Plasmonic Alloys Reveal a Distinct Metabolic Phenotype of Early Gastric Cancer

Energy band engineered nanomatrix assisted mass spectrometry for metabolite detection

Metabolites participate in the regulation of various physiological and pathophysiological processes in organisms. Metabolite detection can identify disease biomarkers, facilitating early disease diagnosis. Laser desorption/ionization mass spectrometry (LDI MS) holds promise in metabolite detection, but the performance of mass spectrometry depends on the precise design and preparation of matrix materials. The lack of clear understanding of LDI mechanisms hinders the rational design of matrices. This paper proposes a matrix design strategy by developing an energy band engineered Indium-doped CuCrO2 (ICCO), thereby achieving hole regulation, for precisely controlling charge-driven desorption to enhance LDI performance. Furthermore, by integrating density functional theory (DFT), this strategy is capable of enhancing the charge transfer ability at the matrix-glucose interface, to the consequent improvement in LDI performance. Compared to CCO, other concentrations of ICCO and traditional organic matrices, 2.5 % ICCO could achieve ≈2-500-fold signal enhancement. The optimized ICCO-assisted laser desorption ionization mass spectrometry platform can quantify glucose concentration in diabetic patient serum samples with only 1 μL of serum. This method has been validated to exhibit high accuracy (Pearson's r = 0.997 compared with the clinical gold standard) and good reproducibility (CV < 7 %). This work has facilitated the development of advanced substrates and underscored the potential of metabolite quantification in practical clinical applications going forward.

Hierarchical structure of pyramid Si-ZnO NRs as reliable SALDI-MS platform for trace detection of pesticide

Assessing agrochemical residues is crucial for food safety and consumer health. Surface-assisted laser desorption/ionization mass spectrometry (SALDI-MS) has been proven to be efficient for pesticide detection, however its uneven substrate surface distribution limits its practical applicability. Improving detection sensitivity while maintaining detection repeatability is still challenging. In this study, a 3-D heterostructure integrated pyramid silicon and zinc oxide nanorods (pyramid Si-ZnO NRs) was proposed as a SALDI-MS substrate to enhance detection sensitivity and repeatability. The pyramid Si-ZnO NRs improve light trapping and hole-electron separation efficiency, significantly boosting SALDI-MS sensitivity. The limit of detections (LODs) for thiabendazole, imidacloprid, and berberine in beverages are respectively as low as 0.45 nM, 0.17 nM and 81 pM. The superhydrophobic substrate suppresses the "sweet spot" effect, improving repeatability with relative standard deviations (RSDs) under 10 %. This platform is a sensitive, reliable tool for detecting pesticide residues, with potential applications in food safety.

Precise positioning of Au islands within mesoporous Pd-Pt nanoparticles for plasmon-enhanced methanol oxidation

Trimetallic systems have garnered considerable attention in (electro)catalysis due to the synergistic effects resulting from the combination of three different metals. However, achieving precise control over the positioning of various metals and understanding the relationship between structure and performance remains challenging. This study introduces an approach for synthesizing Pd@Pt@Au mesoporous nanoparticles (MNPs) with distinct core-shell Pd@Pt structures, featuring well-dispersed isolated Au islands on the outer shell, improving the plasmonic effect. The electrocatalytic performance of Pd@Pt@Au MNPs in the methanol oxidation reaction (MOR) is assessed under light-induced and light-independent conditions. The results indicate significantly enhanced activity compared to commercial Pt black, with catalytic activity during MOR increasing approximately 7.5-fold under light irradiation. The external placement of Au on the shell of Pd@Pt@Au MNPs provides superior plasmonic enhancement, thereby contributing to improved catalytic performance under light irradiation. This investigation sheds light on the controlled synthesis of trimetallic MNPs and their catalytic applications, underscoring the importance of precise Au positioning for optimizing performance.

Non-Invasive Diagnosis of Moyamoya Disease Using Serum Metabolic Fingerprints and Machine Learning

Moyamoya disease (MMD) is a progressive cerebrovascular disorder that increases the risk of intracranial ischemia and hemorrhage. Timely diagnosis and intervention can significantly reduce the risk of new-onset stroke in patients with MMD. However, the current diagnostic methods are invasive and expensive, and non-invasive diagnosis using biomarkers of MMD is rarely reported. To address this issue, nanoparticle-enhanced laser desorption/ionization mass spectrometry (LDI MS) was employed to record serum metabolic fingerprints (SMFs) with the aim of establishing a non-invasive diagnosis method for MMD. Subsequently, a diagnostic model was developed based on deep learning algorithms, which exhibited high accuracy in differentiating the MMD group from the HC group (AUC = 0.958, 95% CI of 0.911 to 1.000). Additionally, hierarchical clustering analysis revealed a significant association between SMFs across different groups and vascular cognitive impairment in MMD. This approach holds promise as a novel and intuitive diagnostic method for MMD. Furthermore, the study may have broader implications for the diagnosis of other neurological disorders.

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.

