Simpler Procedure and Improved Performance for Pathogenic Bacteria Analysis with a Paper-Based Ratiometric Fluorescent Sensor Array

Machine learning-powered wearable interface for distinguishable and predictable sweat sensing

The constrained resources on wearable devices pose a challenge in meeting the demands for comprehensive sensing information, and current wearable non-enzymatic sensors face difficulties in achieving specific detection in biofluids. To address this issue, we have developed a highly selective non-enzymatic sweat sensor that seamlessly integrates with machine learning, ensuring reliable sensing and physiological monitoring of sweat biomarkers during exercise. The sensor consists of two electrodes supported by a microsystem that incorporates signal processing and wireless communication. The device generates four explainable features that can be used to accurately predict tyrosine and tryptophan concentrations, as well as sweat pH. The reliability of this device has been validated through rigorous statistical analysis, and its performance has been tested in subjects with and without supplemental amino acid intake during cycling trials. Notably, a robust linear relationship has been identified between tryptophan and tyrosine concentrations in the collected samples, irrespective of the pH dimension. This innovative sensing platform is highly portable and has significant potential to advance the biomedical applications of non-enzymatic sensors. It can markedly improve accuracy while decreasing costs.

Fast and accurate identification of pathogenic bacteria using excitation-emission spectroscopy and machine learning

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation-emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4'-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye-bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation-emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.

Artificial intelligence applications in the diagnosis and treatment of bacterial infections

The diagnosis and treatment of bacterial infections in the medical and public health field in the 21st century remain significantly challenging. Artificial Intelligence (AI) has emerged as a powerful new tool in diagnosing and treating bacterial infections. AI is rapidly revolutionizing epidemiological studies of infectious diseases, providing effective early warning, prevention, and control of outbreaks. Machine learning models provide a highly flexible way to simulate and predict the complex mechanisms of pathogen-host interactions, which is crucial for a comprehensive understanding of the nature of diseases. Machine learning-based pathogen identification technology and antimicrobial drug susceptibility testing break through the limitations of traditional methods, significantly shorten the time from sample collection to the determination of result, and greatly improve the speed and accuracy of laboratory testing. In addition, AI technology application in treating bacterial infections, particularly in the research and development of drugs and vaccines, and the application of innovative therapies such as bacteriophage, provides new strategies for improving therapy and curbing bacterial resistance. Although AI has a broad application prospect in diagnosing and treating bacterial infections, significant challenges remain in data quality and quantity, model interpretability, clinical integration, and patient privacy protection. To overcome these challenges and, realize widespread application in clinical practice, interdisciplinary cooperation, technology innovation, and policy support are essential components of the joint efforts required. In summary, with continuous advancements and in-depth application of AI technology, AI will enable doctors to more effectivelyaddress the challenge of bacterial infection, promoting the development of medical practice toward precision, efficiency, and personalization; optimizing the best nursing and treatment plans for patients; and providing strong support for public health safety.

Elevating nanomaterial optical sensor arrays through the integration of advanced machine learning techniques for enhancing visual inspection of food quality and safety

Food quality and safety problems caused by inefficient control in the food chain have significant implications for human health, social stability, and economic progress and optical sensor arrays (OSAs) can effectively address these challenges. This review aims to summarize the recent applications of nanomaterials-based OSA for food quality and safety visual monitoring, including colourimetric sensor array (CSA) and fluorescent sensor array (FSA). First, the fundamental properties of various advanced nanomaterials, mainly including metal nanoparticles (MNPs) and nanoclusters (MNCs), quantum dots (QDs), upconversion nanoparticles (UCNPs), and others, were described. Besides, the diverse machine learning (ML) and deep learning (DL) methods of high-dimensional data obtained from the responses between different sensing elements and analytes were presented. Moreover, the recent and representative applications in pesticide residues, heavy metal ions, bacterial contamination, antioxidants, flavor matters, and food freshness detection were comprehensively summarized. Finally, the challenges and future perspectives for nanomaterials-based OSAs are discussed. It is believed that with the advancements in artificial intelligence (AI) techniques and integrated technology, nanomaterials-based OSAs are expected to be an intelligent, effective, and rapid tool for food quality assessment and safety control.

