Abstract Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject’s phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs. 

/pdf/small-molecule-metabolites-discovery-of-biomarkers-and-3d30vx3k.pdf

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Biomarkers are crucial biological indicators in medical diagnostics and therapy. However, the process of biomarker discovery and validation is hindered by a lack of standardized protocols for analytical studies, storage and sample collection. Wearable chemical sensors provide a real-time, non-invasive alternative to typical laboratory blood analysis, and are an effective tool for exploring novel biomarkers in alternative body fluids, such as sweat, saliva, tears and interstitial fluid. These devices may enable remote at-home personalized health monitoring and substantially reduce the healthcare costs. This Review introduces criteria, strategies and technologies involved in biomarker discovery using wearable chemical sensors. Electrochemical and optical detection techniques are discussed, along with the materials and system-level considerations for wearable chemical sensors. Lastly, this Review describes how the large sets of temporal data collected by wearable sensors, coupled with modern data analysis approaches, would open the door for discovering new biomarkers towards precision medicine. 

/pdf/wearable-chemical-sensors-for-biomarker-discovery-in-the-1hkxiyze.pdf

Wearable chemical sensors for biomarker discovery in the omics era

Janus particles with an anisotropic structure have emerged as a focus of intensive research due to their diverse composition and surface chemistry, which show excellent performance in various fields, especially in biomedical applications. In this review, we briefly introduce the structures, composition, and properties of Janus particles, followed by a summary of their biomedical applications. Then we review several design strategies including morphology, particle size, composition, and surface modification, that will affect the performance of Janus particles. Subsequently, we explore the synthetic methodologies of Janus particles, with an emphasis on the most prevalent synthetic method (surface nucleation and seeded growth). Following this, we highlight Janus particles in biomedical applications, especially in drug delivery, bio-imaging, and bio-sensing. Finally, we will consider the current challenges the materials face with perspectives in the future directions.

Janus particles: design, preparation, and biomedical applications.

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics. Early diagnosis significantly improves the probability of successful cancer therapy. Here, the authors develop a technique to analyse serum metabolites and define a biomarker panel for early-stage lung adenocarcinoma diagnosis.

/pdf/machine-learning-of-serum-metabolic-patterns-encodes-early-3dfe6uvdb8.pdf

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

High-throughput metabolic analysis is of significance in diagnostics, while tedious sample pretreatment has largely hindered its clinic application. Herein, we designed FeOOH@ZIF-8 composites with enhanced ionization efficiency and size-exclusion effect for laser desorption/ionization mass spectrometry (LDI-MS)-based metabolic diagnosis of gynecological cancers. The FeOOH@ZIF-8-assisted LDI-MS achieved rapid, sensitive, and selective metabolic fingerprints of the native serum without any enrichment or purification. Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers (GCs) from healthy controls and also differentiated three major subtypes of GCs. Given the low cost, high-throughput, and easy operation, our approach brings a new dimension to disease analysis and classification.

/pdf/feooh-metal-organic-framework-core-satellite-nanocomposites-3aui9isl5p.pdf

FeOOH@Metal–Organic Framework Core–Satellite Nanocomposites for the Serum Metabolic Fingerprinting of Gynecological Cancers

https://onlinelibrary.wiley.com/doi/pdf/10.1002/smll.201970020

Bacteria Inhibition: Detection and Inhibition of Bacteria on a Dual-Functional Silver Platform (Small 3/2019)

We designed a novel polymer-metal oxide nanoparticles matrix for the detection of the metabolites analyzed using laser desorption/ionization mass spectrometry (MALDI MS). These nanoparticles were sensitive towards the small molecules. Due to their unique surface morphology and physicochemical properties, these nanoparticles demonstrated superior multiplicity, salt and protein tolerance in the analysis of small molecules. Our research suggested the utility of conductive polymers as potential matrix in the LDI MS detection.

Polymer-metal Oxide Nanoparticles for Small Metabolite Detection

Detection and inhibition of bacteria are universally required in clinics and daily life for health care. Developing a dual-functional material is challenging and in demand, engaging advanced applications for both defined bioanalysis and targeted biotoxicity. Herein, magnetic silver nanoshells are designed as a multifunctional platform for the detection and inhibition of bacteria. The optimized magnetic silver nanoshells enable direct laser desorption/ionization mass spectrometry based metabolic analysis of bacteria (≈10 µL-1 ), in complex biofluids. The serum infection process (0-10 h) is monitored by statistics toward clinical classification. Moreover, magnetic silver nanoshells facilitate surface adhesion on bacteria due to nanoscale surface roughness and thus display long-term antibacterial effects. Bacteria metabolism is studied with metabolic biomarkers (e.g., malate and lysine) identified during inhibition, showing cell membrane destruction and dysfunctional protein synthesis mechanisms. This work not only guides the design of material-based approaches for bioanalysis and biotoxicity, but contributes to bacteria-related diagnosis by using specific metabolic biomarkers for sensitive detection and new insights by monitoring metabolomic change of bacteria for antibacterial applications.

Detection and Inhibition of Bacteria on a Dual-Functional Silver Platform

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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Bacteria Inhibition: Detection and Inhibition of Bacteria on a Dual-Functional Silver Platform (Small 3/2019)

Polymer-metal Oxide Nanoparticles for Small Metabolite Detection

Detection and Inhibition of Bacteria on a Dual-Functional Silver Platform