Noble Metal Nanoparticle Assisted Mass Spectrometry for Metabolite-Based In Vitro Diagnostics

In vitro diagnostics (IVD) makes clinical diagnosis rapid, simple, and noninvasive to patients, playing a crucial role in the early diagnosis and monitoring of diseases. Metabolic biomarkers are closely correlated to the phenotype of diseases. However, most IVD platforms are constrained by the sensitivity and throughput of assay. In recent years, noble-metal-nanoparticle (NMNP)-assisted laser desorption/ionization mass spectrometry (LDI MS) has generated major advances in metabolite analysis, significantly improving the sensitivity, accuracy, and throughput of IVD due to the unique optical and electrical properties of NMNPs. This review systematically assesses the development of NMNPs as LDI MS matrices in the detection of metabolites for IVD application. The analysis of several NMNP structures, such as core-shell, porous, and 2D nanoparticles, elucidates their significant contribution to the enhancement of MS performance. Furthermore, the recent advancements in the application of NMNPs for diagnosing various systemic diseases are summarized. Finally, the prospects and challenges of NMNP-assisted MS for IVD are discussed. This review elucidates the roles of NMNPs' structure in enhancing MS-based metabolic detection and provides an overview of various IVD applications, consequently offering comprehensive insights for researchers and developers in this field.

Diagnosis and prognosis prediction of gastric cancer by high-performance serum lipidome fingerprints

Early detection is warranted to improve prognosis of gastric cancer (GC) but remains challenging. Liquid biopsy combined with machine learning will provide new insights into diagnostic strategies of GC. Lipid metabolism reprogramming plays a crucial role in the initiation and development of tumors. Here, we integrated the lipidomics data of three cohorts (n = 944) to develop the lipid metabolic landscape of GC. We further constructed the serum lipid metabolic signature (SLMS) by machine learning, which showed great performance in distinguishing GC patients from healthy donors. Notably, the SLMS also held high efficacy in the diagnosis of early-stage GC. Besides, by performing unsupervised consensus clustering analysis on the lipid metabolic matrix of patients with GC, we generated the gastric cancer prognostic subtypes (GCPSs) with significantly different overall survival. Furthermore, the lipid metabolic disturbance in GC tissues was demonstrated by multi-omics analysis, which showed partially consistent with that in GC serums. Collectively, this study revealed an innovative strategy of liquid biopsy for the diagnosis of GC on the basis of the serum lipid metabolic fingerprints.

Biofunctional photoelectrochemical/electrochemical immunosensor based on BiVO4/BiOI-MWCNTs and Au@PdPt for alpha-fetoprotein detection

A biofunctional immunosensor combining photoelectrochemical (PEC) and electrochemical (EC) was proposed for the quantitative detection of the liver cancer marker alpha-fetoprotein (AFP) in human blood. BiVO4/BiOI-MWCNTs photoactive materials were first prepared on conductive glass FTO, and the photoelectrode was functionalized by chitosan and glutaraldehyde. Then, the AFP capture antibody (Ab1) was successfully modified on the photoelectrode, and the label-free rapid detection of AFP antigen was achieved by PEC. In addition, Au@PdPt nanospheres were also used as a marker for binding to AFP detection antibody (Ab2). Due to the excellent catalytic properties of Au@PdPt in EC reaction, a signal increase in the EC response can be achieved when Ab2 binds to the AFP antigen, which ensures high sensitivity for the detection of AFP. The detection limits of PEC and EC are 0.050 pg/mL and 0.014 pg/mL, respectively. The sensor also possesses good specificity, stability and reproducibility, shows excellent performance in the detection of clinical samples and has good clinical applicability.

Plasma Metabolic Profiles via p-p Heterojunction-Assisted Laser Desorption/Ionization Mass Spectrometry for Advanced Warning and Diagnosis of Epidural-Related Maternal Fever

Epidural-related maternal fever (ERMF) heightens the risk of intrapartum fever, whereas effective prevention and treatment in clinical practice are currently lacking. Rapid and sensitive screening tools for ERMF are urgently needed to advance relevant research. In response to this challenge, we devise and craft porous Co3O4/CuO hollow polyhedral nanocages with p-p heterojunctions derived from metal-organic frameworks. We employ these p-p heterojunctions in conjunction with high-throughput mass spectrometry to conduct metabolic analysis of substantial plasma samples, with only about 0.03 μL per sample. Leveraging these p-p heterojunctions, metabolic signals from complex plasma can be amplified, with great reproducibility. By harnessing the power of machine learning on these metabolic signals, we are able to achieve advanced warning of ERMF with an area under the curve (AUC) of 0.887-0.975 by the differentially metabolic analysis of plasma samples collected upon admission. Furthermore, we can accurately diagnose ERMF with an AUC of 0.850-1.000 by analyzing plasma samples collected at the time of delivery from individuals who have received epidural analgesia. These breakthroughs offer invaluable insights for clinical decision making during labor and have the potential to significantly reduce the incidence of ERMF.

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.

