Bile metabolic fingerprints distinguish biliary tract cancer from benign biliary diseases.

BACKGROUND AND AIMS: Biliary tract cancers (BTCs) are aggressive gastrointestinal malignancies characterized by a dismal 5-year overall survival rate less than 20%. Current diagnostic modalities suffer from limitations regarding sensitivity and specificity. This study aimed to develop a bile metabolite-based platform for precise discrimination between malignant and benign biliary diseases. APPROACH AND RESULTS: Samples were collected from 336 patients with BTC or benign biliary diseases across three independent cohorts. Untargeted metabolic fingerprinting was performed on 300 bile samples using novel nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI MS). Subsequently, a diagnostic assay was developed based on the exploratory cohort using a selected bile metabolic biomarker panel, with performance evaluated in the validation cohort. Further external validation of disease-specific metabolites from bile samples was conducted in a prospective cohort (n=36) using quantitative analysis. As a result, we established a novel bile-based assay, BileMet, for the rapid and precise detection of malignancies in the biliary tract system with an area under the curve of 0.891. We identified 6 metabolite biomarker candidates and discovered the critical role of the chenodeoxycholic acid glycine conjugate as a protective metabolite associated with BTC. CONCLUSIONS: Our findings confirmed the improved diagnostic capabilities of BileMet assay in a clinical setting. If applied, the BileMet assay enables intraoperative testing and fast medical decision-making for cases with suspected malignancy where brush cytology detection fails to support malignancy, ultimately reducing the economic burden by over 90%.

Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints.

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

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.

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.

Oxygen content-related DNA damage of graphene oxide on human retinal pigment epithelium cells.

Arguments regarding the biocompatibility of graphene-based materials (GBMs) have never ceased. Particularly, the genotoxicity (e.g., DNA damage) of GBMs has been considered the greatest risk to healthy cells. Detailed genotoxicity studies of GBMs are necessary and essential. Herein, we present our recent studies on the genotoxicity of most widely used GBMs such as graphene oxide (GO) and the chemically reduced graphene oxide (RGO) toward human retinal pigment epithelium (RPE) cells. The genotoxicity of GO and RGOs against ARPE-19 (a typical RPE cell line) cells was investigated using the alkaline comet assay, the expression level of phosphorylated p53 determined via Western blots, and the release level of reactive oxygen species (ROS). Our results suggested that both GO and RGOs induced ROS-dependent DNA damage. However, the DNA damage was enhanced following the reduction of the saturated C-O bonds in GO, suggesting that surface oxygen-containing groups played essential roles in the reduced genotoxicity of graphene and had the potential possibility to reduce the toxicity of GBMs via chemical modification.

Probing Interactions between Metal-Organic Frameworks and Freestanding Enzymes in a Hollow Structure.

It has been reported that the biological functions of enzymes could be altered when they are encapsulated in metal-organic frameworks (MOFs) due to the interactions between them. Herein, we probed the interactions of catalase in solid and hollow ZIF-8 microcrystals. The solid sample with confined catalase is prepared through a reported method, and the hollow sample is generated by hollowing the MOF crystals, sealing freestanding enzymes in the central cavities of hollow ZIF-8. During the hollowing process, the samples were monitored by small-angle X-ray scattering (SAXS) spectroscopy, electron microscopy, powder X-ray diffraction (PXRD), and nitrogen sorption. The interfacial interactions of the two samples were studied by infrared (IR) and fluorescence spectroscopy. IR study shows that freestanding catalase has less chemical interaction with ZIF-8 than confined catalase, and a fluorescence study indicates that the freestanding catalase has lower structural confinement. We have then carried out the hydrogen peroxide degradation activities of catalase at different stages and revealed that the freestanding catalase in hollow ZIF-8 has higher activity.

Design of Hierarchical Beads for Efficient Label-Free Cell Capture.

Defined hierarchical materials promise cell analysis and call for application-driven design in practical use. The further issue is to develop advanced materials and devices for efficient label-free cell capture with minimum instrumentation. Herein, the design of hierarchical beads is reported for efficient label-free cell capture. Silica nanoparticles (size of ≈15 nm) are coated onto silica spheres (size of ≈200 nm) to achieve nanoscale surface roughness, and then the rough silica spheres are combined with microbeads (≈150-1000 µm in diameter) to assemble hierarchical structures. These hierarchical beads are built via electrostatic interaction, covalent bonding, and nanoparticle adherence. Further, after functionalization by hyaluronic acid (HA), the hierarchical beads display desirable surface hydrophilicity, biocompatibility, and chemical/structural stability. Due to the controlled surface topology and chemistry, HA-functionalized hierarchical beads afford high cell capture efficiency up to 98.7% in a facile label-free manner. This work guides the development of label-free cell capture techniques and contributes to the construction of smart interfaces in bio-systems.

Identification and validation of serum metabolite biomarkers for endometrial cancer diagnosis.

