In Situ Array Microextraction and Metabolic Profiling of Small Extracellular Vesicles to Guide and Monitor Maternal Delivery.

The advantages of small extracellular vesicles (sEV) in disease management have become increasingly prominent, with the main challenge lying in meeting the demands of large-scale extraction and high-throughput analysis, a crucial aspect in the realm of precision medicine. To overcome this challenge, an engineered on-plate aptamer array (16×24 spots) is developed for continuous scale-up microextraction of plasma sEV and their in situ metabolic analysis using mass spectrometry. With this integrated array strategy, metabolic profiles of sEV are acquired from the plasma of 274 antenatal or postpartum women, reducing analysis time by half (7.5 h) and sample volume by 95% (only 0.125 µL usage) compared to the traditional suspension method. Moreover, using machine learning algorithms on sEV metabolic profiles, a risk score system is constructed that accurately assesses the need for epidural analgesia during childbirth and the likelihood of post-administration fever. The system, based on admission samples, achieves an impressive 94% accuracy. Furthermore, post-administration fever can be identified from delivery samples, reaching an overall accuracy rate of 88%. This work offers real-time monitoring of the childbirth process that can provide timely guidance for maternal delivery, underscoring the significance of sEV detection in large-scale clinical samples for medicine innovation and advancement.

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.

Magnetic quaternary ammonium polymer bearing porous agarose for selective extraction of Aristolochic acids in the plasma.

Aristolochic acids (AAs) naturally occurring in the herbal genus Aristolochia are associated with a high risk of kidney failure, multiple tumors and cancers. However, approaches with high selectivity and rapidity for measuring AAs in biological samples are still inadequate. Inspired by the mechanism of AAs-induced nephrotoxicity, we designed a hybrid magnetic polymer-porous agarose (denoted as MNs@SiO2M@DNV-A), mimicking the effect of basic and aromatic residues of organic anion transporter 1 (OAT1) for efficient enriching aristolochic acid I (AA I) and aristolochic acid II (AA II) in the plasma. The monomers of vinylbenzyl trimethylammonium chloride (VBTAC), N-vinyl-2-pyrrolidinone (NVP) and divinylbenzene (DVB) were employed to construct the polymer layer, which provided a selective adsorption for AAs by multiple interactions. The porous agarose shell contributed to remove interfering proteins in the plasma samples. A magnetic solid-phase extraction (MSPE) based on the proposed composite enhanced the selectivity toward AA I and AA II in the plasma samples. In combination of HPLC analysis, the proposed method was proved to be applicable to fast and specific quantification of AAs in blood samples, which was characterized by a good linearity, high sensitivity, acceptable recovery, excellent repeatability and satisfactory reusability.

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.

Construction of hierarchical porous and polydopamine/salicylaldoxime functionalized zeolitic imidazolate framework-8 via controlled etching for uranium adsorption.

Efficient uranium extraction from seawater is critical for the development of the nuclear industry. Herein, a polydopamine/salicylaldoxime decorated hierarchical zeolitic imidazolate framework-8 (H-PDA/SA-ZIF-8) is constructed by using a controlled etching process. Benefiting from the combination of PDA/SA and the zeolitic imidazolate framework-8 (ZIF-8), as well as a controlled etching process, the H-PDA/SA-ZIF-8 possesses multiaffinity sites, excellent specific surface area (1234.92 m2 g-1), and a hierarchical pore structure. The H-PDA/SA-ZIF-8 exhibits excellent adsorption capacity (Qm = 869.6 mg g-1), selectivity, and reusability in uranium adsorption. The adsorption process of H-PDA/SA-ZIF-8 fits very well with the Langmuir isotherm model and pseudo-second-order models, and the adsorption process equilibrates within 20 min (C0 = 20 mg L-1). Furthermore, the H-PDA/SA-ZIF-8 shows remarkable antibacterial ability. Impressively, the adsorption capacity of H-PDA/SA-ZIF-8 to uranium in natural seawater reaches 6.9 mg g-1 after circulation for 15 days. Therefore, the H-PDA/SA-ZIF-8 is a promising and fascinating material for uranium extraction from natural seawater.

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.

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.

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.

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.

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.

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.

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 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.