Rapid antibiotic susceptibility testing of bacteria from patients' blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy

SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification

Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when photons interact with substances, providing detailed molecular structure information. It exhibits various advantages including high sensitivity, specificity, and multiple-detection capabilities, which make it particularly effective in bacterial detection and antibiotic resistance research. In this review, we review the recent development of SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification. Although the promising applications have been realized in clinical microbiology and diagnostics, several challenges still limit the further development, including signal variability, the complexity of spectral data interpretation, and the lack of standardized protocols. To overcome these obstacles, more reproducible and standardized methodologies, particularly in nanomaterial design and experimental condition optimization. Furthermore, the integration of SERS with machine learning and artificial intelligence can automate spectral analysis, improving the efficiency and accuracy of bacterial species identification, resistance marker detection, and metabolic monitoring. Combining SERS with other analytical techniques, such as mass spectrometry, fluorescence microscopy, or genomic sequencing, could provide a more comprehensive understanding of bacterial physiology and resistance mechanisms. As SERS technology advances, its applications are expected to extend beyond traditional microbiology to areas like environmental monitoring, food safety, and personalized medicine. In particular, the potential for SERS to be integrated into point-of-care diagnostic devices offers significant promise for enhancing diagnostics in resource-limited settings, providing cost-effective, rapid, and accessible solutions for bacterial infection and resistance detection.

Surface-Enhanced Raman Spectroscopy and Multivariate Analysis for Elucidating Mechanisms of Action in Antibacterial Agents

Antibiotic and antibacterial-resistant bacteria continue to pose a global-health threat. Understanding the mechanism of action (MoA) of antibacterial agents is crucial for developing precise and novel treatment methods. Traditionally, the MoA of a novel treatment is studied with genome sequencing and mass spectrometry, which are both labor-intensive and costly. In contrast, surface-enhanced Raman spectroscopy (SERS) provides a rapid, sensitive, and noninvasive alternative for analyzing bacterial molecular responses to antibacterial agents. In this study, we employed SERS to analyze the effects of various antibacterial agents on Escherichia coli. We treated E. coli cultures with agents that have different known MoAs, including oxidative stress, metabolic disruption, and membrane lysis. Through partial least-squares (PLS) analysis, we correlated changes in the SERS spectra with bacterial viability, achieving high predictive accuracy (R2 > 0.98). From the PLS models, we were able to extract variable importance projection scores, which were used to identify the MoA in subsets of the data. Our results revealed distinct spectral signatures associated with each MoA, demonstrating the potential of SERS to differentiate between different antibacterial treatments. This study highlights the feasibility of using SERS combined with multivariate analysis to rapidly characterize the molecular effects of antibacterial agents even with smaller data sets. By providing a real-time method for monitoring bacterial responses, this SERS approach could accelerate the discovery of novel antibacterial therapies while reducing dependency on more time-consuming and expensive analytical techniques.

Spectroscopic analysis of bacterial photoreactivation

With the rise of bacterial infections and antibiotic resistance, spectroscopic devices originally developed for bacterial detection have shown promise to rapidly identify bacterial strains and determine the ratio of live to dead bacteria. However, the detection of the photoreactivated pathogens remains a critical concern. This study utilizes fluorescence and Raman spectroscopy to analyze bacterial responses to UV irradiation and subsequent photoreactivation. Our experimental results reveal limitations in fluorescence spectroscopy for detecting photoreactivated bacteria, as the intense fluorescence of tryptophan and tyrosine amino acids masks the fluorescence emitted by thymine molecules. Conversely, Raman spectroscopy proves more effective, showing a detectable decrease in band intensities of E. coli bacteria at 1248 and 1665 cm-1 after exposure to UVC radiation. Subsequent UVA irradiation results in the partial restoration of these band intensities, indicating DNA repair and bacterial photoreactivation. This enhanced understanding aims to improve the accuracy and effectiveness of these spectroscopic tools in clinical and environmental settings.