Endometrial cancer (EC) stands as the most prevalent gynecological tumor in women worldwide. Notably, differentiation diagnosis of abnormity detected by ultrasound findings (e.g., thickened endometrium or mass in the uterine cavity) is essential and remains challenging in clinical practice. Herein, we identified a metabolic biomarker panel for differentiation diagnosis of EC using machine learning of high-performance serum metabolic fingerprints (SMFs) and validated the biological function. We first recorded the high-performance SMFs of 191 EC and 204 Non-EC subjects via particle-enhanced laser desorption/ionization mass spectrometry (PELDI-MS). Then, we achieved an area-under-the-curve (AUC) of 0.957-0.968 for EC diagnosis through machine learning of high-performance SMFs, outperforming the clinical biomarker of cancer antigen 125 (CA-125, AUC of 0.610-0.684, p < 0.05). Finally, we identified a metabolic biomarker panel of glutamine, glucose, and cholesterol linoleate with an AUC of 0.901-0.902 and validated the biological function in vitro. Therefore, our work would facilitate the development of novel diagnostic biomarkers for EC in clinics.

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.

Diagnosis of Esophageal Squamous Cell Carcinoma by High-Performance Serum Metabolic Fingerprints: A Retrospective Study.

Esophageal squamous cell carcinoma (ESCC) is a highly prevalent and aggressive malignancy, and timely diagnosis of ESCC contributes to an increased cancer survival rate. However, current detection methods for ESCC mainly rely on endoscopic examination, limited by a relatively low participation rate. Herein, ferric-particle-enhanced laser desorption/ionization mass spectrometry (FPELDI MS) is utilized to record the serum metabolic fingerprints (SMFs) from a retrospective cohort (523 non-ESCC participants and 462 ESCC patients) to build diagnostic models toward ESCC. The PFELDI MS achieved high speed (≈30 s per sample), desirable reproducibility (coefficients of variation < 15%), and high throughput (985 samples with ≈124 200 data points for each spectrum). Desirable diagnostic performance with area-under-the-curves (AUCs) of 0.925-0.966 is obtained through machine learning of SMFs. Further, a metabolic biomarker panel is constructed, exhibiting superior diagnostic sensitivity (72.2-79.4%, p < 0.05) as compared with clinical protein biomarker tests (4.3-22.9%). Notably, the biomarker panel afforded an AUC of 0.844 (95% confidence interval [CI]: 0.806-0.880) toward early ESCC diagnosis. This work highlighted the potential of metabolic analysis for accurate screening and early detection of ESCC and offered insights into the metabolic characterization of diseases including but not limited to ESCC.

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.

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.

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.

Multivariate time-series classification with hierarchical variational graph pooling.

In recent years, multivariate time-series classification (MTSC) has attracted considerable attention owing to the advancement of sensing technology. Existing deep-learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, focus primarily on the temporal dependency of a single time series. Based on this, complex pairwise dependencies among multivariate variables can be better described using advanced graph methods, where each variable is regarded as a node in the graph, and their dependencies are regarded as edges. Furthermore, current spatial-temporal modeling (e.g., graph classification) methodologies based on graph neural networks (GNNs) are inherently flat and cannot hierarchically aggregate node information. To address these limitations, we propose a novel graph-pooling-based framework, MTPool, to obtain an expressive global representation of MTS. We first convert MTS slices into graphs using the interactions of variables via a graph structure learning module and obtain the spatial-temporal graph node features via a temporal convolutional module. To obtain global graph-level representation, we design an "encoder-decoder"-based variational graph pooling module to create adaptive centroids for cluster assignments. Then, we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node. Finally, a differentiable classifier uses this coarsened representation to obtain the final predicted class. Experiments on ten benchmark datasets showed that MTPool outperforms state-of-the-art strategies in the MTSC task.

Glaucoma Characterization by Machine Learning of Tear Metabolic Fingerprinting.

Glaucoma is a common optic neuropathy disease affecting over 76 million people. Both timely diagnosis and progression monitoring are critical but challenging. Conventional characterization of glaucoma needs a combination of methods, calling for tedious procedures and experienced doctors. Herein, a platform through machine learning of tear metabolic fingerprinting (TMF) using nanoparticle enhanced laser desorption-ionization mass spectrometry is built. Direct TMF is obtained noninvasively, with fast speed and high reproducibility, using trace tear samples (down to 10 nL). Consequently, glaucoma patients are screened against healthy controls with the area under the curve (AUC) of 0.866, through machine learning of TMF. Further, primary open-angle glaucoma (POAG) is differentiated from primary angle-closure glaucoma (PACG) and an early-stage POAG is identified. Finally, a biomarker panel of six metabolites for glaucoma characterization (including screening, subtyping, and early diagnosis) with AUC of 0.827-0.891 is constructed, showing related metabolic pathways. The work will provide insights into eye diseases not limited to glaucoma.

Monitoring Retinoblastoma by Machine Learning of Aqueous Humor Metabolic Fingerprinting.

