Deep convolutional neural network-based 3D fluorescence sensor array for sugar identification in serum based on the oxidase-mimicking property of CuO nanoparticles.

Developing sensor arrays capturing comprehensive fluorescence (FL) spectra from a single probe is crucial for understanding sugar structures with very high similarity in biofluids. Therefore, the analysis of highly similar sugar' structures in biofluids based on the entire FL of a single nanozyme probe needs more concern, which makes the development of novel alternative approaches highly wanted for biomedical and other applications. Herein, a well-designed deep learning model with intrinsic information of 3D FL of CuO nanoparticles (NPs)' oxidase-like activity was developed to classify and predict the concentration of a group of sugars with very similar chemical structures in different media. The findings presented that the overall accuracy of the developed model in classifying the nine selected sugars was (99-100 %), which prompted us to transfer the developed model to predict the concentration of the selected sugars at a concentration range of (1-100 µM). The transferred model also gave excellent results (R2 = 97-100 %). Therefore, the model was extended to other more complex applications, namely the identification of mixtures of sugars in serum and the detection of polysaccharides in different media such as serum and lake water. Notably, LOD for fructose was determined at 4.23 nM, marking a 120-fold decrease compared to previous studies. Our developed model was also compared with other deep learning-based models, and the results have demonstrated remarkable progress. Moreover, the identification of other possible coexisting interference substances in lake water samples was considered. This work marks a significant advancement, opening avenues for the widespread application of sensor arrays integrating nanozymes and deep learning techniques in biomedical and other diverse fields.

Multi-excitation wavelength of gold nanocluster-based fluorescence sensor array for sulfonamides discrimination.

Sulfonamides (SAs) are widely used in many fields because of their advantages, including low price, wide antibacterial spectrum, and high stability. However, their accumulation in the human body leads to a variety of serious diseases. Therefore, it is necessary to design a convenient, effective, and sensitive method to detect SAs. Moreover, the fluorescence excitation spectrum has rich information characteristics, especially for the interaction between fluorophore and quencher via various mechanisms. However, the excitation wavelength-guided sensor array construction does not draw proper attention. To address these issues, we used BSA-AuNCs as a single probe to construct a sensor array for the detection of five SAs. The selected SAs showed different quenching effects on the fluorescence intensities of BSA-AuNCs. The changes in the fluorescence intensity at different excitation wavelengths (λ = 230, 250, and 280 nm) have been applied to construct our sensor array and address the distinguishability between the selected SAs. With helping of pattern recognition methods, five different SAs have been identified at three different concentrations. Additionally, qualitative analysis at different moral ratios and quantitative analysis at nanogram concentrations have been considered. Moreover, the proposed sensor array was successfully used to distinguish between different SAs in commercial milk with an accuracy of 100 %. This study provides a simple and powerful approach to SAs detection. Also, it shows a broad application prospect in the field of food and drug monitoring.

Feature Selection Assists BLSTM for the Ultrasensitive Detection of Bioflavonoids in Different Biological Matrices Based on the 3D Fluorescence Spectra of Gold Nanoclusters.

Rapid and on-site qualitative and quantitative analysis of small molecules (including bioflavonoids) in biofluids are of great importance in biomedical applications. Herein, we have developed two deep learning models based on the 3D fluorescence spectra of gold nanoclusters as a single probe for rapid qualitative and quantitative analysis of eight bioflavonoids in serum. The results proved the efficiency and stability of the random forest-bidirectional long short-term memory (RF-BLSTM) model, which was used only with the most important features after deleting the unimportant features that might hinder the performance of the model in identifying the selected bioflavonoids in serum at very low concentrations. The optimized model achieves excellent overall accuracy (98-100%) in the qualitative analysis of the selected bioflavonoids. Next, the optimized model was transferred to quantify the selected bioflavonoids in serum at nanoscale concentrations. The transferred model achieved excellent accuracy, and the overall determination coefficient (R2) value range was 99-100%. Furthermore, the optimized model achieved excellent accuracies in other applications, including multiplex detection in serum and model applicability in urine. Also, LOD in serum at nanoscale concentration was considered. Therefore, this approach opens the window for qualitative and quantitative analysis of small molecules in biofluids at nanoscale concentrations, which may help in the rapid inclusion of sensor arrays in biomedical and other applications.

