A Review of Raman-Based Technologies for Bacterial Identification and Antimicrobial Susceptibility Testing

Synergizing Nanomaterials and Artificial Intelligence in Advanced Optical Biosensors for Precision Antimicrobial Resistance Diagnosis

Antimicrobial resistance (AMR) poses a critical global One Health concern, ensuing from unintentional and continuous exposure to antibiotics, as well as challenges in accurate contagion diagnostics. Addressing AMR requires a strategic approach that emphasizes early stage prevention through screening in clinical, environmental, farming, and livestock settings to identify nonvulnerable antimicrobial agents and the associated genes. Conventional AMR diagnostics, like antibiotic susceptibility testing, possess drawbacks, including high costs, time-consuming processes, and significant manpower requirements, underscoring the need for intelligent, prompt, and on-site diagnostic techniques. Nanoenabled artificial intelligence (AI)-supported smart optical biosensors present a potential solution by facilitating rapid point-of-care AMR detection with real-time, sensitive, and portable capabilities. This Review comprehensively explores various types of optical nanobiosensors, such as surface plasmon resonance sensors, whispering-gallery mode sensors, optical coherence tomography, interference reflection imaging sensors, surface-enhanced Raman spectroscopy, fluorescence spectroscopy, microring resonance sensors, and optical tweezer biosensors, for AMR diagnostics. By harnessing the unique advantages of these nanoenabled smart biosensors, a revolutionary paradigm shift in AMR diagnostics can be achieved, characterized by rapid results, high sensitivity, portability, and integration with Internet-of-Things (IoT) technologies. Moreover, nanoenabled optical biosensors enable personalized monitoring and on-site detection, significantly reducing turnaround time and eliminating the human resources needed for sample preservation and transportation. Their potential for holistic environmental surveillance further enhances monitoring capabilities in diverse settings, leading to improved modern-age healthcare practices and more effective management of antimicrobial treatments. Embracing these advanced diagnostic tools promises to bolster global healthcare capacity to combat AMR and safeguard One Health.

A novel derivative synchronous fluorescence method for the rapid, non-destructive and intuitive differentiation of denitrifying bacteria

It is challenging to differentiate bacteria residing in the same habitat by direct observation. This difficulty impedes the harvest, application and manipulation of functional bacteria in environmental engineering. In this study, we developed a novel method for rapid differentiation of living denitrifying bacteria based on derivative synchronous fluorescence spectroscopy, as exemplified by three heterotrophic nitrification-aerobic denitrification bacteria having the maximum nitrogen removal efficiencies greater than 90%. The intact bacteria and their living surroundings can be analyzed as an integrated target, which eliminates the need for the complex pre-processing of samples. Under the optimal synchronous scanning parameter (Δλ = 40 nm), each bacterium possesses a unique fluorescence spectral structure and the derivative synchronous fluorescence technique can significantly improve the spectral resolution compared to other conventional fluorescence methods, which enables the rapid differentiation of different bacteria through derivative synchronous fluorescence spectra as fast as 2 min per spectrum. Additionally, the derivative synchronous fluorescence technique can extract the spectral signals contributed by bacterial extracellular substances produced in the biological nitrogen removal process. Moreover, the results obtained from our method can reflect the real-time denitrification properties of bacteria in the biological nitrogen removal process of wastewater. All these merits highlight derivative synchronous fluorescence spectroscopy as a promising analytic method in the environmental field.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

Sensing of Antibiotic-Bacteria Interactions

Sensing of antibiotic-bacteria interactions is an important area of research that has gained significant attention in recent years. Antibiotic resistance is a major public health concern, and it is essential to develop new strategies for detecting and monitoring bacterial responses to antibiotics in order to maintain effective antibiotic development and antibacterial treatment. This review summarizes recent advances in sensing strategies for antibiotic-bacteria interactions, which are divided into two main parts: studies on the mechanism of action for sensitive bacteria and interrogation of the defense mechanisms for resistant ones. In conclusion, this review provides an overview of the present research landscape concerning antibiotic-bacteria interactions, emphasizing the potential for method adaptation and the integration of machine learning techniques in data analysis, which could potentially lead to a transformative impact on mechanistic studies within the field.

