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.

A non-targeting magnetic metal-organic framework probe for highly specific peptide-mediated precise disease monitoring.

Alzheimer's disease (AD) is a universal neurodegenerative disease in older adults with incurable and progressive properties, urging for precise monitoring to perform timely treatment to delay its progression. Herein, we introduced a non-targeting magnetic metal-organic framework probe coupled with high-throughput mass spectrometry, creating a rapid screening strategy for highly specific peptides associated with AD. Notably, an elution-free extraction process was proposed, significantly reducing sample preprocessing time while simultaneously ensuring the efficient detection of captured peptides. Using this elution-free extraction process, high-quality peptide profiles were rapidly extracted from the hundreds of samples from both diseased and healthy individuals. By integrating machine learning algorithms, LC-MS/MS, and Uniprot database searching, we identified three specific serum endogenous peptides (m/z = 4215.41, 2884.77 and 2704.61) closely associated with AD. Remarkably, with the use of any single specific peptide, the AUC (Area Under the Curve) values can reach approximately 0.9 during monitoring AD. Moreover, integrating three specific biomarkers provides a robust basis for machine learning algorithms to build monitoring models, with AUC value up to 1.000. This work represents a substantial advancement in the development of peptide-specific precise monitoring approaches for complex diseases, serving as a catalyst for increased dedication to the molecular detection field.

Straightforward Creation of Multishell Hollow Hybrids for an Integrated Metabolic Monitoring System in Disease Management.

Multidimensional metabolic analysis has become a new trend in establishing efficient disease monitoring systems, as the constraints associated with relying solely on a single dimension in refined monitoring are increasingly pronounced. Here, coordination polymers are employed as derivative precursors to create multishell hollow hybrids, developing an integrated metabolic monitoring system. Briefly, metabolic fingerprints are extracted from hundreds of serum samples and urine samples, encompassing not only membranous nephropathy but also related diseases, using high-throughput mass spectrometry. With optimized algorithm and initial feature selection, the established combined panel demonstrates enhanced accuracy in both subtype differentiation (over 98.1%) and prognostic monitoring (over 95.6%), even during double blind test. This surpasses the serum biomarker panel (≈90.7% for subtyping, ≈89.7% for prognosis) and urine biomarker panel (≈94.4% for subtyping, ≈76.5% for prognosis). Moreover, after attempting to further refine the marker panel, the blind test maintains equal sensitivity, specificity, and accuracy, showcasing a comprehensive improvement over the single-fluid approach. This underscores the remarkable effectiveness and superiority of the integrated strategy in discriminating between MN and other groups. This work has the potential to significantly advance diagnostic medicine, leading to the establishment of more effective strategies for patient management.

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma.

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

An integrated methodology for quality assessment of therapeutic antibodies with potential long circulation half-life in harvested cell culture fluid using FcRn immobilized hydrophilic magnetic graphene.

Designing modified therapeutic antibodies with enhanced FcRn-binding affinity holds promise in the extension of circulation half-lives and potential refinement of pharmacokinetics. During the development of these new-generation therapeutic antibodies, FcRn binding affinity of IgGs is emphasized and monitored as a critical quality attribute (CQA), alongside other critical assessments including titer and aggregation level. However, the traditional workflow for assessing the overall quality of expressed IgGs in harvested cell culture fluid (HCCF) is blamed to be cumbersome and time-consuming. This study presents an integrated methodology for the rapid quality assessment of IgGs in HCCF by selectively extracting IgGs with favorable high FcRn affinity for subsequent analysis using size exclusion chromatography (SEC). The approach utilizes innovative adsorbents known as FcRn immobilized hydrophilic magnetic graphene (MG@PDA@PAMAM-FcRn) in a magnetic solid-phase extraction (MSPE) process. To simulate the in vivo binding dynamics, MSPE binding and dissociation was performed at pH 6.0 and 7.4, respectively. The composite have demonstrated enhanced extraction efficiency and impurity removal ability in comparison to commercially available magnetic beads. The SEC monomer peak area value provides the output of this method, the ranking of which enabled the facile identification of superior HCCF samples with high overall quality of IgG. Optimization of MSPE parameters was performed, and the method was validated for specificity, precision, sensitivity, and accuracy. The proposed method exhibited an analytical time of 0.6 h, which is 7-22 times shortened in comparison to the conventional workflow.

Serological Exosome Metabolic Biopsy of Hepatocellular Carcinoma via Designed Core-Shell Nanoparticles.