Highly Efficient Luminescence from Charge-Transfer Gold Nanoclusters Enabled by Lewis Acid.

Understanding the complicated intramolecular charge transfer (ICT) behaviors of nanomaterials is crucial to the development of high-quality nanoluminophores for various applications. However, the ICT process in molecule-like metal nanoclusters has been rarely explored. Herein, a proton binding-induced enhanced ICT state is discovered in 6-aza-2-thiothymine-protected gold nanoclusters (ATT-AuNCs). Such an excited-state electron transfer process gives rise to the weakened and red-shifted photoluminescence of these nanoclusters. By the joint use of this newfound ICT mechanism and a restriction of intramolecular motion (RIM) strategy, a red shift in the emission maxima of 30 nm with 27.5-fold higher fluorescence quantum efficiency is achieved after introducing rare-earth scandium ion (Sc3+) into ATT-AuNCs. Furthermore, it is found that upon the addition of Sc3+, the photoinduced electron transfer (PET) rate from ATT-AuNCs to minocycline is largely accelerated by forming a donor-bridge-acceptor structure. This paper offers a simple method to modulate the luminescent properties of metal nanoclusters for the rational design of next-generation sensing platforms.

Carboxylated chitosan enabled platinum nanozyme with improved stability and ascorbate oxidase-like activity for a fluorometric acid phosphatase sensor.

Chitosan modification has attracted considerable interest in the nanozyme field last decade. As a chitosan derivative, carboxylated chitosan (CC) has been less explored. Herein, PtNPs with an average size of approximately 3.3 nm and zeta potential of -44.8 ± 0.3 mV (n = 3) have been prepared by using CC as the surface modification (CC-PtNPs). We have carried out an in-depth investigation of CC-PtNPs, including the characterization, colloidal stability, and ascorbate oxidase-like activity. Due to the contribution of carboxylated chitosan, CC-PtNPs present improved colloidal stability and ascorbate oxidase-like activity compared to chitosan-modified Pt nanozyme. Inspired by these results, a fluorometric acid phosphatase sensor was proposed based on the improved performance of CC-PtNPs. This sensor exhibits excellent sensitivity and selectivity towards acid phosphatase in the linear range of 0.25-18 U/L with a low limit of detection (1.31 × 10-3 U/L). The concentration of acid phosphatase in human semen samples has been successfully measured.

Machine learning-based sensor array: full and reduced fluorescence data for versatile analyte detection based on gold nanocluster as a single probe.

Different acquisition data approaches have been used to fetch the fluorescence spectra. However, the comparison between them is rare. Also, the extendability of a sensor array, which can work with heavy metal ions and other types of analytes, is scarce. In this study, we used first- and second-order fluorescent data generated by 6-Aza-2-thiothymine-gold nanocluster (ATT-AuNCs) as a single probe along with machine learning to distinguish between a group of heavy metal ions. Moreover, the dimensionality reduction was carried out for the different acquisition data approaches. In our case, the accuracy of different machine learning algorithms using first-order data outperforms the second-order data before and after the dimensionality reduction. For proving the extendibility of this approach, four anions were used as an example. As expected, the same finding has been found. Furthermore, random forest (RF) showed more stable and accurate results than other models. Also, linear discriminant analysis (LDA) gave acceptable accuracy in the analysis of the high-dimensionality data. Accordingly, using LDA in high-dimensionality data (the first- and second-order data) analysis was highlighted for discrimination between the selected heavy metal ions in different concentrations and in different molar ratios, as well as in real samples. Also, the same method was applied for the anion's discrimination, and LDA gave an excellent separation ability. Moreover, LDA was able to differentiate between all the selected analytes with excellent separation ability. Additionally, the quantitative detection was considered using a wide concentration range of Cd2+, and the LOD was 60.40 nM. Therefore, we believe that our approach opens new avenues for linking analytical chemistry, especially sensor array chemistry, with machine learning.

