6-Aza-2-Thio-Thymine Stabilized Gold Nanoclusters as Photoluminescent Probe for Protein Detection

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.

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.

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.

Highly Selective Detection of Paraoxon in Food Based on the Platform of Cu Nanocluster/MnO2 Nanosheets

Selective and sensitive identification of paraoxon residue in agricultural products is greatly significant for food safety but remains a challenging task. Herein, a detection platform was developed by integrating Cu nanoclusters (Cu NCs) with MnO₂ nanosheets, where the fluorescence of Cu NCs was effectively quenched. Upon introducing butyrylcholinesterase and butyrylcholine into the system, their hydrolysate, thiocholine, leads to the decomposition of the platform through a reaction between the MnO₂ nanosheets and thiol groups on thiocholine. The electron-rich groups on thiocholine can further promote the fluorescence intensity of Cu NCs through host–guest interactions. Adding paraoxon results in the failure of fluorescence recovery and further promotion, which could be utilized for the quantitative detection of paraoxon, and a limit of detection as low as 0.22 ng/mL can be achieved. The detection platform shows strong tolerance to common interference species, which endows its applications for the detection of paraoxon in vegetables and fruit. These presented results not only open a new door for the functionalization of metal nanoclusters but also offer an inspiring strategy for analytic techniques in nanomedicine and environmental science.

Evaluation of noble metal nanostructure-serum albumin interactions in 2D and 3D systems: Thermodynamics and possible mechanisms

In this review, we clearly highlight the importance of the detailed study of the interactions between noble metal colloids (nanoparticles (NPs) and nanoclusters (NCs)) with serum albumins (SAs) due to their rapidly growing presence in biomedical research. Besides the changes in the structure and optical property of SA, we demonstrate that the characteristic localized surface plasmon resonance (LSPR) feature of the colloidal noble metal NPs and the size- and structure-dependent photoluminescence (PL) property of the sub-nanometer sized NCs are also altered differently because of the interactions between them. Namely, for plasmonic NPs - SA interactions the PL quenching of SA (mainly static) is identified, while the SA cause PL enhancement of the ultra-small NCs after complexation. This review summarizes that the thermodynamic nature and the possible mechanisms of the binding processes are dependent partly on the size, morphology, and type of the noble metals, while the chemical structure as well as the charge of the stabilizing ligands have the most dominant effect on the change in optical features. In addition to the thermodynamic data and proposed binding mechanisms provided by three-dimensional spectroscopic techniques, the quantitative and real-time data of "quasi" two-dimensional sensor apparatus should also be considered to provide a comprehensive evaluation on many aspects of the particle/cluster - SA interactions.

Supramolecular Gold Chemistry: From Atomically Precise Thiolate-Protected Gold Nanoclusters to Gold-Thiolate Nanostructures

Supramolecular chemistry is defined as chemistry beyond the molecule [...].