Exosome metabolite-based liquid biopsy is a promising strategy for large-scale application in practical clinics toward precise medicine. Given the current challenges in successive isolation and analysis of exosomes and their metabolites in this field, we established a low-cost, high-throughput, and rapid platform for serological exosome metabolic biopsy of hepatocellular carcinoma (HCC) via designed core-shell nanoparticles. It starts with the efficient extraction of high-quality serum exosomes and exosome metabolic features, based on which significantly obvious sample clusters are observed by unsupervised cluster analysis. The following integration of feature selection and supervised machine learning enables the identification of six key metabolites and achieves high-performance prediction between HCC, liver cirrhosis, and healthy controls. Specifically, both sensitivity and accuracy achieve 100% among any pairwise intergroup discrimination in a blind test. The quality and reliability of six key metabolites are further evaluated and validated by using different machine learning algorithms and pathway exploration. Our platform contributes to the future growth of new liquid biopsy technologies for precision diagnosis and real-time monitoring of HCC, among other conditions.

Designed Directional Growth of Ti-Metal-Organic Frameworks for Decoding Alzheimer's Disease-Specific Exosome Metabolites.

Exosomes, a growing focus for liquid biopsies, contain diverse molecular cargos. In particular, exosome metabolites with valuable information have exhibited great potential for improving the efficiency of liquid biopsies for addressing complex medical conditions. In this work, we design the directional growth of Ti-metal-organic frameworks on polar-functionalized magnetic particles. This design facilitates the rapid synergistic capture of exosomes with the assistance of an external magnetic field and additionally synergistically enhances the ionization of their metabolites during mass spectrometry detection. Benefiting from this dual synergistic effect, we identified three high-performance exosome metabolites through the differential comparison of a large number of serum samples from individuals with Alzheimer's disease (AD) and normal cognition. Notably, the accuracy of AD identification ranges from 93.18 to 100% using a single exosome metabolite and reaches a flawless 100% with three metabolites. These findings emphasize the transformative potential of this work to enhance the precision and reliability of AD diagnosis, ushering in a new era of improved diagnostic accuracy.

Analysing pharmacodynamic interactions of traditional Chinese medicine in treating acute pancreatitis based on OPLS method.

Acute pancreatitis (AP) is a surgical abdominal disease for which the Dachengqi Decoction (DCQD) of traditional Chinese medicine (TCM) is widely used in China. This study aims to analyse the pharmacodynamic interactions and quantitative relationship of DCQD in the treatment of AP based on orthogonal partial least squares (OPLS) analysis. The experimental data show organic chemical components as candidate pharmacodynamic substances (PS) in the blood and include pharmacodynamic indicators (PIs). Taking each PI as the target and using OPLS method to construct three types of mathematical equations, including the mathematical relationship between the pharmacodynamic substances and each target pharmacodynamic indicator (PS-TPI); the mathematical relationship between the pharmacodynamic substances, the pharmacodynamics indicators and each target pharmacodynamic indicator (PS, PI-TPI); and the mathematical relationship between the pharmacodynamic indicators and each target pharmacodynamic indicator (PI-TPI). Through analysis, we find that the R2Y(cum) values and VIP values indicate that PS and PI are the follow-up factors of TPI; the coefficient value indicates that there is a quantitative relationship between the PS and the TPI; and there also is a quantitative relationship between PI and TPI. The results demonstrated that PS and other PIs are the important influencing factors of TPI, and that there are interactions and quantitative relationships among the PIs.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection.

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 µL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Heterogeneous MXene Hybrid-Oriented Exosome Isolation and Metabolic Profiling for Early Screening, Subtyping and Follow-up Evaluation of Bladder Cancer.

Exosome metabolite-based noninvasive liquid biopsy is an emerging research hotspot that tends to substitute current means in clinics. Nanostructure-based mass spectrometry enables continuous exosome isolation and metabolic profiling with superior analysis speed and high efficiency. Herein, we construct a heterogeneous MXene hybrid that possesses ternary binding sites for exosome capture and outstanding matrix performance for metabolite analysis. Upon optimizing experimental conditions, the average extraction of exosomes and their metabolic patterns from a 60 mL urine sample is completed within 45 s (40 samples per batch for 30 min). According to the exosomal metabolic patterns and the subsequently established biomarker panel, we distinguish early bladder cancer (BCa) from healthy controls with an area under the curve (AUC) value greater than 0.995 in model training and validation sets. As well, we realize subtype classification of BCa in the blind test on metabolic patterns, with an AUC value of 0.867. We also explore the significant biomarkers that are sensitive to follow-up patients, which indeed present reverse change levels compared with pathological progression. This study has the potential to guide the development of the liquid biopsy approach.

Zwitterionic mesoporous engineering aids peptide-dependent pattern profiling for identification of different liver diseases.