Deuterated driven new chemical entities: An optimistic way to improve therapeutic efficacy

In organic chemistry, the use of deuterium exchange as a tool to study the mechanism of chemical reaction has been well explored. Since two decades, the research focus on deuterated bioactive molecules has been gaining attention for investigating the therapeutic potential of deuterium replacement in a chemical structure. Recently, Food Drug Administration (FDA) approved the first deuterium-labeled drug "deutetrabenazine", and notified the deuterated drugs as new chemical entities (NCEs). Henceforth, the deuterium substitution driven structure activity relationship, preclinical pharmacokinetics, and toxicity studies were much initiated. Deuteration of a bioactive molecule often results in improved therapeutic efficacy due to the altered pharmacokinetic profile. This review provides a conceptual framework on the importance of deuterium atom in chemical structure of a drug, and its biological value in improved physiochemical properties, pharmacokinetics, biological target interaction, diagnosis, and toxicity. In addition, this review concisely updated the recent deuteration methods, chemical stability, challenges in drug development, deuterium-based imaging in diagnosis, and selected synthetic scheme of deuterated molecules.

SERS-based antibiotic susceptibility testing: Towards point-of-care clinical diagnosis

Emerging antibiotic resistant bacteria constitute one of the biggest threats to public health. Surface-enhanced Raman scattering (SERS) is highly promising for detecting such bacteria and for antibiotic susceptibility testing (AST). SERS is fast, non-destructive (can probe living cells) and it is technologically flexible (readily integrated with robotics and machine learning algorithms). However, in order to integrate into efficient point-of-care (PoC) devices and to effectively replace the current culture-based methods, it needs to overcome the challenges of reliability, cost and complexity. Recently, significant progress has been made with the emergence of both new questions and new promising directions of research and technological development. This article brings together insights from several representative SERS-based AST studies and approaches oriented towards clinical PoC biosensing. It aims to serve as a reference source that can guide progress towards PoC routines for identifying antibiotic resistant pathogens. In turn, such identification would help to trace the origin of sporadic infections, in order to prevent outbreaks and to design effective medical treatment and preventive procedures.

The Development of Technology to Prevent, Diagnose, and Manage Antimicrobial Resistance in Healthcare-Associated Infections

There is a growing risk of antimicrobial resistance (AMR) having an adverse effect on the healthcare system, which results in higher healthcare costs, failed treatments and a higher death rate. A quick diagnostic test that can spot infections resistant to antibiotics is essential for antimicrobial stewardship so physicians and other healthcare professionals can begin treatment as soon as possible. Since the development of antibiotics in the last two decades, traditional, standard antimicrobial treatments have failed to treat healthcare-associated infections (HAIs). These results have led to the development of a variety of cutting-edge alternative methods to combat multidrug-resistant pathogens in healthcare settings. Here, we provide an overview of AMR as well as the technologies being developed to prevent, diagnose, and control healthcare-associated infections (HAIs). As a result of better cleaning and hygiene practices, resistance to bacteria can be reduced, and new, quick, and accurate instruments for diagnosing HAIs must be developed. In addition, we need to explore new therapeutic approaches to combat diseases caused by resistant bacteria. In conclusion, current infection control technologies will be crucial to managing multidrug-resistant infections effectively. As a result of vaccination, antibiotic usage will decrease and new resistance mechanisms will not develop.

Using an ATR-FTIR Technique to Detect Pathogens in Patients with Urinary Tract Infections: A Pilot Study

Urinary tract infections (UTIs) are a leading hospital-acquired infection. Although timely detection of causative pathogens of UTIs is important, rapid and accurate measures assisting UTI diagnosis and bacterial determination are poorly developed. By reading infrared spectra of urine samples, Fourier-transform infrared spectroscopy (FTIR) may help detect urine compounds, but its role in UTI diagnosis remains uncertain. In this pilot study, we proposed a characterization method in attenuated total reflection (ATR)-FTIR spectra to evaluate urine samples and assessed the correlation between ATR-FTIR patterns, UTI diagnosis, and causative pathogens. We enrolled patients with a catheter-associated UTI in a subacute-care unit and non-UTI controls (total n = 18), and used urine culture to confirm the causative pathogens of the UTIs. In the ATR-FTIR analysis, the spectral variation between the UTI group and non-UTI, as well as that between various pathogens, was found in a range of 1800-900 cm^-1, referring to the presence of specific constituents of the bacterial cell wall. The results indicated that the relative ratios between different area zones of vibration, as well as multivariate analysis, can be used as a clue to discriminate between UTI and non-UTI, as well as different causative pathogens of UTIs. This warrants a further large-scale study to validate the findings of this pilot research.