Illuminating Bacterial Contamination in Water Sources: The Power of Fluorescence-Based Methods

Bacterial contamination of water sources is a significant public health concern, and therefore, it is important to have accurate and efficient methods for monitoring bacterial concentration in water samples. Fluorescence-based methods, such as SYTO 9 and PI staining, have emerged as a promising approach for real-time bacterial quantification. In this review, we discuss the advantages of fluorescence-based methods over other bacterial quantification methods, including the plate count method and the most probable number (MPN) method. We also examine the utility of fluorescence arrays and linear regression models in improving the accuracy and reliability of fluorescence-based methods. Overall, fluorescence-based methods offer a faster, more sensitive, and more specific option for real-time bacterial quantification in water samples.

A review on machine learning-powered fluorescent and colorimetric sensor arrays for bacteria identification

Biosensors have been widely used for bacteria determination with great success. However, the "lock-and-key" methodology used by biosensors to identify bacteria has a significant limitation: it can only detect one species of bacteria. In recent years, optical (fluorescent and colorimetric) sensor arrays are gradually gaining attention from researchers as a new type of biosensor. They can acquire multiple features of a target simultaneously, form a feature pattern, and determine the bacteria species with the help of pattern recognition/machine learning algorithms. Previous reviews in this area have focused on the interaction between the sensor array and bacteria or the materials used to make the sensors. This review, on the other hand, will provide researchers with a better understanding of the field by discussing fluorescent and colorimetric sensor arrays based on the mechanism of optical signal generation. These sensor arrays will be compared based on the identified species. Finally, we will discuss the limitations of these sensor arrays and explore possible solutions.

Portable biosensor-based oral pathogenic bacteria detection for community and family applications

Detection of oral pathogens is essential in the management of oral diseases, as their occurrence and progression are closely linked to an imbalance in these microorganisms. Detection techniques such as microbial cultures, enzyme-linked immunosorbent assays and polymerase chain reactions are highly dependent on complex testing procedures and specialized laboratory equipment, making prevention and early diagnosis of oral diseases difficult. To comprehensively implement oral disease prevention and early diagnosis in social groups, there is an urgent need for portable testing methods for oral pathogenic bacteria that can be applied in community and home settings. In this review, several common portable biosensors for pathogenic bacteria are first described. Based on the goal of achieving primary prevention and diagnosis of oral diseases, we elaborate and summarize portable biosensors for common oral pathogenic bacteria in terms of how to achieve portability of the technique. This review aims to reflect the current status of portable biosensors for common oral pathogens and to lay the foundation for the further realization of portable detection of oral pathogens.

2D polyaniline derivatives as turn-on fluorescent probe for efficient triethylamine detection at room temperature

Due to the severe toxicity of triethylamine (TEA), the preparation of chemsensors with high sensitivity, low cost and visualization for TEA detection has been a research hotspot. However, based on the fluorescence turn-on detection of TEA remains rare. In this work, three two-dimensional conjugated polymers (2D CPs) were prepared by chemical oxidation polymerization. These sensors show a quick response and excellent selectivity toward TEA at room temperature. The minimum limit of detection (LOD) for TEA was 3.6 nM in the range of 10 μM ∼ 30 μM. Interestingly, the paper sensor based on P2-HCl can quantitatively detect TEA gas within 20 s, which showed great application potential in fields of environmental monitoring. Besides, Fourier transform infrared spectra (FT-IR), scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) data were used to thoroughly interpret the sensing mechanism. This work provided an effective method for the development of 2D fluorescent chemosensors for TEA detection.

A novel single-particle multiple-signal sensor array combined with multidimensional data mining for the detection of tricarboxylic acid cycle metabolites and discrimination of cells

Tricarboxylic acid (TCA) metabolites in cancer cells show a marked difference from those in normal cells. Herein, we report a single-particle multiple-signal lanthanide/europium-based metal-organic framework (Tb/Eu MOF) sensor array for the detection of TCA metabolites and discrimination of cancer cells. In the presence of TCA metabolite, 6 characteristic peaks of Tb/Eu MOF showed dramatic changes due to host-guest interactions, allowing sensor array-based qualitative and quantitative detection to be performed. In the qualitative detection ability test, 18 TCA metabolites at 4 concentrations (50 μM, 100 μM, 200 μM, 300 μM) were accurately discriminated by the sensor array via linear discriminant analysis (LDA). Significantly, these 4 concentrations include the clinical detection criteria for most TCA metabolites. In the quantitative detection ability test, a good linear relationship between Euclidean distances and the concentrations of L-valine (Val) could be obtained in the range of 50 to 500 μM (R² = 0.9755). On this basis, the provided method was successfully applied for the classification of 2 normal cells and 5 cancer cells via principal components analysis (PCA), LDA and a radial basis function neural network (RBFN). What's more, by verifying the weight coefficient of each point, detection and discrimination results are proved as a trustworthy balanced evaluation of multiple factors. Depending on precise data processing, the experimental operation was simplified on the premise of ensuring accuracy, so our method is a meaningful exploration for array design.

