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.

Alkaline phosphatase-activated prodrug system based on a bifunctional CuO NP tandem nanoenzyme for on-demand bacterial inactivation and wound disinfection.

Nanozymes are promising antimicrobials, as they produce reactive oxygen species (ROS). However, the intrinsic lack of selectivity of ROS in distinguishing normal flora from pathogenic bacteria deprives nanozymes of the necessary selectivities of ideal antimicrobials. Herein, we exploit the physiological conditions of bacteria (high alkaline phosphatase (ALP) expression) using a novel CuO nanoparticle (NP) nanoenzyme system to initiate an ALP-activated ROS prodrug system for use in the on-demand precision killing of bacteria. The prodrug strategy involves using 2-phospho-L-ascorbic acid trisodium salt (AAP) that catalyzes the ALP in pathogenic bacteria to generate ascorbic acid (AA), which is converted by the CuO NPs, with intrinsic ascorbate oxidase- and peroxidase-like activities, to produce ROS. Notably, the prodrug system selectively kills Escherichia coli (pathogenic bacteria), with minimal influence on Staphylococcus hominis (non-pathogenic bacteria) due to their different levels of ALP expression. Compared to the CuO NPs/AA system, which generally depletes ROS during storage, CuO NPs/AAP exhibits a significantly higher stability without affecting its antibacterial activity. Furthermore, a rat model is used to indicate the applicability of the CuO NPs/AAP fibrin gel in wound disinfection in vivo with negligible side effects. This study reveals the therapeutic precision of this bifunctional tandem nanozyme platform against pathogenic bacteria in ALP-activated conditions.

Enzyme-activatable charge transfer in gold nanoclusters.

Surface-protecting ligands, as a major component of metal nanoclusters (MNCs), can dominate molecular characteristics, performance behaviors, and biological properties of MNCs, which brings diversity and flexibility to the nanoclusters and largely promotes their applications in optics, electricity, magnetism, catalysis, biology, and other fields. We report herein the design of a new kind of water-soluble luminescent gold nanoclusters (AuNCs) for enzyme-activatable charge transfer (CT) based on the ligand engineering of AuNCs with 6-mercaptopurine ribonucleoside (MPR). This elaborately designed cluster, Au5(MPR)2, can form a stable intramolecular CT state after light excitation, and exhibits long-lived color-tunable phosphorescence. After the cleavage by purine nucleoside phosphorylase (PNP), the CT triplet state can be easily directed to a low-lying energy level, leading to a bathochromic shift of the emission band accompanied by weaker and shorter-lived luminescence. Remarkably, these ligand-engineered AuNCs show high affinity towards PNP as well as decent performance for analyzing and visualizing enzyme activity and related drugs. The work of this paper provides a good example for diversifying physicochemical properties and application scenarios of MNCs by rational ligand engineering, which will facilitate future interest and new strategies to precisely engineer solution-based nanocluster materials.

Multi-targeted nanoarrays for early broad-spectrum detection of lung cancer based on blood biopsy of tumor exosomes.

Liquid biopsies utilizing tumor exosomes offer a noninvasive approach for cancer diagnosis. However, validation studies consistently report that in the early stages of cancer, the secretion of exosomes by cancer cells is relatively low, while bodily fluids exhibit a high abundance of other interfering biomolecules. Additionally, target mutations or differences in biomarker expression among various lung cancer subtypes may contribute to detection failures. In this study, we propose a targeted nanoarray-based early cancer diagnostic approach for multiple subtypes of lung cancer. The targeted nanoarray was constructed by modifying five targeting aptamers onto mesoporous silica nanoparticles through the conjugation between amino and carboxyl groups. The flow cytometry experiments demonstrated the specific recognition ability of the targeted nanoarray to tumor exosomes in PBS, even at biomarker expression levels as low as 1.5 %. Moreover, the TEM results indicated that the targeted nanoarray could isolate tumor exosomes in the blood of tumor-bearing mice. Furthermore, the targeted nanoarray could detect tumor exosomes in the blood of various lung cancer bearing mice, including at the early stages of cancer, which has just been established for 7 days. Overall, the targeted nanoarray represents a promising tool for the early detection of various subtypes of lung cancer.

Glucose Oxidase Energized Osmium with Dual-Active Centers and Triple Enzyme Activities for Infected Diabetic Wound Management.