Pt/NiFe-LDH hybrids for quantification and qualification of polyphenols

Polyphenols with antioxidant properties are of significant interest in medical and pharmaceutical applications. Given the diverse range of activities of polyphenols in vivo, accurate detection of these compounds plays a crucial role in nutritional surveillance and pharmaceutical development. Yet, the efficient quantitation of polyphenol contents and qualification of monomer compositions present a notable challenge when studying polyphenol bioavailability. In this study, platinum-modified nickel-iron layered double hydroxide (Pt/NiFe-LDH hybrids) were designed to mimic peroxidases for colorimetric analysis and act as enhanced matrices for laser desorption/ionization mass spectrometry (LDI MS) to quantify and qualify polyphenols. The hybrids exhibited an enzymatic activity of 33.472 U/mg for colorimetric assays, facilitating the rapid and direct quantitation of total tea polyphenols within approximately 1 min. Additionally, the heterogeneous structure and exposed hydroxyl groups on the hybrid surface contributed to photoelectric enhancement and in-situ enrichment of polyphenols in LDI MS. This study introduces an innovative approach to detect polyphenols using advanced materials, potentially inspiring the future development and applications of other photoactive nanomaterials.

Early Diagnosis and Prognosis Prediction of Pancreatic Cancer Using Engineered Hybrid Core-Shells in Laser Desorption/Ionization Mass Spectrometry

Effective detection of bio-molecules relies on the precise design and preparation of materials, particularly in laser desorption/ionization mass spectrometry (LDI-MS). Despite significant advancements in substrate materials, the performance of single-structured substrates remains suboptimal for LDI-MS analysis of complex systems. Herein, designer Au@SiO2@ZrO2 core-shell substrates are developed for LDI-MS-based early diagnosis and prognosis of pancreatic cancer (PC). Through controlling Au core size and ZrO2 shell crystallization, signal amplification of metabolites up to 3 orders is not only achieved, but also the synergistic mechanism of the LDI process is revealed. The optimized Au@SiO2@ZrO2 enables a direct record of serum metabolic fingerprints (SMFs) by LDI-MS. Subsequently, SMFs are employed to distinguish early PC (stage I/II) from controls, with an accuracy of 92%. Moreover, a prognostic prediction scoring system is established with enhanced efficacy in predicting PC survival compared to CA19-9 (p < 0.05). This work contributes to material-based cancer diagnosis and prognosis.

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.

Fast Label-Free Metabolic Profile Recognition Identifies Phenylketonuria and Subtypes

Phenylketonuria (PKU) is the most common inherited metabolic disease in humans. Clinical screening of newborn heel blood samples for PKU is costly and time-consuming because it requires multiple procedures, like isotope labeling and derivatization, and PKU subtype identification requires an additional urine sample. Delayed diagnosis of PKU, or subtype identification can result in mental disability. Here, plasmonic silver nanoshells are used for laser desorption/ionization mass spectrometry (MS) detection of PKU with label-free assay by recognizing metabolic profile in dried blood spot (DBS) samples. A total of 1100 subjects are recruited and each DBS sample can be processed in seconds. This platform achieves PKU screening with a sensitivity of 0.985 and specificity of 0.995, which is comparable to existing clinical liquid chromatography MS (LC-MS) methods. This method can process 360 samples per hour, compared with the LC-MS method which processes only 30 samples per hour. Moreover, this assay enables precise identification of PKU subtypes without the need for a urine sample. It is demonstrated that this platform enables high-performance and fast, low-cost PKU screening and subtype identification. This approach might be suitable for the detection of other clinically relevant biomarkers in blood or other clinical samples.

Metabolic profiling of bacteria with the application of polypyrrole-MOF SPME fibers and plasmonic nanostructured LDI-MS substrates

Here we present application of innovative lab-made analytical devices such as plasmonic silver nanostructured substrates and polypyrrole-MOF solid-phase microextraction fibers for metabolic profiling of bacteria. For the first time, comprehensive metabolic profiling of both volatile and non-volatile low-molecular weight compounds in eight bacterial strains was carried out with utilization of lab-made devices. Profiles of low molecular weight metabolites were analyzed for similarities and differences using principal component analysis, hierarchical cluster analysis and random forest algorithm. The results showed clear differentiation between Gram positive (G+) and Gram negative (G-) species which were identified as distinct clusters according to their volatile metabolites. In case of non-volatile metabolites, differentiation between G+ and G- species and clustering for all eight species were observed for the chloroform fraction of the Bligh & Dyer extract, while methanolic fraction failed to recover specific ions in the profile. Furthermore, the results showed correlation between volatile and non-volatile metabolites, which suggests that lab-made devices presented in the current study might be complementary and therefore, useful for species differentiation and gaining insights into bacterial metabolic pathways.

Plasmonic Alloys Enhanced Metabolic Fingerprints for the Diagnosis of COPD and Exacerbations

Accurate diagnosis of chronic obstructive pulmonary disease (COPD) and exacerbations by metabolic biomarkers enables individualized treatment. Advanced metabolic detection platforms rely on designed materials. Here, we design mesoporous PdPt alloys to characterize metabolic fingerprints for diagnosing COPD and exacerbations. As a result, the optimized PdPt alloys enable the acquisition of metabolic fingerprints within seconds, requiring only 0.5 μL of native plasma by laser desorption/ionization mass spectrometry owing to the enhanced electric field, photothermal conversion, and photocurrent response. Machine learning decodes metabolic profiles acquired from 431 individuals, achieving a precise diagnosis of COPD with an area under the curve (AUC) of 0.904 and an accurate distinction between stable COPD and acute exacerbations of COPD (AECOPD) with an AUC of 0.951. Notably, eight metabolic biomarkers identified accurately discriminate AECOPD from stable COPD while providing valuable information on disease progress. Our platform will offer an advanced nanoplatform for the management of COPD, complementing standard clinical techniques.