Unraveling RNA contribution to the molecular origins of bacterial surface-enhanced Raman spectroscopy (SERS) signals

Surface-enhanced Raman spectroscopy (SERS) is widely utilized in bacterial analyses, with the dominant SERS peaks attributed to purine metabolites released during sample preparation. Although adenosine triphosphate (ATP) and nucleic acids are potential molecular origins of these metabolites, research on their exact contributions remains limited. This study explored purine metabolite release from E. coli and RNA integrity following various sample preparation methods. Standard water washing generated dominant SERS signals within 10 s, a duration shorter than the anticipated RNA half-lives under starvation. Evaluating RNA integrity indicated that the most abundant ribosomal RNA species remained intact for hours post-washing, whereas messenger RNA and transfer RNA species degraded gradually. This suggests that bacterial SERS signatures observed after the typical washing step could originate from only a small fraction of endogenous purine-containing molecules. In contrast, acid depurination led to degradation of most RNA species, releasing about 40 times more purine derivatives than water washing. Mild heating also instigated the RNA degradation and released more purine derivatives than water washing. Notably, differences were also evident in the dominant SERS signals following these treatments. This work provides insights into SERS-based studies of purine metabolites released by bacteria and future development of methodologies.

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Dynamic Changes in Soluble Triggering Receptor Expressed on Myeloid Cells-1 in Sepsis with Respect to Antibiotic Susceptibility

Purpose

Proper antibiotic administration is crucial for sepsis management. Given the escalating incidence of antimicrobial resistance, there is a pressing need for indicators of antimicrobial susceptibility with short turnaround times. This study aimed to investigate the potential of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) as an early biomarker for in vivo antibiotic susceptibility in patients with sepsis.

Patients and Methods

We conducted a retrospective analysis of plasma samples from patients enrolled in a pre-established study designed to investigate prognostic biomarkers in patients with sepsis or septic shock. Baseline and 6 h sTREM-1 levels were examined using enzyme-linked immunosorbent assays. The primary outcome of the study was the comparison of percentage changes in sTREM-1 levels at the 6 h relative to baseline with respect to antibiotic susceptibility.

Results

Of the 596 patients enrolled in the pre-established study, 29 with a median age of 75.8 and a 28-day mortality rate of 17.2% were included in the present analysis. Among these patients, 24 were classified into the susceptible group, whereas the remaining five were classified into the resistant group. The trend in plasma sTREM-1 levels differed with respect to antibiotic susceptibility. Moreover, percentage change in sTREM-1 levels at the 6 h relative to baseline was significantly higher in the resistant group (P = 0.028).

Conclusion

The trend in plasma sTREM-1 levels in patients with sepsis differed with respect to antibiotic susceptibility, with a higher percentage change in patients treated with inappropriate antibiotics. These findings indicate the potential utility of sTREM-1 as an early biomarker of antibiotic susceptibility.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Exploiting Purine as an Internal Standard for SERS Quantification of Purine Derivative Molecules Released by Bacteria

Surface-enhanced Raman scattering (SERS) is a highly sensitive technique used in diverse biomedical applications including rapid antibiotic susceptibility testing (AST). However, signal fluctuation in SERS, particularly the widespread of signals measured from different batches of SERS substrates, compromises its reliability and introduces potential errors in SERS-AST. In this study, we investigate the use of purine as an internal standard (IS) to recalibrate SERS signals and quantify the concentrations of two important purine derivatives, adenine and hypoxanthine, which are the most important biomarkers used in SERS-AST. Our findings demonstrate that purine IS effectively mitigates SERS signal fluctuations and enables accurate prediction of adenine and hypoxanthine concentrations across a wide range (5 orders of magnitude). Calibrations with purine as an IS outperform those without, exhibiting a 10-fold increase in predictive accuracy. Additionally, the calibration curve obtained from the first batch of SERS substrates remains effective for 64 additional substrates fabricated over a half-year period. Measurements of adenine and hypoxanthine concentrations in bacterial supernatants using SERS with purine IS closely align with the liquid chromatography-mass spectrometry results. The use of purine as an IS offers a simple and robust platform to enhance the speed and accuracy of SERS-AST, while also paving the way for in situ SERS quantification of purine derivatives released by bacteria under various stress conditions.