Fructose oxidase-like activity of CuO nanoparticles supported by phosphate for a tandem catalysis-based fructose sensor.

A surge of nanozymes with oxidase-like activities is emerging in various fields, whereas nanozymes with the ability to catalyze the oxidation of saccharides have less been explored. Herein, CuO nanoparticles (NPs) with phosphate-supported fructose oxidase-like activity have been reported. Notably, reactive oxygen species (ROS) have been confirmed as the products during the process. By coupling the fructose oxidase-like activity with the peroxidase-like activity of CuO NPs, a tandem catalysis-based fructose sensor can be fabricated. In detail, CuO NPs can catalyze the fructose oxidation under O2 to yield ROS (e.g., H2O2, •OH, and O2·-) and effectively decompose H2O2 into ·OH. After that, terephthalic acid can be oxidized by •OH produced from the tandem catalysis to generate a fluorescent product. This sensor shows a linear range toward fructose (0.625-275 µÐœ) with a low limit of detection (0.5 µÐœ), which can be successfully conducted to detect fructose from real samples. Overall, this work aims to expand the catalytic types of nanozymes and provide a desirable fructose sensor.

Deep Learning-Based Sensor Array: 3D Fluorescence Spectra of Gold Nanoclusters for Qualitative and Quantitative Analysis of Vitamin B6 Derivatives.

Vitamin B6 derivatives (VB6Ds) are of great importance for all living organisms to complete their physiological processes. However, their excess in the body can cause serious problems. What is more, the qualitative and quantitative analysis of different VB6Ds may present significant challenges due to the high similarity of their chemical structures. Also, the transfer of deep learning model from one task to a similar task needs to be present more in the fluorescence-based biosensor. Therefore, to address these problems, two deep learning models based on the intrinsic fingerprint of 3D fluorescence spectra have been developed to identify five VB6Ds. The accuracy ranges of a deep neural network (DNN) and a convolutional neural network (CNN) were 94.44-97.77% and 97.77-100%, respectively. After that, the developed models were transferred for quantitative analysis of the selected VB6Ds at a broad concentration range (1-100 µM). The determination coefficient (R2) values of the test set for DNN and CNN were 93.28 and 97.01%, respectively, which also represents the outperformance of CNN over DNN. Therefore, our approach opens new avenues for qualitative and quantitative sensing of small molecules, which will enrich fields related to deep learning, analytical chemistry, and especially sensor array chemistry.

Bis-Schiff base linkage-triggered highly bright luminescence of gold nanoclusters in aqueous solution at the single-cluster level.

Metal nanoclusters (NCs) have been developed as a new class of luminescent nanomaterials with potential applications in various fields. However, for most of the metal NCs reported so far, the relatively low photoluminescence quantum yield (QY) in aqueous solution hinders their applications. Here, we describe the utilization of bis-Schiff base linkages to restrict intramolecular motion of surface motifs at the single-cluster level. Based on Au22(SG)18 (SG: glutathione) NCs, an intracluster cross-linking system was constructed with 2,6-pyridinedicarboxaldehyde (PDA), and water-soluble gold NCs with luminescence QY up to 48% were obtained. The proposed approach for achieving high emission efficiency can be extended to other luminescent gold NCs with core-shell structure. Our results also show that the content of surface-bound Au(I)-SG complexes has a significant impact on the PDA-induced luminescence enhancement, and a high ratio of Au(I)-SG will be beneficial to increasing the photoluminescence intensity of gold NCs.

Gold Nanocluster-Based Fluorometric Banoxantrone Assay Enabled by Photoinduced Electron Transfer.