A single atom cobalt anchored MXene bifunctional platform for rapid, label-free and high-throughput biomarker analysis and tissue imaging

Few of single-atom materials have been served as platform to analyze small molecules for surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). Herein, a novel single Co atom-anchored MXene (Co-N-Ti3C2) is prepared to achieve enhanced SALDI-MS and mass spectrometry imaging (MSI) performance for the first time. The Co-N-Ti3C2 films were prepared by a simple in situ self-assembly strategy to generate an efficient SALDI-MS platform. Compared to typical inorganic/organic matrices, Co-N-Ti3C2 films exhibit superior performance in small molecules detection with ultra-high sensitivity (LOD at amol level), excellent repeatability (CV <4%), clean background and wide analyte coverage, enabling accurate quantitative analysis of various low-concentration metabolites from 1 μL biofluid in seconds. Its usage efficiently enhanced SALDI-MS detection of various small-molecule biomarkers such as amino acids, succinic acid, itaconic acid, arachidonic acid, citrulline, prostaglandin E2, creatinine, uric acid, glutamine, D-mannose, cholesterol and inositol in positive ion mode. The blood glucose level in humans was successfully determined from a linearity concentration range (0.25-10 mM). Notably, the Co-N-Ti3C2 assisted SALDI-MSI enables study the spatial distribution of small molecules covering the range central to metabolomics at a high resolution on a tissue section. Furthermore, Co-N-Ti3C2 platform revealed a specific peak profile that distinguishes osteoarthritis (OA) from rheumatoid arthritis (RA) tissue. Density functional theory theoretical investigation revealed that single Co atoms anchored on Ti3C2 could highly enhanced the ionization ability of metabolites, resulting in high-sensitivity and heterogeneous metabolome coverage.

Self-Driven Electric Field Control of Orbital Electrons in AuPd Alloy Nanoparticles for Cancer Catalytic Therapy

The free radical generation efficiency of nanozymes in cancer therapy is crucial, but current methods fall short. Alloy nanoparticles (ANs) hold promise for improving catalytic performance due to their inherent electronic effect, but there are limited ways to modulate this effect. Here, a self-driven electric field (E) system utilizing triboelectric nanogenerator (TENG) and AuPd ANs with glucose oxidase (GOx)-like, catalase (CAT)-like, and peroxidase (POD)-like activities is presented to enhance the treatment of 4T1 breast cancer in mice. The E stimulation from TENG enhances the orbital electrons of AuPd ANs, resulting in increased CAT-like, GOx-like, and POD-like activities. Meanwhile, the catalytic cascade reaction of AuPd ANs is further amplified after catalyzing the production of H2 O2 from the GOx-like activities. This leads to 89.5% tumor inhibition after treatment. The self-driven E strategy offers a new way to enhance electronic effects and improve cascade catalytic therapeutic performance of AuPd ANs in cancer therapy.

Self-Assembled Hyperbranched Gold Nanoarrays Decode Serum United Urine Metabolic Fingerprints for Kidney Tumor Diagnosis

Serum united urine metabolic analysis comprehensively reveals the disease status for kidney diseases in particular. Thus, the precise and convenient acquisition of metabolic molecular information from united biofluids is vitally important for clinical disease diagnosis and biomarker discovery. Laser desorption/ionization mass spectrometry (LDI-MS) presents various advantages in metabolic analysis; however, there remain challenges in ionization efficiency and MS signal reproducibility. Herein, we constructed a self-assembled hyperbranched black gold nanoarray (HyBrAuNA) assisted LDI-MS platform to profile serum united urine metabolic fingerprints (S-UMFs) for diagnosis of early stage renal cell carcinoma (RCC). The closely packed HyBrAuNA afforded strong electromagnetic field enhancement and high photothermal conversion efficacy, enabling effective ionization of low abundant metabolites for S-UMF collection. With a uniform nanoarray, the platform presented excellent reproducibility to ensure the accuracy of S-UMFs obtained in seconds. When it was combined with automated machine learning analysis of S-UMFs, early stage RCC patients were discriminated from the healthy controls with an area under the curve (AUC) > 0.99. Furthermore, we screened out a panel of 9 metabolites (4 from serum and 5 from urine) and related pathways toward early stage kidney tumor. In view of its high-throughput, fast analytical speed, and low sample consumption, our platform possesses potential in metabolic profiling of united biofluids for disease diagnosis and pathogenic mechanism exploration.

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.

Efficient plasma metabolic fingerprinting as a novel tool for diagnosis and prognosis of gastric cancer: a large-scale, multicentre study

OBJECTIVE

Metabolic biomarkers are expected to decode the phenotype of gastric cancer (GC) and lead to high-performance blood tests towards GC diagnosis and prognosis. We attempted to develop diagnostic and prognostic models for GC based on plasma metabolic information.