Liver disease remains a global health challenge, with its incidence steadily increasing worldwide. Herein, zwitterionic mesoporous engineering was developed for the identification of different liver diseases including liver cirrhosis and liver cancer. Based on this engineering, a total of 2633 m/z signals were observed to be enriched. Notably, three key peptides were identified and showed high accuracy and precision for distinguishing the healthy and disease states, propelling the field of nanomedicine toward genuine personalized medicine.

Construction of Porous Perovskite Oxide Microrods with Au Nanoparticle Anchor for Precise Metabolic Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive illness, and early diagnosis and treatment can help delay its progression. However, clinics still lack high-throughput, low-invasive, precise, and objective diagnostic strategies. Herein, the Au nanoparticles anchored porous perovskite oxide microrods (CTO@Au) with designed superior properties is developed to construct a high-throughput detection platform. Specifically, a single metabolic fingerprinting is obtained from only 30 nL of serum within seconds, enabling the rapid acquisition of 239 × 8 high-quality fingerprints in ≈ 2 h. AD is distinguish from health controls and Parkinson's disease with an area under the curve (AUC) of 1.000. Moreover, eight specific metabolites are identified as a biomarker panel, based on which precise diagnosis of AD is achieved, with an AUC of 1.000 in blind test. The possible relevant pathways and potential mechanism involved in these biomarkers are investigated and discussed. This work provides a high-performance platform for metabolic diagnostic analysis.

Resol/triblock copolymer composite-guided smart fabrication of carbonized mesopores for efficiently decoding exosomal glycans.

Soft-template carbonized mesopores were developed for the purpose of enriching urinary exosomal glycans through organic-organic self-assembly using block copolymers and resol precursors. With a high surface area of 229 m2 g-1, a small pore size of 3.1 nm, and a significant amount of carbon that specifically interacts with oligosaccharides in glycans, this carbonized mesopore material exhibits high selectivity and low limits of detection (5 ng µL-1) towards glycans. Our analysis of complex urine samples from healthy volunteers and bladder carcinoma patients successfully profiled 48 and 56 exosomal glycans, respectively, and 16 of them were significantly changed. Moreover, one upregulated bisecting N-acetylglucosamine (GlcNAc)-type glycan with core fucose, two upregulated and two downregulated terminal-sialylated glycans were revealed to be linked to bladder carcinoma. This approach is of significant importance for understanding diseases that arise from protein glycosylation mutations, and it may contribute to the development of novel diagnostic and therapeutic strategies for bladder carcinoma.

Antibody-assisted MIL-53(Fe)/Pt-based electrochemical biosensor for the detection of the nicotine metabolite cotinine.

Antibody-assisted MIL-53(Fe)/Pt was used as an electrochemical biosensor, and a rapid detection method for analysing cotinine content in smokers' saliva was developed based on this sensor. In this sensor, Pt-modified MIL-53(Fe) was modified as an electrode material onto the surface of the working electrode. The amino group was activated with glutaraldehyde and antibodies to cotinine were modified onto the surface and closed with 1% BSA. Cyclic voltammetry (CV), differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS) were used to study the electrode assembly and cotinine detection. The DPV results showed an ideal linear relationship between the current value and the logarithm of the concentration. The detection limit was 0.0092 ng/mL. It has good selectivity and cycling stability. The proposed Abs-MIL-53(Fe)/Pt can effectively and sensitively detect cotinine in saliva and has promisingapplications.

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

Acute coronary syndrome (ACS), comprising unstable angina (UA) and acute myocardial infarction (AMI), is the leading cause of death worldwide. Currently, lacking effective strategies for classifying ACS hinders the prognosis improvement of ACS patients. Disclosing the nature of metabolic disorders holds the potential to reflect disease progress and high-throughput mass spectrometry-based metabolic analysis is a promising tool for large-scale screening. Herein, a hollow crystallization COF capsuled MOF hybrids (UiO-66@HCOF) assisted serum metabolic analysis is developed for the early diagnosis and risk stratification of ACS. UiO-66@HCOF exhibits unrivaled chemical and structural stability as well as endowing satisfying desorption/ionization efficiency in the detection of metabolites. Paired with machine learning algorithms, early diagnosis of ACS is achieved with the area under the curve (AUC) value of 0.945 for validation sets. Besides, a comprehensive ACS risk stratification method is established, and the AUC value for the discrimination of ACS from healthy controls, and AMI from UA are 0.890, and 0.928. Moreover, the AUC value of the subtyping of AMI is 0.964. Finally, the potential biomarkers exhibit high sensitivity and specificity. This study makes metabolic molecular diagnosis a reality and provided new insight into the progress of ACS.

Hollow Core-Shell Metal Oxide Heterojunctions for the Urinary Metabolic Fingerprint-Based Noninvasive Diagnostic Strategy.