A Paper-Based Fluorescent Sensor for Rapid Early Screening of Oral Squamous Cell Carcinoma

Various types of sensors play an irreplaceable role in the detection of biomarkers, but their high cost and complicated operation make it difficult to benefit ordinary people. Herein, we develop a low-cost, double-layered, paper-based fluorescent sensor (CP/HQ) structurally consisting of the upper reaction layer loaded with two oxidases (lactate oxidase and choline oxidase) and the bottom fluorescent layer that physically associates with the porphine-grafted composite fluorescent polymer colloids (PF-PDMTP/HQ). Based on the dramatic and rapid fluorescence decrease of porphine induced by the oxidation between saliva and oxidases and subsequent fluorescence resonance energy transfer from oxidized hydroquinone, the resultant fluorescent paper sensor enables us to achieve visual detection of OSCC, which was further recognized by smartphone scanning as the grayscale variation. It was found that the linear sensing range of grayscale value are 10-200 μM for lactic acid and 10-100 μM for choline, with LODs of 5.7 and 8.9 μM, respectively. More importantly, the sensor can achieve a powerful detection capability comparable to that of high-performance liquid chromatography (HPLC) in clinical settings with simple operation, demonstrating its great application potential. Our proposed sensor not only improves the accuracy of OSCC diagnosis but also provides a valuable attempt for the device modification of polymer-sensing systems and the development of non-invasive and easy-to-operate disease screening methods.

A mitochondria-targeted fluorescent probe based on biocompatible RBH-U for the enhanced response of Fe3+ in living cells and quenching of Cu2+ in vitro

A rhodamine hydrazide conjugating uridine moiety (RBH-U) is firstly synthesized by screening different synthetic routes, and then developed as a fluorescence probe for selective detection of Fe³⁺ ions in an aqueous solution, accompanied by visual color change with naked eyes. Upon the addition of Fe³⁺ in a 1:1 stoichiometry, a 9-fold enhancement in the fluorescence intensity of the RBH-U was observed with an emission wavelength of 580 nm. In the presence of other metal ions, the "turn-on" fluorescent probe with pH-independent (value 5.0 to 8.0) is remarkably specific for Fe³⁺ with a detection limit as low as 0.34 μM. Further, the enhanced fluorescence intensity of RBH-U- Fe³⁺ can be quenched as a switch-off sensor to assist in the recognition of Cu²⁺ ions. Additionally, the colocalization assay demonstrated that RBH-U containing uridine residue can be used as a novel mitochondria-targeted fluorescent probe with rapid reaction time. Cytotoxicity and cell imaging of RBH-U probe in live NIH-3T3 cells suggest that it can be a potential candidate for clinical diagnosis and Fe³⁺ tracking toll for the biological system due to its biocompatibility and nontoxicity in NIH-3T3 cells even up to 100 μM.

Cellulose-Based Laser-Induced Graphene Devices for Electrochemical Monitoring of Bacterial Phenazine Production and Viability

As an easily disposable substrate with a microporous texture, paper is a well-suited, generic substrate to build analytical devices for studying bacteria. Using a multi-pass lasing process, cellulose-based laser-induced graphene (cLIG) with a sheet resistance of 43.7 ± 2.3 Ωsq-1 is developed and utilized in the fabrication of low-cost and environmentally-friendly paper sensor arrays. Two case studies with Pseudomonas aeruginosa and Escherichia coli demonstrate the practicality of the cLIG sensors for the electrochemical analysis of bacteria. The first study measures the time-dependent profile of phenazines released from both planktonic (up to 60 h) and on-chip-grown (up to 22 h) Pseudomonas aeruginosa cultures. While similarities do exist, marked differences in phenazine production are seen with cells grown directly on cLIG compared to the planktonic culture. Moreover, in planktonic cultures, pyocyanin levels increase early on and plateau around 20 h, while optical density measurements increase monotonically over the duration of testing. The second study monitors the viability and metabolic activity of Escherichia coli using a resazurin-based electrochemical assay. These results demonstrate the utility of cLIG paper sensors as an inexpensive and versatile platform for monitoring bacteria and could enable new opportunities in high-throughput antibiotic susceptibility testing, ecological studies, and biofilm studies.