DESIGN

We conducted a large-scale, multicentre study comprising 1944 participants from 7 centres in retrospective cohort and 264 participants in prospective cohort. Discovery and verification phases of diagnostic and prognostic models were conducted in retrospective cohort through machine learning and Cox regression of plasma metabolic fingerprints (PMFs) obtained by nanoparticle-enhanced laser desorption/ionisation-mass spectrometry (NPELDI-MS). Furthermore, the developed diagnostic model was validated in prospective cohort by both NPELDI-MS and ultra-performance liquid chromatography-MS (UPLC-MS).

RESULTS

We demonstrated the high throughput, desirable reproducibility and limited centre-specific effects of PMFs obtained through NPELDI-MS. In retrospective cohort, we achieved diagnostic performance with areas under curves (AUCs) of 0.862-0.988 in the discovery (n=1157 from 5 centres) and independent external verification dataset (n=787 from another 2 centres), through 5 different machine learning of PMFs, including neural network, ridge regression, lasso regression, support vector machine and random forest. Further, a metabolic panel consisting of 21 metabolites was constructed and identified for GC diagnosis with AUCs of 0.921-0.971 and 0.907-0.940 in the discovery and verification dataset, respectively. In the prospective study (n=264 from lead centre), both NPELDI-MS and UPLC-MS were applied to detect and validate the metabolic panel, and the diagnostic AUCs were 0.855-0.918 and 0.856-0.916, respectively. Moreover, we constructed a prognosis scoring system for GC in retrospective cohort, which can effectively predict the survival of GC patients.

CONCLUSION

We developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.

Comprehensive Metabolic Fingerprints Characterize Neuromyelitis Optica Spectrum Disorder by Nanoparticle-Enhanced Laser Desorption/Ionization Mass Spectrometry

Timely screening of neuromyelitis optica spectrum disorder (NMOSD) and differential diagnosis from myelin oligodendrocyte glycoprotein associated disorder (MOGAD) are the keys to improving the quality of life of patients. Metabolic disturbance occurs with the development of NMOSD. Still, advanced tools are required to probe the metabolic phenotype of NMOSD. Here, we developed a fast nanoparticle-enhanced laser desorption/ionization mass spectrometry assay for multiplexing metabolic fingerprints (MFs) from trace plasma and cerebrospinal fluid (CSF) samples in 30 s. Machine learning of the plasma MFs achieved the timely screening of NMOSD from healthy donors with an area under receiver operator characteristic curve (AUROC) of 0.998, and it comprehensively revealed the dysregulated neurotransmitter and energy metabolisms. Combining comprehensive MFs from both plasma and CSF, we constructed an integrated panel for differential diagnosis of NMOSD versus MOGAD with an AUROC of 0.923. This approach demonstrated performance superior to that of human experts in classifying two diseases, especially in antibody assay-limited regions. Together, this approach provides an advanced nanomaterial-based tool for identifying vulnerable populations below the antibody threshold of aquaporin-4 positivity.

Optical Manipulation of Soft Matter

Optical manipulation has emerged as a pivotal tool in soft matter research, offering superior applicability, spatiotemporal precision, and manipulation capabilities compared to conventional methods. Here, an overview of the optical mechanisms governing the interaction between light and soft matter materials during manipulation is provided. The distinct characteristics exhibited by various soft matter materials, including liquid crystals, polymers, colloids, amphiphiles, thin liquid films, and biological soft materials are highlighted, and elucidate their fundamental response characteristics to optical manipulation techniques. This knowledge serves as a foundation for designing effective strategies for soft matter manipulation. Moreover, the diverse range of applications and future prospects that arise from the synergistic collaboration between optical manipulation and soft matter materials in emerging fields are explored.

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.

Plasmonic alloys for quantitative determination and reaction monitoring of biothiols

Biothiols participate in numerous physiological and pathological processes in an organism. Quantitative determination and reaction monitoring of biothiols have important implications for evaluating human health. Herein, we synthesized plasmonic alloys as the matrix to assist the laser desorption and ionization (LDI) process of biothiols in mass spectrometry (MS). Plasmonic alloys were constructed with mesoporous structures for LDI enhancement and trimetallic (PdPtAu) compositions for noble metal-thiol hybridization, toward enhanced detection sensitivity and selectivity, respectively. Plasmonic alloys enabled direct detection of biothiols from complex biosamples without any enrichment or separation. We introduced internal standards into the quantitative MS system, achieving accurate quantitation of methionine directly from serum samples with a recovery rate of 103.19% ± 6.52%. Moreover, we established a rapid monitoring platform for the oxidation-reduction reaction of glutathione, consuming trace samples down to 200 nL with an interval of seconds. This work contributes to the development of molecular tools based on plasmonic materials for biothiol detection toward real-case applications.

MnO2 Coated Mesoporous PdPt Nanoprobes for Scavenging Reactive Oxygen Species and Solving Acetaminophen-Induced Liver Injury

As one of the most widely used drugs, acetaminophen, is the leading cause of acute liver injury. In addition, acetaminophen-induced liver injury (AILI) has a strong relationship with the overproduced reactive oxygen species, which can be effectively eliminated by nanozymes. To address these challenges, mesoporous PdPt@MnO2 nanoprobes (PPM NPs) mimicking peroxide, catalase, and superoxide dismutase-like properties are synthesized. They demonstrate nontoxicity, high colloidal stability, and exceptional reactive oxygen species (ROS)-scavenging ability. By scavenging excessive ROS, decreasing inflammatory cytokines, and inhibiting the recruitment and activation of monocyte/macrophage cells and neutrophils, the pathology mechanism of PPM NPs in AILI is confirmed. Moreover, PPM NPs' therapeutic effect and good biocompatibility may facilitate the clinical treatment of AILI.

