Fluorescent sensor array based on aggregation-induced emission luminogens for pathogen discrimination

Recent advances in microfluidic-based spectroscopic approaches for pathogen detection

Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

An AIE-active bacterial inhibitor and photosensitizer for selective imaging, killing, and photodynamic inactivation of bacteria over mammalian cells

Photodynamic therapy is becoming increasingly popular for combat of bacteria. In the clinical photodynamic combat of bacteria, one critical issue is to avoid the potential damage to the host since the reactive oxygen species produced by photosensitizers are also harmful to mammalian cells. In this work, we report an aggregation-induced-emission-active bacterial inhibitor and photosensitizer, OEO-TPE-MEM (OTM), for the imaging, killing, and light-enhanced inactivation of bacteria. OTM could efficiently bind to and kill Gram-positive bacteria, while its affinity to Gram-negative bacteria is lower, and a higher OTM concentration is required for killing Gram-negative bacteria. OTM is also an efficient photosensitizer and could efficiently sensitize the production of reactive oxygen species, which enhances its killing effect on both Gram-positive and Gram-negative bacteria. More interestingly, OTM is very biocompatible with normal mammalian cells both in the dark and under light irradiation. OTM in mice models with bacteria-infected wounds could promote the healing of infected wounds without affecting their organs and blood parameters, which makes it an excellent candidate for clinical applications.

Recent Advances in Colorimetric Sensors Based on Gold Nanoparticles for Pathogen Detection

Infectious pathogens cause severe threats to public health due to their frightening infectivity and lethal capacity. Rapid and accurate detection of pathogens is of great significance for preventing their infection. Gold nanoparticles have drawn considerable attention in colorimetric biosensing during the past decades due to their unique physicochemical properties. Colorimetric diagnosis platforms based on functionalized AuNPs are emerging as a promising pathogen-analysis technique with the merits of high sensitivity, low-cost, and easy operation. This review summarizes the recent development in this field. We first introduce the significance of detecting pathogens and the characteristics of gold nanoparticles. Four types of colorimetric strategies, including the application of indirect target-mediated aggregation, chromogenic substrate-mediated catalytic activity, point-of-care testing (POCT) devices, and machine learning-assisted colorimetric sensor arrays, are systematically introduced. In particular, three biomolecule-functionalized AuNP-based colorimetric sensors are described in detail. Finally, we conclude by presenting our subjective views on the present challenges and some appropriate suggestions for future research directions of colorimetric sensors.

Combining tetraphenylethene (TPE) derivative cations with Eu3+-β-diketone complex anions for tunable luminescence

Tetraphenylethene (TPE) derivative cations (TPE⁺) and Eu³⁺-β-diketone complex anions (Eu(ABM)₄^-) were combined to construct a novel dual energy transfer system (TPE⁺ to Eu³⁺ and ABM to Eu³⁺). Our system exhibits tunable luminescence in DMF/water mixtures under different f_w conditions owing to the AIE and ACQ properties of TPE⁺ and ABM, respectively. Its luminescence can be also regulated by adding P-containing oxysalts or polyacrylic acids.

Identification of foodborne pathogenic bacteria using confocal Raman microspectroscopy and chemometrics

Rapid and accurate identification of foodborne pathogenic bacteria is of great importance because they are often responsible for the majority of serious foodborne illnesses. The confocal Raman microspectroscopy (CRM) is a fast and easy-to-use method known for its effectiveness in detecting and identifying microorganisms. This study demonstrates that CRM combined with chemometrics can serve as a rapid, reliable, and efficient method for the detection and identification of foodborne pathogenic bacteria without any laborious pre-treatments. Six important foodborne pathogenic bacteria including S. flexneri, L. monocytogenes, V. cholerae, S. aureus, S. typhimurium, and C. botulinum were investigated with CRM. These pathogenic bacteria can be differentiated based on several characteristic peaks and peak intensity ratio. Principal component analysis (PCA) was used for investigating the difference of various samples and reducing the dimensionality of the dataset. Performances of some classical classifiers were compared for bacterial detection and identification including decision tree (DT), artificial neural network (ANN), and Fisher's discriminant analysis (FDA). Correct recognition ratio (CRR), area under the receiver operating characteristic curve (ROC), cumulative gains, and lift charts were used to evaluate the performance of models. The impact of different pretreatment methods on the models was explored, and pretreatment methods include Savitzky-Golay algorithm smoothing (SG), standard normal variate (SNV), multivariate scatter correction (MSC), and Savitzky-Golay algorithm 1st Derivative (SG 1st Der). In the DT, ANN, and FDA model, FDA is more robust for overfitting problem and offers the highest accuracy. Most pretreatment methods raised the performance of the models except SNV. The results revealed that CRM coupled with chemometrics offers a powerful tool for the discrimination of foodborne pathogenic bacteria.

Rapid Fluorescence Sensor Guided Detection of Urinary Tract Bacterial Infections

Introduction

Urinary tract infections (UTI) are one of the most serious human bacterial infections affecting millions of people every year. Therefore, simple and reliable identification of the urinary tract pathogenic bacteria within a few minutes would be of great significance for diagnosis and treatment of clinical patients with UTIs. In this study, the fluorescence sensor was reported to guide the detection of urinary tract bacterial infections rapidly.

Methods

The Ami-AuNPs-DNAs sensor was fabricated by the amino-modified Au nanoparticles (Ami-AuNPs) and six DNAs signal molecules, which bound to the urinary tract pathogenic bacteria and generated corresponding response signals. Further, based on the collected response signals, identification was performed by principal component analysis (PCA) and linear discriminant analysis (LDA). The Ami-AuNPs and Ami-AuNPs-DNAs were characterized by transmission electron microscopy, UV−vis absorption spectrum, Fourier transform infrared spectrum, dynamic light scattering and zeta potentials. Thereafter, the Ami-AuNPs-DNAs sensor was used to discriminate and identify five kinds of urinary tract pathogenic bacteria. Moreover, the quantitative analysis performance towards individual bacteria at different concentrations were also evaluated.

Results

The Ami-AuNPs-DNAs sensor were synthesized successfully in terms of spherical, well-dispersed and uniform in size, which could well discriminate five main urinary tract pathogenic bacteria with unique fingerprint-like patterns and was sufficiently sensitive to determine individual bacteria with a detection limit to 1×10⁷ cfu/mL. Furthermore, the sensor had also been successfully applied to identify bacteria in urine samples collected from clinical UTIs.

Conclusion

The developed fluorescence sensor could be applied to rapid and accurate discrimination of urinary tract pathogenic bacteria and holds great promise for the diagnosis of the disease caused by bacterial infection.