The most common intraocular pediatric malignancy, retinoblastoma (RB), accounts for ≈10% of cancer in children. Efficient monitoring can enhance living quality of patients and 5-year survival ratio of RB up to 95%. However, RB monitoring is still insufficient in regions with limited resources and the mortality may even reach over 70% in such areas. Here, an RB monitoring platform by machine learning of aqueous humor metabolic fingerprinting (AH-MF) is developed, using nanoparticle enhanced laser desorption/ionization mass spectrometry (LDI MS). The direct AH-MF of RB free of sample pre-treatment is recorded, with both high reproducibility (coefficient of variation < 10%) and sensitivity (low to 0.3 pmol) at sample volume down to 40 nL only. Further, early and advanced RB patients with area-under-the-curve over 0.9 and accuracy over 80% are differentiated, through machine learning of AH-MF. Finally, a metabolic biomarker panel of 7 metabolites through accurate MS and tandem MS (MS/MS) with pathway analysis to monitor RB is identified. This work can contribute to advanced metabolic analysis of eye diseases including but not limited to RB and screening of new potential metabolic targets toward therapeutic intervention.

Enhanced Oral NO Delivery through Bioinorganic Engineering of Acid-Sensitive Prodrug into a Transformer-like DNIC@MOF Microrod.

Nitric oxide (NO) is an endogenous gasotransmitter regulating alternative physiological processes in the cardiovascular system. To achieve translational application of NO, continued efforts are made on the development of orally active NO prodrugs for long-term treatment of chronic cardiovascular diseases. Herein, immobilization of NO-delivery [Fe2(µ-SCH2CH2COOH)2(NO)4] (DNIC-2) onto MIL-88B, a metal-organic framework (MOF) consisting of biocompatible Fe3+ and 1,4-benzenedicarboxylate (BDC), was performed to prepare a DNIC@MOF microrod for enhanced oral delivery of NO. In simulated gastric fluid, protonation of the BDC linker in DNIC@MOF initiates its transformation into a DNIC@tMOF microrod, which consisted of DNIC-2 well dispersed and confined within the BDC-based framework. Moreover, subsequent deprotonation of the BDC-based framework in DNIC@tMOF under simulated intestinal conditions promotes the release of DNIC-2 and NO. Of importance, this discovery of transformer-like DNIC@MOF provides a parallel insight into its stepwise transformation into DNIC@tMOF in the stomach followed by subsequent conversion into molecular DNIC-2 in the small intestine and release of NO in the bloodstream of mice. In comparison with acid-sensitive DNIC-2, oral administration of DNIC@MOF results in a 2.2-fold increase in the oral bioavailability of NO to 65.7% in mice and an effective reduction of systolic blood pressure (SBP) to a ΔSBP of 60.9 ± 4.7 mmHg in spontaneously hypertensive rats for 12 h.

NIR-triggered upconversion nanoparticles@thermo-sensitive liposome hybrid theranostic nanoplatform for controlled drug delivery.

Posterior segment ocular diseases are highly prevalent worldwide due to the lack of suitable noninvasive diagnostic and therapeutic tactics. Herein, concerning this predicament, we designed a hybrid retina-targeted photothermal theranostic nanoplatform (UCNPs@Bi@SiO2@GE HP-lips), based on the unique upconversion luminescence (UCL) imaging of upconversion nanoparticles (UCNPs), efficient photothermal conversion ability of Bi nanoparticles, and thermal-induced phase transition properties of the liposomes (lips). The nanoplatform was functionalized with penetratin (PNT) and hyaluronic acid (HA), to obtain retina-targeted liposomes (HP-lips). Lipophilic genistein (GE) was entrapped into the liposomes (GE HP-lips). An in vitro release study showed NIR irradiation could photothermally trigger controlled release of GE from the liposomal platform. Moreover, cellular uptake evaluation via UCL imaging demonstrated UCNPs@Bi@SiO2@GE HP-lips represented the brightest UCL, compared with other formulations, which is beneficial for the accurate evaluation of the prognosis and severity of angiogenesis-related posterior segment disorders. Therefore, UCNPs@Bi@SiO2@GE HP-lips exhibit promising potential as a theranostic nanoplatform for posterior segment ocular diseases.

Bioinspired Synthesis of Ce1-x O2:x%Cu2+ Nanobelts for CO Oxidation and Organic Dye Degradation.

Ce1-x O2:x%Cu2+ nanobelts were bioinspired, designed, and fabricated using commercial filter papers as scaffolds by adding Cu(NO3)2 in the original sol solution of CeO2 nanobelts, which display excellent catalyst properties for CO oxidation and photocatalytic activity for organic dyes. Compared with pure CeO2, CuO belts were synthesized using the same method and the corresponding Ce0.5O2:50%Cu2+ bulk materials were synthesized without filter paper as scaffolds; the synthesized Ce1-x O2:x%Cu2+ nanobelts, especially Ce0.5O2:50%Cu2+ nanobelts, can decrease the reaction temperature of CO to CO2 at 100 °C with the conversion rate of 100%, much lower than the formerly reported kinds of Ce1-x O2:x%Cu2+ catalysts. Meanwhile, the synthesized Ce1-x O2:x%Cu2+ nanobelts also display better photocatalytic activity for organic dyes. All of these results provide useful information for the potential applications of the synthesized Ce1-x O2:x%Cu2+ nanobelts in catalyst fields.