Diabetic wounds are susceptible to bacterial infections, largely linked to high blood glucose levels (hyperglycemia). To treat such wounds, enzymes like glucose oxidase (GOx) can be combined with nanozymes (nanomaterials mimic enzymes) to use glucose effectively for purposes. However, there is still room for improvement in these systems, particularly in terms of process simplification, enzyme activity regulation, and treatment effects. Herein, the approach utilizes GOx to directly facilitate the biomineralized growth of osmium (Os) nanozyme (GOx-OsNCs), leading to dual-active centers and remarkable triple enzyme activities. Initially, GOx-OsNCs use vicinal dual-active centers, enabling a self-cascaded mechanism that significantly enhances glucose sensing performance compared to step-by-step reactions, surpassing the capabilities of other metal sources such as gold and platinum. In addition, GOx-OsNCs are integrated into a glucose-sensing gel, enabling instantaneous visual feedback. In the treatment of infected diabetic wounds, GOx-OsNCs exhibit multifaceted benefits by lowering blood glucose levels and exhibiting antibacterial properties through the generation of hydroxyl free radicals, thereby expediting healing by fostering a favorable microenvironment. Furthermore, the catalase-like activity of GOx-OsNCs aids in reducing oxidative stress, inflammation, and hypoxia, culminating in improved healing outcomes. Overall, this synergistic enzyme-nanozyme blend is user-friendly and holds considerable promise for diverse applications.

Unraveling a Concerted Proton-Coupled Electron Transfer Pathway in Atomically Precise Gold Nanoclusters.

Metal nanoclusters (MNCs) represent a promising class of materials for catalytic carbon dioxide and proton reduction as well as dihydrogen oxidation. In such reactions, multiple proton-coupled electron transfer (PCET) processes are typically involved, and the current understanding of PCET mechanisms in MNCs has primarily focused on the sequential transfer mode. However, a concerted transfer pathway, i.e., concerted electron-proton transfer (CEPT), despite its potential for a higher catalytic rate and lower reaction barrier, still lacks comprehensive elucidation. Herein, we introduce an experimental paradigm to test the feasibility of the CEPT process in MNCs, by employing Au18(SR)14 (SR denotes thiolate ligand), Au22(SR)18, and Au25(SR)18- as model clusters. Detailed investigations indicate that the photoinduced PCET reactions in the designed system proceed via an CEPT pathway. Furthermore, the rate constants of gold nanoclusters (AuNCs) have been found to be correlated with both the size of the cluster and the flexibility of the Au-S framework. This newly identified PCET behavior in AuNCs is prominently different from that observed in semiconductor quantum dots and plasmonic metal nanoparticles. Our findings are of crucial importance for unveiling the catalytic mechanisms of quantum-confined metal nanomaterials and for the future rational design of more efficient catalysts.

A colorimetric sensing strategy based on chitosan-stabilized platinum nanoparticles for quick detection of α-glucosidase activity and inhibitor screening.

α-Glucosidase (α-Glu) is implicated in the progression and pathogenesis of type II diabetes (T2D). In this study, we developed a rapid colorimetric technique using platinum nanoparticles stabilized by chitosan (Ch-PtNPs) to detect α-Glu activity and its inhibitor. The Ch-PtNPs facilitate the conversion of 3,3',5,5'-tetramethylbenzidine (TMB) into oxidized TMB (oxTMB) in the presence of dissolved O2. The catalytic hydrolysis of 2-O-α-D-glucopyranosyl-L-ascorbic acid (AA-2G) by α-Glu produces ascorbic acid (AA), which reduces oxTMB to TMB, leading to the fading of the blue color. However, the presence of α-Glu inhibitors (AGIs) hinders the generation of AA, allowing Ch-PtNPs to re-oxidize colorless TMB back to blue oxTMB. This unique phenomenon enables the colorimetric detection of α-Glu activity and AGIs. The linear range for α-Glu was found to be 0.1-1.0 U mL-1 and the detection limit was 0.026 U mL-1. Additionally, the half-maximal inhibition value (IC50) for acarbose, an α-Glu inhibitor, was calculated to be 0.4769 mM. Excitingly, this sensing platform successfully detected α-Glu activity in human serum samples and effectively screened AGIs. These promising findings highlight the potential application of the proposed strategy in clinical diabetes diagnosis and drug discovery.

Micelle-mediated chemiluminescence of 6-aza-2-thiothymine-protected gold nanoclusters for carbazochrome sodium sulfonate detection.