Monitoring the blood concentration of banoxantrone (AQ4N) is important to evaluate the therapeutic efficacy and side effects of this new anticancer prodrug during its clinical applications. Herein, we report a fluorescence method for AQ4N detection through the modulation of the molecule-like photoinduced electron transfer (PET) behavior of gold nanoclusters (AuNCs). AQ4N can electrostatically bind to the surface of carboxylated chitosan (CC) and dithiothreitol (DTT) co-stabilized AuNCs and quench their fluorescence via a Coulomb interaction-accelerated PET process. Under optimized experimental conditions, the linear range of AQ4N is from 25 to 200 nM and the limit of detection is as low as 5 nM. In addition, this assay is confirmed to be reliable based on its successful use in AQ4N determination in mouse plasma samples. This work offers an effective strategy for AQ4N sensing based on fluorescent AuNCs and widens the application of AuNCs in clinical diagnosis and pharmaceutical analysis.

6-Aza-2-thio-thymine-gold nanoclusters: an excellent candidate in the photoelectrochemical field.

The high performance of the photoelectrochemical (PEC) properties of AuNCs can be achieved with 6-aza-2-thio-thymine-AuNCs (ATT-AuNCs) as a photoactive material. The ATT-AuNCs yielded a cathodic photocurrent density as high as 88 µA cm-2 with O2 as electron acceptor, which is three orders of magnitude higher than those of other AuNCs in aqueous solutions. Moreover, ATT-AuNCs also show a higher carrier density, shorter Debye length, and smaller depletion layer width than those of reported AuNCs. This work not only reveals the PEC performance and mechanism of ATT-AuNCs, but also establishes a framework for in-depth design and studying the PEC performance of AuNCs.

A peroxidase-like activity-based colorimetric sensor array of noble metal nanozymes to discriminate heavy metal ions.

Heavy metal ions (HMIs), including Cu2+, Ag+, Cd2+, Hg2+, and Pb2+ from the environment pose a threat to human beings and can cause a series of life-threatening diseases. Therefore, colorimetric sensors with convenience and flexibility for HMI discrimination are still required. To provide a solution, a peroxidase-like activity-based colorimetric sensor array of citrate-capped noble metal nanozymes (osmium, platinum, and gold) has been fabricated. Some studies reported that some HMIs could interact with the noble metal nanozymes leading to a change in their peroxidase-like activity. This phenomenon was confirmed in our work. Based on this principle, different concentrations of HMIs (Cu2+, Ag+, Cd2+, Hg2+, and Pb2+) were discriminated. Moreover, their practical application has been tested by discriminating HMIs in tap water and SiYu lake water. What is more, as an example of the validity of our method to quantify HMIs at nanomolar concentrations, the LOD of Hg2+ was presented. To sum up, our study not only demonstrates the differentiation ability of this nanozyme sensor array but also gives hints for using nanozyme sensor arrays for further applications.

Protein-Assisted Osmium Nanoclusters with Intrinsic Peroxidase-like Activity and Extrinsic Antifouling Behavior.

Extensive studies have laid the groundwork for understanding peroxidase-like nanozymes. However, improvements are still required before their practical applications. On one hand, it is significant to explore highly reactive nanozymes. On the other hand, it is necessary to avoid fouling formed on the surface of nanozymes, which will affect their activity and the results of H2O2 sensors or H2O2-related applications. Herein, a strategy is reported to design osmium nanoclusters (Os NCs) with the existence of bovine serum albumin (BSA) through biomineralization. BSA-Os NCs were found to possess intrinsic peroxidase-like activity with a high specific activity (6120 U/g). Studies also found that the catalytic activity of BSA-Os NCs was better than those of reported protein-assisted metal nanozymes (e.g., BSA-Pt NPs and BSA-Au NCs). More significantly, BSA has been confirmed as a protective shell to give Os NCs extrinsic antifouling property in some typical ions (e.g., Hg2+, Ag+, Pb2+, I-, Cr6+, Cu2+, Ce3+, S2-, etc.), saline (0-2 M), or protein (0-100 mg/mL) conditions. Under optimal conditions, a colorimetric sensor was established to realize a linear range of H2O2 from 1.25 to 200 µM with a low detection limit of 300 nM. On this basis, remarkable features enable a BSA-Os NCs-based colorimetric sensor to detect H2O2 from complex systems with clear color gradients. Together, this work highlights the advantages of protein-assisted Os nanozymes and provides a paragon for peroxidase-like nanozymes in H2O2-related applications.

