Significance Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction ( P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.

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.

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.

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.

materials with diverse nanoshell structures can be emerging candidates to prepare hierarchical materials owing to the unique bio-interfaces, [1b,7] but present core-shell materials are usually in the nanoscale or sub-micrometer size limiting their general application. [3c,8] Considering the above aspects, assembling of hierarchical core-shell materials with microsizes for mass production is still very challenging and calling for applicationdriven design toward practical use.Cell analysis plays a key role in biomedical research for diagnostic purpose. [9] In real case cell analysis, cell capture tends to be essential owing to the high complexity of biological specimens. [9b,c,10] To date, there are two main categories including prelabeling [10a,11] and labelfree [9a,12] process for cell capture. Compared to the labeling process that needs specific probes (such as antibodies), label-free cell capture represents the next generation tool and usually requires smart materials or devices. [6c,13] Ideal materials and devices for efficient label-free cell capture may offer: 1) surface topology with strong adherence to cell membranes; [14] 2) surface chemistry with high biocompatibility and affinity; [3c,12a,15] 3) specific size to avoid cellular endocytosis; [16] and 4) structural stability during the whole cell capture process. [6c,11b,12a,17] Until now, most existing label-free techniques only addressed parts of these issues and usually applied in imaging or delivery. Therefore, it would be desirable to construct newer materials and devices as efficient platforms for label-free cell capture.In this work, we designed hierarchical beads for efficient label-free cell capture, as shown in Figure 1a. We coated silica nanoparticles (size of ≈15 nm) onto silica spheres (size of ≈200 nm) to achieve nanoscale surface roughness, and then combined the rough silica spheres with microbeads (≈150-1000 µm in diameter) to assemble hierarchical structures. We built complex hierarchical beads via electrostatic interaction, covalent bonding, and nanoparticle adherence. Further after functionalization by hyaluronic acid (HA), the hierarchical microbeads displayed desirable surface hydrophilicity, biocompatibility, and chemical/structural stability. Due to the controlled surface topology and chemistry, the functionalized microbeads showed high cell capture efficiency of 87.9-98.7% in a label-free manner. Our work contributed to the design of Defined hierarchical materials promise cell analysis and call for applicationdriven 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 hierarch...

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Advanced detection of biomarkers in biofluids plays an important role in disease diagnosis and prognosis. Current techniques with pre-labelling suffer from high cost and complicated operation, etc. Herein, we designed al abel-free electrochemical biosensor for rapid detection of transferrin receptor with desirable linear range, sensitivity,s pecificity, reproducibility,a nd stability for practical applications.

ObjectiveMetabolic 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.DesignWe 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).ResultsWe 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.ConclusionWe developed and validated diagnostic and prognostic models for GC, which also contribute to advanced metabolic analysis towards diseases, including but not limited to GC.