields a 97.8% (95% confidence interval (CI), 0.964-0.986) area under the curve (AUC) in TB diagnosis and an 85.7% (95% CI, 0.806-0.891) AUC in phenotyping. In this study, serum metabolic biomarker panels are revealed and develop an accurate metabolic tool with desirable diagnostic performance for TB diagnosis and phenotyping, which may expedite the effective implementation of the end-TB strategy.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

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 (<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.

Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling

It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.

High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a high-throughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Covalent Organic Frameworks: Opportunities for Rational Materials Design in Cancer Therapy

Nanomedicines are extensively used in cancer therapy. Covalent organic frameworks (COFs) are crystalline organic porous materials with several benefits for cancer therapy, including porosity, design flexibility, functionalizability, and biocompatibility. This review examines the use of COFs in cancer therapy from the perspective of reticular chemistry and function-oriented materials design. First, the modification sites and functionalization methods of COFs are discussed, followed by their potential as multifunctional nanoplatforms for tumor targeting, imaging, and therapy by integrating functional components. Finally, some challenges in the clinical translation of COFs are presented with the hope of promoting the development of COF-based anticancer nanomedicines and bringing COFs closer to clinical trials.

Serum Metabolic Fingerprints Characterize Systemic Lupus Erythematosus

Metabolic fingerprints in serum characterize diverse diseases for diagnostics and biomarker discovery. The identification of systemic lupus erythematosus (SLE) by serum metabolic fingerprints (SMFs) will facilitate precision medicine in SLE in an early and designed manner. Here, a discovery cohort of 731 individuals including 357 SLE patients and 374 healthy controls (HCs), and a validation cohort of 184 individuals (SLE/HC, 91/93) are constructed. Each SMF is directly recorded by nano-assisted laser desorption/ionization mass spectrometry (LDI MS) within 1 minute using 1 µL of native serum, which contains 908 mass to charge features. Sparse learning of SMFs achieves the SLE identification with sensitivity/specificity and area-under-the-curve (AUC) up to 86.0%/92.0% and 0.950 for the discovery cohort. For the independent validation cohort, it exhibits no performance loss by affording the sensitivity/specificity and AUC of 89.0%/100.0% and 0.992. Notably, a metabolic biomarker panel is screened out from the SMFs, demonstrating the unique metabolic pattern of SLE patients different from both HCs and rheumatoid arthritis patients. In conclusion, SMFs characterize SLE by revealing its unique metabolic pattern. Different regulation of small molecule metabolites contributes to the precise diagnosis of autoimmune disease and further exploration of the pathogenic mechanisms.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.

Organic metal chalcogenide-assisted metabolic molecular diagnosis of central precocious puberty

Metabolic analysis in biofluids based on laser desorption/ionization mass spectrometry (LDI-MS), featuring rapidity, simplicity, small sample volume and high throughput, is expected to be a powerful diagnostic tool. Nevertheless, the signals of most metabolic biomarkers obtained by matrix-assisted LDI-MS are too limited to achieve a highly accurate diagnosis due to serious background interference. To address this issue, nanomaterials have been frequently adopted in LDI-MS as substrates. However, the "trial and error" approach still dominates the development of new substrates. Therefore, rational design of novel LDI-MS substrates showing high desorption/ionization efficiency and no background interference is extremely desired. Herein, four few-layered organic metal chalcogenides (OMCs) were precisely designed and for the first time investigated as substrates in LDI-MS, which allowed a favorable internal energy and charge transfer by changing the functional groups of organic ligands and metal nodes. As a result, the optimized OMC-assisted platform satisfyingly enhanced the mass signal by ≈10 000 fold in detecting typical metabolites and successfully detected different saccharides. In addition, a high accuracy diagnosis of central precocious puberty (CPP) with potential biomarkers of 12 metabolites was realized. This work is not only expected to provide a universal detection tool for large-scale clinical diagnosis, but also provides an idea for the design and selection of LDI-MS substrates.

Next Generation of Mass Spectrometry Imaging: from Micrometer to Subcellular Resolution

Developing an imaging method with micrometer-to-subcellular resolution is of great significance for visualizing biological samples of different sizes. The label-free and high-throughput mass spectrometry imaging (MSI) technology has shown potential in the implementation of this view. Despite many improvements in MSI witnessed over the past decades, it remains a challenge to achieve a flexible resolution from micrometer down to subcellular level with high detection sensitivity. In this Perspective, we focus on the recent development of MSI techniques based on different ionization resources. Furthermore, several designs of instruments and applications in bioimaging have been reviewed and compared. Additionally, we proposed the perspectives and challenges for MSI methods, including pursuing the matrix free and multiscale resolution with high detection sensitivity and deeply combining machine learning in omics research.