Hollow multishelled heterostructures with enhanced performance for laser desorption/ionization mass spectrometry based metabolic diagnosis

High-performance metabolic diagnosis-based laser desorption/ionization mass spectrometry (LDI-MS) improves the precision diagnosis of diseases and subsequent treatment. Inorganic matrices are promising for the detection of metabolites by LDI-MS, while the structure and component impacts of the matrices on the LDI process are still under investigation. Here, we designed a multiple-shelled ZnMn2O4/(Co, Mn)(Co, Mn)2O4 (ZMO/CMO) as the matrix from calcined MOF-on-MOF for detecting metabolites in LDI-MS and clarified the synergistic impacts of multiple-shells and the heterostructure on LDI efficiency. The ZMO/CMO heterostructure allowed 3-5 fold signal enhancement compared with ZMO and CMO with the same morphology. Furthermore, the ZMO/CMO heterostructure with a triple-shelled hollow structure displayed a 3-fold signal enhancement compared to its nanoparticle counterpart. Taken together, the triple-shelled hollow ZMO/CMO exhibits 102-fold signal enhancement compared to the commercial matrix products (e.g., DHB and DHAP), allowing for sensitive metabolic profiling in bio-detection. We directly extracted metabolic patterns by the optimized triple-shelled hollow ZMO/CMO particle-assisted LDI-MS within 1 s using 100 nL of serum and used machine learning as the readout to distinguish hepatocellular carcinoma from healthy controls with the area under the curve value of 0.984. Our approach guides us in matrix design for LDI-MS metabolic analysis and drives the development of a nanomaterial-based LDI-MS platform toward precision diagnosis.

Efficient Metabolic Fingerprinting of Follicular Fluid Encodes Ovarian Reserve and Fertility

Ovarian reserve (OR) and fertility are critical in women's healthcare. Clinical methods for encoding OR and fertility rely on the combination of tests, which cannot serve as a multi-functional platform with limited information from specific biofluids. Herein, metabolic fingerprinting of follicular fluid (MFFF) from follicles is performed, using particle-assisted laser desorption/ionization mass spectrometry (PALDI-MS) to encode OR and fertility. PALDI-MS allows efficient MFFF, showing fast speed (≈30 s), high sensitivity (≈60 fmol), and desirable reproducibility (coefficients of variation <15%). Further, machine learning of MFFF is applied to diagnose diminished OR (area under the curve of 0.929) and identify high-quality oocytes/embryos (p < 0.05) by a single PALDI-MS test. Meanwhile, metabolic biomarkers from MFFF are identified, which also determine oocyte/embryo quality (p < 0.05) from the sampling follicles toward fertility prediction in clinics. This approach offers a powerful platform in women's healthcare, not limited to OR and fertility.

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.

Bovine Omasum-Inspired Interfacial Carbon-Based Nanocomposite for Saliva Metabolic Screening of Gastric Cancer

Gastric cancer is one of the most common malignant digestive cancers, and its diagnostic has still faced challenges based on metabolic analysis due to complex sample pretreatment and low metabolite abundance. In this study, inspired by the structure of bovine omasum, we in situ synthesized a novel interfacial carbon-based nanocomposite of graphene supported nickel nanoparticles-encapsulated in the nitrogen-doped carbon nanotube (Ni/N-CNT/rGO), which was served as a novel matrix with enhanced ionization efficiency for the matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) saliva metabolic analysis of gastric cancer. Benefiting from its high sp² graphitic degree, large surface area, strong UV absorption, and rich active sites, Ni/N-CNT/rGO matrix exhibited excellent performances of reproducibility, coverage, salt-tolerance, sensitivity, and adsorption ability in MALDI-TOF MS. The differential scanning calorimetry (DSC) and thermal conversion behaviors explained the highly efficient LDI mechanism. Based on saliva metabolic fingerprints, Ni/N-CNT/rGO assisted LDI MS with cross-validation analysis could successfully distinguish gastric cancer patients from healthy controls through the screening of four potential biomarkers with an accuracy of 92.50%, specificity of 88.03%, and sensitivity of 97.12%. This work provided a fast and sensitive MS sensing platform for the metabolomics characterization of gastric cancer and might have potential value for precision medicine in the future.

Label-free MXene-assisted field effect transistor for the determination of IL-6 in patients with kidney transplantation infection

A spiral interdigitated MXene-assisted field effect transistor (SiMFETs) was proposed for determination of IL-6 in patients with kidney transplantation infection. Our SiMFETs demonstrated enhanced IL-6 detection range of 10 fg/mL-100 ng/mL due to the combination of optimized transistor's structure and semiconducting nanocomposites. Specifically, on one hand, MXene-based field effect transistor drastically amplified the amperometric signal for determination of IL-6; on the other hand, the multiple spiral structure of interdigitated drain-source architecture improved the transconductance of FET biosensor. The developed SiMFETs biosensor demonstrated satisfactory stability for 2 months, and favorable reproducibility and selectivity against other biochemical interferences. The SiMFETs biosensor exhibited acceptable correlation coefficient (R2=0.955) in quantification of clinical biosamples. The sensor successfully distinguished the infected patients from the health control with enhanced AUC of 0.939 (sensitivity of 91.7%, specificity of 86.7%). Those merits introduced here may pave an alternative strategy for transistor-based biosensor in point-of-care clinic applications.