Urine is a preferred object for noninvasive diagnostic strategies. Urinary metabolic analysis is speculatively regarded as an ideal tool for screening diseases closely related to the genitourinary system in view of the intimate relationship between metabolomics and phenotype. Herein, we propose a urinary metabolic fingerprint-based noninvasive diagnostic strategy by designing hollow core-shell metal oxide heterojunctions (denoted as MOHs). With outstanding light absorption and electron-hole separation ability, MOHs aid in the extraction of high-performance urine metabolic fingerprints. Coupled with optimized machine learning algorithms, we establish a metabolic marker panel for accurate diagnosis of prostate cancer (PCa), which is the most common malignant tumor of the male genitourinary system, achieving accuracies of 84.72 and 83.33% in the discovery and validation sets, respectively. Furthermore, metabolite variations and related pathway analyses confirm the credibility and change correlation of key metabolic features in PCa. This work tends to advance the noninvasive diagnostic strategy toward clinical realities.

A protocol of carbonized on-column enrichment for urinary exosomal N-glycans profiling.

As a widely present vesicle, exosome plays an important role in lots of biological processes due to its inclusive cargos. In particular, exosome glycan cargo is attracting attentions since its aberrant alteration is closely related to many progressions in diseases. In this work, a novel carbonized packing capillary trap column for urinary exosomal N-glycan enrichment was proposed. The carbonized packing exhibited large specific surface area, mesoporous structure with narrow pore size distribution and abundant carbon for specially interacting with oligosaccharides. Benefitting from all these advantages, the N-glycans deriving from standard glycoproteins or complex human urine exosomes could be identified with high sensitivity and selectivity. Finally, from the glycans identified in healthy volunteers and patients with bladder carcinoma, we observed that 10 of glycans shared by two groups were obvious downregulation and the 18 were upregulation. These results show great potential of capillary trap column as a tool for the enrichment and detection of glycans in exosomal, attracting more attention on disease progression monitoring and biomarker discovery.

Multiscale Element-Doped Nanowire Array-Coupled Machine Learning Reveals Metabolic Fingerprints of Nonreversible Liver Diseases.

Timely detection of nonreversible liver diseases contributes greatly to reasonable therapy and quality of life. Given the current situation, minimally invasive high-specificity molecular diagnosis based on body fluid can be a good choice. Herein, a mesoporous superstructure is designed using silicon atom-doped nanowire arrays to uniformly load Pt nanoparticles on the surface to produce a desirable ionization effect. We apply the multiscale element-doped nanowire arrays to efficiently assist extraction of high-quality metabolic fingerprints from only 35 nL of serum within seconds. Using different machine learning algorithms, we establish specific biomarker panels to distinguish different liver diseases from the healthy control, with more than 90% accuracy, sensitivity, and specificity. Moreover, from established biomarker panels, we further determine key metabolites of significant difference (p < 0.01) via group comparison to realize the discrimination of different liver diseases with 100% sensitivity. Our work confirms the design protocol of an advanced diagnosis tool and lays a robust foundation for metabolic molecular diagnosis in large-scale clinical application.

Diagnosing, Typing, and Staging of Renal Cell Carcinoma by Designer Matrix-Based Urinary Metabolic Analysis.

Molecular diagnosing, typing, and staging have been considered to be the ideal alternatives of imaging-based detection methods in clinics. Designer matrix-based analytical tools, with high speed, throughout, efficiency and low/noninvasiveness, have attracted much attention recently for in vitro metabolite detection. Herein, we develop an advanced metabolic analysis tool based on highly porous metal oxides derived from available metal-organic frameworks (MOFs), which elaborately inherit the morphology and porosity of MOFs and newly incorporate laser adsorption capacity of metal oxides. Through optimized conditions, direct high-quality fingerprinting spectra in 0.5 µL of urine are acquired. Using these fingerprinting spectra, we can discriminate the renal cell carcinoma (RCC) from healthy controls with higher than 0.99 of area under the curve (AUC) values (R2Y(cum) = 0.744, Q2 (cum) = 0.880), as well, from patients with other tumors (R2Y(cum) = 0.748, Q2(cum) = 0.871). We also realize the typing of three RCC subtypes, including clear cell RCC, chromophobe RCC (R2Y(cum) = 0.620, Q2(cum) = 0.656), and the staging of RCC (R2Y(cum) = 0.755, Q2(cum) = 0.857). Moreover, the tumor sizes (threshold value is 3 cm) can be remarkably recognized by this advanced metabolic analysis tool (R2Y(cum) = 0.710, Q2(cum) = 0.787). Our work brings a bright prospect for designer matrix-based analytical tools in disease diagnosis, typing and staging.