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![Physical principles of Raman-based techniques. (a) Energy level diagrams representing the generation of emission signals. For spontaneous Raman scattering, the molecule is excited to a virtual energy state. It then returns to a lower energy level, accompanied by light scattering, which usually relies on Stokes Raman scattering. CRS, including SRS and CARS, is a nonlinear optical process based on the interaction between pump and Stokes lasers. (b) Mechanisms for SERS. SERS relies on electromagnetic and chemical enhancements, increasing Raman signal intensities by up to 10 8 -10 14 orders of magnitude. Ω p , pump beam; ω s , Stokes beam; ω as , anti-Stokes beam; Ω, Raman-active molecular vibration (adapted and reproduced from reference [11,13]).](publication/358911579/figure/fig1/AS:1140102340321282@1648832988417/Physical-principles-of-Raman-based-techniques-a-Energy-level-diagrams-representing-the.png)
Physical principles of Raman-based techniques. (a) Energy level diagrams representing the generation of emission signals. For spontaneous Raman scattering, the molecule is excited to a virtual energy state. It then returns to a lower energy level, accompanied by light scattering, which usually relies on Stokes Raman scattering. CRS, including SRS and CARS, is a nonlinear optical process based on the interaction between pump and Stokes lasers. (b) Mechanisms for SERS. SERS relies on electromagnetic and chemical enhancements, increasing Raman signal intensities by up to 10 8 -10 14 orders of magnitude. Ω p , pump beam; ω s , Stokes beam; ω as , anti-Stokes beam; Ω, Raman-active molecular vibration (adapted and reproduced from reference [11,13]).
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Antimicrobial resistance (AMR) is a global medical threat that seriously endangers human health. Rapid bacterial identification and antimicrobial susceptibility testing (AST) are key interventions to combat the spread and emergence of AMR. Although current clinical bacterial identification and AST provide comprehensive information, they are labor-i...
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... this review, we summarize and discuss the latest advances in Raman-based technology in rapid bacterial identification and AST. In particular, we highlight innovative research on three promising technologies, spontaneous Raman, SERS, and CRS, which include stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS) (Figure 1). Moreover, we will discuss their potential for transformation from the laboratory to the clinic. ...
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... SERS, previously mentioned as an emerging tool in mechanistic studies, finds diverse application in AST and the detection of bacteria [120,127,128]. This approach has significant advantages, driving significant attention to further development of SERS-AST: label-free, direct detection and live cell imaging, low sample volume, no complex pretreatment, costeffective techniques with minimal turnaround time. ...
... Ion-selective sensors [124] Colorimetric and photothermal assays [125,126] SERS [120,127,128] Miniaturization in microfluidic platforms [133][134][135][136]138,[140][141][142] Inactivating enzymes detection ...
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.
... Different bacterial responses during antibiotics exposure SERS has been successfully applied to analyze the different responses of antibiotic resistant and sensitive bacteria during exposure to antibiotics to assess the antibiotic susceptibility of bacteria. 80 It has been applied to differentiate the antibiotic-resistant and susceptible E. coli strains, [81][82][83] S. Typhimurium, 84 B. cereus, 82,85 S. aureus, 86 and carbapenem sensitive and resistant Klebsiella strains from clinical samples. 87 In addition to the instant one-time treatment of antibiotics, the antibiotic susceptibility testing was conducted over a long period such as three months, or 21 days in some studies, to investigate the dynamic molecular changes in the drug-resistant strains. ...
Rapid and sensitive bacteria detection and identification are becoming increasingly important for a wide range of areas including the control of food safety, the prevention of infectious diseases, and environmental monitoring. Raman spectroscopy is an emerging technology which provides comprehensive information for the analysis of bacteria in a short time and with high sensitivity. Raman spectroscopy offers many advantages including relatively simple operation, non‐destructive analysis, and information on molecular differences between bacteria species and strains. A variety of biochemical properties can be measured in a single spectrum. This short review covers the recent advancements and applications of Raman spectroscopy for bacteria analysis with specific focuses on bacteria detection, bacteria identification and discrimination, as well as bacteria antibiotic susceptibility testing in 2022. The development of novel substrates, the combination with other techniques, and the utilization of advanced data processing tools for the improvement of Raman spectroscopy and future directions are discussed.
... Methods such as surface-enhanced Raman scattering (SERS) are considered the principal biochemical fingerprinting methods, because they accurately reflect bacterial molecular profiles and changes brought about by antimicrobial treatment [128][129][130]. Several studies have used SERS to investigate bacteria's tolerance to antibiotics or the spread of antibiotic resistance, along with analyzing how drugs work by looking at the entire cell's spectroscopic signature [131,132]. By using SERS, it can detect pathogenic strains rapidly, with extreme sensitivity, and with a minimal amount of sample processing [128,133]. ...
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.
... Methods such as surface-enhanced Raman scattering (SERS) are considered the principal biochemical fingerprinting methods, because they accurately reflect bacterial molecular profiles and changes brought about by antimicrobial treatment [128][129][130]. Several studies have used SERS to investigate bacteria's tolerance to antibiotics or the spread of antibiotic resistance, along with analyzing how drugs work by looking at the entire cell's spectroscopic signature [131,132]. By using SERS, it can detect pathogenic strains rapidly, with extreme sensitivity, and with a minimal amount of sample processing [128,133]. ...
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.
... The spectroscopic fingerprint of bacterial cells reflects their physiological state and provides phenotypic specificity and pathogenicity screening potential. Raman-based identification and AST have played an increasingly important role in fighting AMR as comprehensively reviewed by Zhang et al., (2022a). To address this stringent concern, Raman spectroscopy is more frequently combined with machine learning, reaching up to 95.8% accuracy in pathogen identification. ...
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.
... There is hence a necessity to develop a more rapid and directly pathogen-oriented method to detect UTIs and pathogens. Several methods being considered at present are Raman spectroscopy, oligonucleotide-based molecular beacons, MALDI-TOF, plasmonics, electronic nose, flow cytometry, and nucleic acid-based diagnostics, which represent attractive molecular diagnostic modalities for a clinic [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The advantages of ATR-FTIR spectroscopy compared to other methods are the high signal-to-noise ratio, reduced dispersion, good spatial resolution, nondestructiveness, lack of sample preparation (or minimal preparation), low relative cost, and automated analysis [25,26]. ...
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.
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.
In this mini-review, we delve into the transformative impact of artificial intelligence (AI) and machine learning (ML) in the field of microbiology. The paper provides a brief overview of various domains where AI is reshaping practices, including clinical diagnostics, drug and vaccine discovery, and public health management. Our discussion spotlights the implementation of convolutional neural networks for enhanced pathogen identification, the advancements in point-of-care diagnostics, and the emergence of new antimicrobials to tackle resistant strains. The application of AI in epidemiology, microbial ecology and forensic microbiology is also outlined, underscoring its proficiency in deciphering complex microbial interactions and forecasting disease outbreaks. We critically examine the challenges in AI application, such as ensuring data quality and overcoming algorithmic constraints, and stress the necessity for interpretable AI models that align with medical and ethical standards. We address the intricacies of digitalization in microbiology diagnostics, emphasizing the need for efficient data management in laboratory and clinical environments. Looking forward, we identify key directions for AI in microbiology, particularly focusing on developing adaptable, self-updating AI models and their integration into clinical settings. We conclude by highlighting AI's potential to revolutionize microbiological diagnostics and infection control, significantly influencing patient care and public health. This review serves as an invitation to explore AI's integration into microbiology, showcasing its role in evolving current methodologies and propelling future innovations.