Recent advances of fluorescent sensors for bacteria detection-A review

Bacterial infections have become a global public health problem. Rapid and sensitive bacterial detection is of great importance for human health. Among various sensor systems, fluorescence sensor is rapid, portable, multiplexed, and cost-efficient. Herein, we reviewed the current trends of fluorescent sensors for bacterial detection from three aspects (response materials, target and recognition way). The fluorescent materials have the advantages of high fluorescent strength, high stability, and good biocompatibility. They provide a new path for bacterial detection. Several recent fluorescent nanomaterials for bacterial detection, including semiconductor quantum dots (QDs), carbon dots (CDs), up-conversion nanoparticles (UCNPs) and metal organic frameworks (MOFs), were introduced. Their optical properties and detection mechanisms were analyzed and compared. For different response targets in the detection process, we studied the fluorescence strategy using DNA, bacteria, and metabolites as the response target. In addition, we classified the recognition way between nanomaterial and target, including specific recognition methods based on aptamers, antibodies, bacteriophages, and non-specific recognition methods based on biological functional materials. The characteristics of different recognition methods were summarized. Finally, the weaknesses and future development of bacterial fluorescence sensor were discussed. This review provides new insights into the application of fluorescent sensing systems as an important tool for bacterial detection.

Rapid and sensitive naked eye detection of faecal pigments using their enhanced solid-state green fluorescence on a zinc acetate substrate

The identification of trace faecal pigments in real-time and on-site detection remains a challenge for water quality monitoring. Herein, a simple, low-cost and rapid fluorescence-based analytical method has been developed in a solid matrix for faecal pigments like stercobilin and urobilin detection. This was made possible due to significant enhancement of green solid-state fluorescence (520 nm) by zinc(II) complexation with faecal pigments embedded in the surface of zinc acetate crystals. It enables naked-eye detection of these pigments even at a 10 μM level when excited with 365 nm blue-UV. It was demonstrated that easily available white cellulose paper strips or TLC silica plates coated with zinc acetate can be used as substrates. A photophysical study of solid-state faecal pigments-zinc(II) complexes suggests that green fluorescence enhancement results from the complexation, which can be attributed to the substantial decrease of the non-radiative decay rate (k_nr) as well as more efficient use of excitation light. The observation of reduced interference of humic acid fluorescence makes faecal pigment detection more efficient by this proposed method.

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.

Back to the Drawing Board: A Unifying First-Principle Model for Correlating Sample UV-Vis Absorption and Fluorescence Emission

The popular textbook and literature model I(λ_x,λ_m) = K(λx,λm)(1-10-Ax) or its variants for correlating the sample absorption and fluorescence often fails even for the simplest samples where the fluorophore is the only light absorber. Reported is a first-principle model I(λ_x,λ_m) = K(λx,λm)Ax,f10-(Ax,sdx+Am,sdm) for correlating the sample fluorescence measured with a conventional spectrofluorometer and its UV-vis absorbance quantified with a conventional UV-vis spectrophotometer. This model can be simplified or expanded for a variety of fluorescence analyses. First, it enables curve-fitting fluorescence intensity as a function of the fluorophore or sample absorbance over a sample concentration range impossible with existing models. Second, it provides the theoretical foundation for an inner-filter-effect (IFE)-correction method developed earlier and explains mathematically the linearity between the IFE-corrected fluorescence and the fluorophore concentration or absorbance. Third, this model can be expanded for quantitative mechanistic studies of fluorescence intensity variations triggered by stimuli treatments. One demonstrated example is to quantify temperature effects on the emission-wavelength-specific and total fluorescence quantum yield of anthracene. We expect that this first-principle model will be broadly adopted for both student education that promotes evidence-based learning and a variety of fluorescence applications where disentangling sample absorption and emission are critical for reliable data analysis.