Metabolite-Mediated Bacterial Antibiotic Resistance Revealed by Surface-Enhanced Raman Spectroscopy

A prompt on-site, real-time method to detect bacterial antibiotic resistance is crucial for controlling the spread of resistance. Herein, we report the use of surface-enhanced Raman spectroscopy (SERS) for the monitoring of bioactive metabolites produced by ampicillin-resistant Pseudomonas aeruginosa strains and identification of mechanisms underlying antibiotic resistance. The results indicate that the blue-green pigment pyocyanin (PYO) dominates the metabolite signals and is significantly enhanced upon exposure to subminimal inhibitory concentrations of ampicillin. PYO accumulates during exponential growth and subsequently either diffuses into the culture medium or is consumed in response to nutrient deprivation. The SERS spectra further reveal that the production of some intermediate substances such as polysaccharides and amino acids is minimally impacted and that nutrient consumption remains consistent. Moreover, the intensity changes and peak shifts observed in the SERS spectra of non-PYO-producing ampicillin-susceptible Escherichia coli demonstrate that exogenously added PYO enhances E. coli tolerance to ampicillin to some extent. These results indicate that PYO mediates antibiotic resistance not only in the parent species but also in cocultured bacterial strains. The metabolic SERS signal provides new insight regarding antibiotic resistance with promising applications for both environmental monitoring and rapid clinical detection.

SERS-based rapid susceptibility testing of commonly administered antibiotics on clinically important bacteria species directly from blood culture of bacteremia patients

Bloodstream infections are a growing public health concern due to emerging pathogens and increasing antimicrobial resistance. Rapid antibiotic susceptibility testing (AST) is urgently needed for timely and optimized choice of antibiotics, but current methods require days to obtain results. Here, we present a general AST protocol based on surface-enhanced Raman scattering (SERS-AST) for bacteremia caused by eight clinically relevant Gram-positive and Gram-negative pathogens treated with seven commonly administered antibiotics. Our results show that the SERS-AST protocol achieves a high level of agreement (96% for Gram-positive and 97% for Gram-negative bacteria) with the widely deployed VITEK 2 diagnostic system. The protocol requires only five hours to complete per blood-culture sample, making it a rapid and effective alternative to conventional methods. Our findings provide a solid foundation for the SERS-AST protocol as a promising approach to optimize the choice of antibiotics for specific bacteremia patients. This novel protocol has the potential to improve patient outcomes and reduce the spread of antibiotic resistance.

Antimicrobial Resistance Studies Using Raman Spectroscopy on Clinically Relevant Bacterial Strains

There has been a steep rise in the emergence of antibiotic-resistant bacteria in the past few years. A timely diagnosis can help in initiating appropriate antibiotic therapy. However, conventional techniques for diagnosing antibiotic resistance are time-consuming and labor-intensive. Therefore, we investigated the potential of Raman spectroscopy as a rapid surveillance technology for tracking the emergence of antibiotic resistance. In this study, we used Raman spectroscopy to differentiate clinical isolates of antibiotic-resistant and -sensitive bacteria of Escherichia coli, Acinetobacter baumannii, and Enterobacter species. The spectra were collected with or without exposure to various antibiotics (ciprofloxacin, gentamicin, meropenem, and nitrofurantoin), each having a distinct mechanism of action. Ciprofloxacin- and meropenem-treated sensitive strains showed a decrease in the intensity of Raman bands associated with DNA (667, 724, 785, 1378, 1480, and 1575 cm^-1) and proteins (640 and 1662 cm^-1), coupled with an increase in the intensity of lipid bands (891, 960, and 1445 cm^-1). Gentamicin- and nitrofurantoin-treated sensitive strains showed an increase in the intensity of nucleic acid bands (668, 724, 780, 810, 1378, 1480, and 1575 cm^-1) while a decrease in the intensity of protein bands (640, 1003, 1606, and 1662 cm^-1) and the lipid band (1445 cm^-1). The Raman spectral changes observed in the antibiotic-resistant strains were opposite to that of antibiotic-sensitive strains. The Raman spectral data correlated well with the antimicrobial susceptibility test results. The Raman spectral dataset was used for partial least-squares (PLS) analysis to validate the biomarkers obtained from the univariate analysis. Overall, this study showcases the potential of Raman spectroscopy for detecting antibiotic-resistant and -sensitive bacteria.

Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification

Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.

Rapid determination of Proteus mirabilis susceptibility to antibiotics using infrared spectroscopy in tandem with random forest

Bacterial infections cause serious illnesses that are treated with antibiotics. Currently used methods for detecting bacterial antibiotic susceptibility consume 48-72 h, leading to overuse of antibiotics. Thus, many bacterial species have acquired resistance to a broad range of available antibiotics. There is an urgent need to develop efficient methods for rapid determination of bacterial susceptibility to antibiotics. The combination of machine learning and Fourier-transform infrared (FTIR) spectroscopy has generated a promising diagnostic approach in medicine and biology. Our main goal is to examine the potential of FTIR spectroscopy to determine the susceptibility of urinary tract infection-Proteus mirabilis to a specific range of antibiotics, within about 20 min after 24 h culture and identification. We measured the infrared spectra of 489 different P. mirabilis isolates and used random forest to analyze this spectral database. A classification success rate of ~84% was achieved in differentiating between the resistant and sensitive isolates based on their susceptibility to ceftazidime, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, ciprofloxacin, gentamicin, and sulfamethoxazole antibiotics in a time span of 24 h instead of 48 h.

Fast Track Diagnostic Tools for Clinical Management of Sepsis: Paradigm Shift from Conventional to Advanced Methods

Sepsis is one of the deadliest disorders in the new century due to specific limitations in early and differential diagnosis. Moreover, antimicrobial resistance (AMR) is becoming the dominant threat to human health globally. The only way to encounter the spread and emergence of AMR is through the active detection and identification of the pathogen along with the quantification of resistance. For better management of such disease, there is an essential requirement to approach many suitable diagnostic techniques for the proper administration of antibiotics and elimination of these infectious diseases. The current method employed for the diagnosis of sepsis relies on the conventional culture of blood suspected infection. However, this method is more time consuming and generates results that are false negative in the case of antibiotic pretreated samples as well as slow-growing microbes. In comparison to the conventional method, modern methods are capable of analyzing blood samples, obtaining accurate results from the suspicious patient of sepsis, and giving all the necessary information to identify the pathogens as well as AMR in a short period. The present review is intended to highlight the culture shift from conventional to modern and advanced technologies including their limitations for the proper and prompt diagnosing of bloodstream infections and AMR detection.

Antibiotic Susceptibility Testing with Raman Biosensing

Antibiotics guard us against bacterial infections and are among the most commonly used medicines. The immediate consequence of their large-scale production and prescription is the development of antibiotic resistance. Therefore, rapid detection of antibiotic susceptibility is required for efficient antimicrobial therapy. One of the promising methods for rapid antibiotic susceptibility testing is Raman spectroscopy. Raman spectroscopy combines fast and contactless acquisition of spectra with good selectivity towards bacterial cells. The antibiotic-induced changes in bacterial cell physiology are detected as distinct features in Raman spectra and can be associated with antibiotic susceptibility. Therefore, the Raman-based approach may be beneficial in designing therapy against multidrug-resistant infections. The surface-enhanced Raman spectroscopy (SERS) and resonance Raman spectroscopy (RRS) additionally provide excellent sensitivity. In this review, we present an analysis of the Raman spectroscopy-based optical biosensing approaches aimed at antibiotic susceptibility testing.