Metabolic biomarkers in lung cancer screening and early diagnosis (Review)

Late diagnosis is one of the major contributing factors to the high mortality rate of lung cancer, which is now the leading cause of cancer-associated mortality worldwide. At present, low-dose CT (LDCT) screening in the high-risk population, in which lung cancer incidence is higher than that of the low-risk population is the predominant diagnostic strategy. Although this has efficiently reduced lung cancer mortality in large randomized trials, LDCT screening has high false-positive rates, resulting in excessive subsequent follow-up procedures and radiation exposure. Complementation of LDCT examination with biofluid-based biomarkers has been documented to increase efficacy, and this type of preliminary screening can potentially reduce potential radioactive damage to low-risk populations and the burden of hospital resources. Several molecular signatures based on components of the biofluid metabolome that can possibly discriminate patients with lung cancer from healthy individuals have been proposed over the past two decades. In the present review, advancements in currently available technologies in metabolomics were reviewed, with particular focus on their possible application in lung cancer screening and early detection.

Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning

Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.

An efficient Pt@MXene platform for the analysis of small-molecule natural products

Small-molecule (m/z<500) natural products have rich biological activity and significant application value thus need to be effectively detected. Surface-assisted laser desorption/ionization mass spectrometry (SALDI MS) has become a powerful detection tool for small-molecule analysis. However, more efficient substrates need to be developed to improve the efficiency of SALDI MS. Thus, platinum nanoparticle-decorated Ti₃C₂ MXene (Pt@MXene) was synthesized in this study as an ideal substrate for SALDI MS in positive ion mode and exhibited excellent performance for the high-throughput detection of small molecules. Compared with using MXene, GO, and CHCA matrix, a stronger signal peak intensity and wider molecular coverage was obtained using Pt@MXene in the detection of small-molecule natural products, with a lower background, excellent salt and protein tolerance, good repeatability, and high detection sensitivity. The Pt@MXene substrate was also successfully used to quantify target molecules in medicinal plants. The proposed method has potentially wide application.

Aptamer-based technology for gastric cancer theranostics

Gastric cancer is one of the most common causes of cancer death worldwide. This cancer exhibits high molecular and phenotype heterogeneity. The overall survival rate for gastric cancer is very low because it is always diagnosed in the advanced stages. Therefore, early detection and treatment are of great significance. Currently, biomedical studies have tapped the potential clinical applicability of aptamer-based technology for gastric cancer diagnosis and targeted therapy. Herein, we summarize the enrichment and evolution of relevant aptamers, followed by documentation of the recent developments in aptamer-based techniques for early diagnosis and precision therapy for gastric cancers.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

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.

Ratiometric electrochemical OR gate assay for NSCLC-derived exosomes

Non-small cell lung cancer (NSCLC) is the most common pathological type of LC and ranks as the leading cause of cancer deaths. Circulating exosomes have emerged as a valuable biomarker for the diagnosis of NSCLC, while the performance of current electrochemical assays for exosome detection is constrained by unsatisfactory sensitivity and specificity. Here we integrated a ratiometric biosensor with an OR logic gate to form an assay for surface protein profiling of exosomes from clinical serum samples. By using the specific aptamers for recognition of clinically validated biomarkers (EpCAM and CEA), the assay enabled ultrasensitive detection of trace levels of NSCLC-derived exosomes in complex serum samples (15.1 particles μL^-1 within a linear range of 10²-10⁸ particles μL^-1). The assay outperformed the analysis of six serum biomarkers for the accurate diagnosis, staging, and prognosis of NSCLC, displaying a diagnostic sensitivity of 93.3% even at an early stage (Stage I). The assay provides an advanced tool for exosome quantification and facilitates exosome-based liquid biopsies for cancer management in clinics.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Functionalization of Mesoporous Silica with a G-A-Mismatched dsDNA Chain for Efficient Identification and Selective Capturing of the MutY Protein

MUTYH adenine DNA glycosylase and its homologous protein (collectively MutY) are typical DNA glycosylases with a [4Fe4S] cluster and a helix-hairpin-helix (HhH) motif in its structure. In the present work, the binding behaviors of the MutY protein to dsDNA containing different base mismatches were investigated. The type and distribution of base mismatch in the dsDNA chain were found to influence the DNA-protein binding interaction greatly. The [4Fe4S] cluster of the MutY protein is able to identify a G-A mismatch in the dsDNA chain specifically by monitoring the anomalies of charge transport in the dsDNA chain, allowing the entrance of the identified dsDNA chain into the internal cavity of the MutY protein and the strong DNA-protein binding at the HhH motif of the protein through multiple H-bonds. The dsDNA chain with a centrally located G-A mismatch is thus functionalized on mesoporous silica (MSN) via amination reaction, and the obtained dsDNA(G-A)@MSN is used as a powerful sorbent for the selective capturing of the MutY protein from complex samples. By using 0.5% NH3·H2O (m/v) as a stripping reagent, efficient isolation of the MutY protein from different cell lines and bacteria is achieved and the recovered MutY protein is demonstrated to maintain favorable DNA adenine glycosylase activity.