Chemiluminescence (CL) intensity of luminol-H2O2 system was dramatically enhanced by cetyltrimethylammonium bromide (CTAB) micelle-mediated 6-aza-2-thiothymine-protected gold nanoclusters (ATT-AuNCs). It is proved that spherical micelles of CTAB in aqueous solution improved the dispersity of ATT-AuNCs, thus enhancing their catalytic activity, which brought in the increased CL intensity of luminol-H2O2 system. Carbazochrome sodium sulfonate (CSS) with a hemostatic containing tetrahydroindole structure broke the spherical micelles and notably quenched the CL intensity of luminol-H2O2-CTAB-ATT AuNCs system. Based on these results, a simple, fast, and sensitive CL method has been developed for the detection of CSS with a linear range of 0.25-25 µM and a detection limit of 0.11 µM. The method has also been successfully applied to the determination of CSS in serum with satisfied recoveries in the range of 89.6 % to 103.7 %. This study not only provides an effective approach for CSS detection but also paves the way for AuNCs-based CL applications.

6-Aza-2-Thiothymine-Capped Gold Nanoclusters as Robust Antimicrobial Nanoagents for Eradicating Multidrug-Resistant Escherichia coli Infection.

Multidrug-resistant bacterial infections, especially those caused by multidrug-resistant Escherichia coli (E. coli) bacteria, are an ever-growing threat because of the shrinking arsenal of efficacious antibiotics. Therefore, it is urgently needed to develop a kind of novel, long-term antibacterial agent effectively overcome resistant bacteria. Herein, we present a novel designed antibacterial agent-6-Aza-2-thiothymine-capped gold nanoclusters (ATT-AuNCs), which show excellent antibacterial activity against multidrug-resistant E. coli bacteria. The prepared AuNCs could permeabilize into the bacterial cell membrane via binding with a bivalent cation (e.g., Ca2+), followed by the generation of reactive oxygen species (e.g., •OH and •O2-), ultimately resulting in protein leakage from compromised cell membranes, inducing DNA damage and upregulating pro-oxidative genes intracellular. The AuNCs also speed up the wound healing process without noticeable hemolytic activity or cytotoxicity to erythrocytes and mammalian tissue. Altogether, the results indicate the great promise of ATT-AuNCs for treating multidrug-resistant E. coli bacterial infection.

Poricoic acid A induces mitophagy to ameliorate podocyte injury in diabetic kidney disease via downregulating FUNDC1.

Diabetic kidney disease (DKD) is a devastating complication of diabetes mellitus (DM) and is the most prevalent chronic kidney disease (CKD). Poricoic acid A (PAA), a component isolated from Traditional Chinese Medicine (TCM) Poria cocos, has hypoglycaemic and anti-fibrosis effects. However, the role of PAA in DKD remains largely unclear. To mimics an in vitro model of DKD, the mouse podocyte MPC5 cells were treated with high glucose (25 mM; HG) for 24 h. CCK-8 and flow cytometry assays were conducted for assessing MPC5 cell viability and apoptosis. Meanwhile, streptozotocin (STZ) was used to induce experimental DKD in mice by intraperitoneal injection. PAA notably inhibited the apoptosis and inflammation, reduced the generation of ROS, and elevated the MMP level in HG-treated MPC5 cells. Moreover, PAA obviously reduced blood glucose and urine protein levels, inhibited renal fibrosis in DKD mice. Meanwhile, PAA markedly increased LC3 and ATG5 levels and declined p62 and FUNDC1 levels in HG-treated MPC5 cells and in the kidney tissues of DKD mice, leading to the activation of cell mitophagy. Furthermore, the downregulation of FUNDC1 also inhibited apoptosis, inflammation, and promoted mitophagy in HG-treated MPC5 cells. As expected, the knockdown of FUNDC1 further enhanced the protective role of PAA in MPC5 cells following HG treatment, indicating that induction of mitophagy could attenuate podocyte injury. Collectively, PAA could exert beneficial effects on podocyte injury in DKD by promoting mitophagy via downregulating FUNDC1. These findings suggested that PAA may have great potential in alleviating kidney injury in DKD.

Promoting the healing of methicillin-resistant Staphylococcus aureus-infected wound by a multi-target antimicrobial AIEgen of 6-Aza-2-thiothymine-decorated gold nanoclusters.