Lipid alterations and subtyping maker discovery of lung cancer based on nontargeted tissue lipidomics using liquid chromatography-mass spectrometry.

High morbidity and mortality are still associated with lung cancer, especially non-small cell lung cancer (NSCLC). Although several studies showed abnormality in lung cancer lipidome, the underlying mechanism remains unclear. Therefore, a marker for early diagnosis of NSCLC is highly needed. In this study, a nontargeted lipidomics approach based on liquid chromatography-mass spectrometry was used to analyze the lung tumor tissue (LCT) lipidome to investigate the characteristics of lipid metabolism alterations in lung cancer tissues and find suitable lipid markers for lung cancer and its subtypes. Our results revealed that the most prominent alterations in LCT were the decrease of free fatty acid (FFA) and increase of triglyceride compared with distal noncancerous tissue (DNT). We also identified and verified the combinational lipid markers which can discriminate malignant from benign tissue as well as different NSCLC subtypes. These results not only provide hints in the lipid metabolism dysregulations between malignant and benign tissues but also define the combinational lipid markers to aid clinical diagnosis of lung cancer and its subtypes.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer.

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Serum lipidomic biomarkers for non-small cell lung cancer in nonsmoking female patients.

Lung cancer (Lca) is one of the malignant tumors with the fastest morbidity and mortality increase and the greatest threat to human health and life. The incidence of non-small cell lung cancer (NSCLC) in the nonsmoking female has increased recently. However, its pathogenesis is still unclear, and there is an urgent need for clinical diagnostic biomarkers, especially for early diagnosis. A nontargeted lipidomic approach based on ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS), as well as two machine learning approaches (genetic algorithm and binary logistic regression) was used to screen candidate discriminating lipids and define a combinational lipid biomarker in serum samples to distinguish female patients with NSCLC from healthy controls. Moreover, the candidate biomarkers were verified by using an external validation sample set. Our result revealed that fatty acid (FA) (20:4), FA (22:0) and LPE (20:4) can serve as a combinational biomarker for distinguishing female patients with NSCLC from healthy control with good sensitivity and specificity. Furthermore, this combinational biomarker also showed good performance in distinguishing early-stage NSCLC female patients from a healthy control. We observed that levels of unsaturated fatty acids clearly decreased, while saturated fatty acids and lysophosphatidylethanolamines pronouncedly increased in Lca patients, compared with the healthy controls, which revealed significant disturbance of lipid metabolism in NSCLC females. Our results not only provide hints to the pathological mechanism of NSCLC in nonsmoking female but also supply a combinational lipid biomarker to aid the diagnosis of NSCLC at early-stage.

Metabolomics of lung cancer: Analytical platforms and their applications.

Lung cancer is the leading type of cancer worldwide in terms of the number of new cases and is responsible for the largest number of deaths due to poor prognosis and difficult early detection. Due to its ability to detect numerous small molecular metabolites simultaneously, metabolomics has been widely used for the assessment of global metabolic changes in a living organism to discover candidate biomarkers for cancer diagnosis, investigate the development of cancer, and provide insights into the underlying pathophysiology. This review will mainly describe recent developments in lung cancer metabolomics in terms of early-stage detection, biomarker discovery and mechanism exploration by using nuclear magnetic resonance, liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, and capillary electrophoresis-mass spectrometry in the last 10 years. The sample collection and metabolite extraction methods are also summarized.