Deep Learning of Dual Plasma Fingerprints for High-Performance Infection Classification

Infection classification is the key for choosing the proper treatment plans. Early determination of the causative agents is critical for disease control. Host responses analysis can detect variform and sensitive host inflammatory responses to ascertain the presence and type of the infection. However, traditional host-derived inflammatory indicators are insufficient for clinical infection classification. Fingerprints-based omic analysis has attracted increasing attention globally for analyzing the complex host systemic immune response. A single type of fingerprints is not applicable for infection classification (area under curve (AUC) of 0.550-0.617). Herein, an infection classification platform based on deep learning of dual plasma fingerprints (DPFs-DL) is developed. The DPFs with high reproducibility (coefficient of variation <15%) are obtained at low sample consumption (550 nL native plasma) using inorganic nanoparticle and organic matrix assisted laser desorption/ionization mass spectrometry. A classifier (DPFs-DL) for viral versus bacterial infection discrimination (AUC of 0.775) and coronavirus disease 2019 (COVID-2019) diagnosis (AUC of 0.917) is also built. Furthermore, a metabolic biomarker panel of two differentially regulated metabolites, which may serve as potential biomarkers for COVID-19 management (AUC of 0.677-0.883), is constructed. This study will contribute to the development of precision clinical care for infectious diseases.

Artificial Neural Networks Coupled with MALDI-TOF MS Serum Fingerprinting To Classify and Diagnose Pathological Pain Subtypes in Preclinical Models

Pathological pain subtypes can be classified as either neuropathic pain, caused by a somatosensory nervous system lesion or disease, or nociplastic pain, which develops without evidence of somatosensory system damage. Since there is no gold standard for the diagnosis of pathological pain subtypes, the proper classification of individual patients is currently an unmet challenge for clinicians. While the determination of specific biomarkers for each condition by current biochemical techniques is a complex task, the use of multimolecular techniques, such as matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), combined with artificial intelligence allows specific fingerprints for pathological pain-subtypes to be obtained, which may be useful for diagnosis. We analyzed whether the information provided by the mass spectra of serum samples of four experimental models of neuropathic and nociplastic pain combined with their functional pain outcomes could enable pathological pain subtype classification by artificial neural networks. As a result, a simple and innovative clinical decision support method has been developed that combines MALDI-TOF MS serum spectra and pain evaluation with its subsequent data analysis by artificial neural networks and allows the identification and classification of pathological pain subtypes in experimental models with a high level of specificity.

Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification

Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.

A “One‐Stop Shop” Decision Tree for Diagnosing and Phenotyping Polycystic Ovarian Syndrome on Serum Metabolic Fingerprints

Polycystic ovary syndrome (PCOS) is a common endocrine disease regulated by metabolic disorders, the effective intervention of which depends on diverse phenotypes (e.g., insulin resistance). Serum metabolic fingerprint (SMF) holds promise in characterizing the pathogenesis stress related to diseases; yet, PCOS diagnosis and phenotyping are time‐consuming and challenging due to the lack of an integrated metabolic tool. Here, a nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform is introduced for one‐time serum metabolic fingerprinting and to identify the metabolic heterogeneity associated with obesity in PCOS patients. A decision tree based on the acquired SMFs is constructed, and real‐world simulations on independent internal and external cohorts are performed. The decision tree yields the area under the receiver operating characteristic curves (AUC) of 0.967 for PCOS diagnosis and AUC of 0.898 for phenotyping, respectively. The technical robustness of the “one‐stop shop” decision tree across laboratories is validated for clinical utility. The decision tree aims to improve PCOS management in comparison to clinical assessment, leading to a potential reduction in multiple blood tests and physician workload.

MALDI-TOF Mass Spectrometry in Clinical Analysis and Research

In the decade after being awarded the Nobel Prize in Chemistry in 2002, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) has been widely used as an analytical chemistry tool for the detection of large and small molecules (e.g., polymers, proteins, peptides, nucleic acids, amino acids, lipids, etc.) and for clinical analysis and research (e.g., pathogen identification, genetic disorders screening, cancer diagnosis, etc.). In view of the fast development of MALDI-TOF MS in clinical usage, this review systematically summarizes the most important applications of MALDI-TOF MS in clinical analysis and research by analyzing MALDI TOF MS-related reviews collected in the Web of Science database. On the basis of the analysis of keyword co-occurrence of over 2000 review articles, four themes consisting of "pathogen identification", "disease diagnosis", "nucleic acids analysis", and "small molecules analysis" were found. For each theme, the review further outlined their application implications, analytical methods, and systems as well as limitations that need to be addressed. Overall, the review summarizes and elaborates on the clinical applications of MALDI-TOF MS, providing a comprehensive picture for researchers embarking on MALDI TOF MS-related clinical analysis and research.