The use of conventional antibiotic therapies is in question owing to the emergence of drug-resistant pathogenic bacteria. Therefore, novel, highly efficient antibacterial agents to effectively overcome resistant bacteria are urgently needed. Accordingly, in this work, we described a novel class luminogen of 6-Aza-2-thiothymine-decorated gold nanoclusters (ATT-AuNCs) with aggregation-induced emission property that possessed potent antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA). Scanning electron microscopy was performed to investigate the interactions between ATT-AuNCs and MRSA. In addition, ATT-AuNCs exhibited excellent ROS generation efficiency and could effectively ablate MRSA via their internalization to the cells. Finally, tandem mass tag-labeling proteome analysis was carried out to investigate the differential expression proteins in MRSA strains. The results suggested that ATT-AuNCs killed MRSA cells through altering the expression of multiple target proteins involved in DNA replication, aminoacyl-tRNA synthesis, peptidoglycan and arginine biosynthesis metabolism. Parallel reaction monitoring technique was further used for the validation of these proteome results. ATT-AuNCs could also be served as a wound-healing agent and accelerate the healing process. Overall, we proposed ATT-AuNCs could serve as a robust antimicrobial aggregation-induced emission luminogen (AIEgen) that shows the ability to alter the activities of multiple targets for the elimination of drug-resistant bacteria.

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.

1,2,3,4,6-O-Pentagalloylglucose Protects against Acute Lung Injury by Activating the AMPK/PI3K/Akt/Nrf2 Pathway.

An acute lung injury (ALI) is a serious lung disease with a high mortality rate, warranting the development of novel therapies. Previously, we reported that 1,2,3,4,6-O-pentagalloylglucose (PGG) could afford protection against ALI, however, the PGG-mediated protective effects remain elusive. Herein, PGG (60 and 30 mg/kg) markedly inhibited the lung wet/drug weight ratio and attenuated histological changes in the lungs (p < 0.05). A pretreatment with PGG (60 and 30 mg/kg) reduced the number of total leukocytes and the production of pro-inflammatory cytokines IL-6 and IL-1ß in bronchoalveolar lavage fluid (p < 0.05). In addition, PGG (60 and 30 mg/kg) also attenuated oxidative stress by reducing the formation of formation and the depletion of superoxide dismutase to treat an ALI (p < 0.05). To further explore the PGG-induced mechanism against an ALI, we screened the PGG pathway using immunohistochemical analysis, immunofluorescence assays, and Western blotting (WB). WB revealed that the expression levels of adenosine monophosphate-activated protein kinase phosphorylation (p-AMPK), phosphoinositide 3-kinase (PI3K), protein kinase B phosphorylation (P-Akt), and nuclear factor erythroid 2-related factor (Nrf2) were significantly higher in the PGG group (60 and 30 mg/kg) than in the lipopolysaccharide group (p < 0.05); these findings were confirmed by the immunohistochemical and immunofluorescence results. Accordingly, PGG could be effective against an ALI by inhibiting inflammation and oxidative stress via AMPK/PI3K/Akt/Nrf2 signaling, allowing for the potential development of this as a natural drug against an ALI.

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.

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.

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.

Co-Reactant-Mediated Low-Potential Anodic Electrochemiluminescence Platform and Its Immunosensing Application.

Screening high-performance anodic electrochemiluminescence (ECL) systems with low triggering potential is a promising way to broaden their applications. In addition to electrochemiluminophore, co-reactant also plays an important role in the ECL process, since the oxidation of co-reactants is one of the most important steps in the anodic ECL process. Herein, a novel co-reactant-mediated high-performance low-potential Au nanocluster (AuNC)-based ECL system has been successfully developed. Benefiting from the isopropyl substitution and hydroxyl addition to the triethylamine (TEA), the BSA-AuNC/2-(diisopropylamino)ethanol (DIPEA-OH) ECL system achieved higher energy efficiency at a lower potential of 0.75 V. In addition, compared with the BSA-AuNC/TEA system, the ECL intensity and quantum yield (ΦECL) with DIPEA-OH as a co-reactant increased 22.34-fold and 13-fold (as high as 68.17%), respectively. Based on the low potential, high ΦECL of the AuNC/DIPEA-OH ECL system, a sandwich-type immunosensor has been constructed for a highly selective SARS-CoV-2 N protein assay. In the absence of any complex signal amplification strategies, the ECL immunosensor for the SARS-CoV-2 N protein detection showed a linear range of 0.001-100 ng/mL and a detection limit of 0.35 pg/mL. Moreover, the ECL platform had good reproducibility and stability and exhibited acceptable detection performance in the detection of actual serum samples. This work established a framework for in-depth design and study of anode ECL co-reactants for AuNCs and other luminophores, and expanded the potential application of ECL sensors in the clinical diagnosis of COVID-19.

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.