Cheminformatics Study on Structural and Bactericidal Activity of Latest Generation β-Lactams on Widespread Pathogens

Raman spectra of oxacillin (OXN), carbenicillin (CBC), and azlocillin (AZL) are reported for the first time together with their full assignment of the normal modes, as calculated using Density Functional Theory (DFT) methods with the B3LYP exchange-correlation functional coupled to the 6-31G(d) and 6-311+G(2d,p) basis sets. Molecular docking studies were performed on five penicillins, including OXN, CBC, and AZL. Subsequently, their chemical reactivity and correlated efficiency towards specific pathogenic strains were revealed by combining frontier molecular orbital (FMO) data with molecular electrostatic potential (MEP) surfaces. Their bactericidal activity was tested and confirmed on a couple of species, both Gram-positive and Gram-negative, by using the disk diffusion method. Additionally, a surface-enhanced Raman spectroscopy (SERS)-principal component analysis (PCA)-based resistogram of A. hydrophila is proposed as a clinically relevant insight resulting from the synergistic cheminformatics and vibrational study on CBC and AZL.

YOLO Algorithm for Long-Term Tracking and Detection of Escherichia Coli at Different Depths of Microchannels Based on Microsphere Positioning Assistance

The effect evaluation of the antibiotic susceptibility test based on bacterial solution is of great significance for clinical diagnosis and prevention of antibiotic abuse. Applying a microfluidic chip as the detection platform, the detection method of using microscopic images to observe bacteria under antibiotic can greatly speed up the detection time, which is more suitable for high-throughput detection. However, due to the influence of the depth of the microchannel, there are multiple layers of bacteria under the focal depth of the microscope, which greatly affects the counting and recognition accuracy and increases the difficulty of relocation of the target bacteria, as well as extracting the characteristics of bacterial liquid changes under the action of antibiotics. After the focal depth of the target bacteria is determined, although the z-axis can be controlled with the help of a three-dimensional micro-operator, the equipment is difficult to operate and the long-term changes of the target bacteria cannot be tracked quickly and accurately. In this paper, the YOLOv5 algorithm is adopted to accurately identify bacteria with different focusing states of multi-layer bacteria at the z-axis with any focal depth. In the meantime, a certain amount of microspheres were mixed into bacteria to assist in locating bacteria, which was convenient for tracking the growth state of bacteria over a long period, and the recognition rates of both bacteria and microspheres were high. The recognition accuracy and counting accuracy of bacteria are 0.734 and 0.714, and the two recognition rates of microspheres are 0.910 and 0.927, respectively, which are much higher than the counting accuracy of 0.142 for bacteria and 0.781 for microspheres with the method of enhanced depth of field (EDF method). Moreover, during long-term bacterial tracking and detection, target bacteria at multiple z-axis focal depth positions can be recorded by the aid of microspheres as a positioning aid for 3D reconstruction, and the focal depth positions can be repositioned within 3-10 h. The structural similarity (SSIM) of microscopic image structure differences at the same focal depth fluctuates between 0.960 and 0.975 at different times, and the root-mean-square error (RMSE) fluctuates between 8 and 12, which indicates that the method also has good relocation accuracy. Thus, this method provides the basis for rapid, high-throughput, and long-term analysis of microscopic changes (e.g., morphology, size) of bacteria detection under the addition of antibiotics with different concentrations based on microfluidic channels in the future.

Single Cell Raman Spectroscopy Deuterium Isotope Probing for Rapid Antimicrobial Susceptibility Test of Elizabethkingia spp

Nosocomial infection by multi-drug resistance Elizabethkingia spp. is an emerging concern with severe clinical consequences, particularly in immunocompromised individuals and infants. Efficient control of this infection requires quick and reliable methods to determine the appropriate drugs for treatment. In this study, a total of 31 Elizabethkingia spp., including two standard strains (ATCC 13253 and FMS-007) and 29 clinical isolates obtained from hospitals in China were subjected to single cell Raman spectroscopy analysis coupled with deuterium probing (single cell Raman-DIP). The results demonstrated that single cell Raman-DIP could determine antimicrobial susceptibility of Elizabethkingia spp. in 4 h, only one third of the time required by standard broth microdilution method. The method could be integrated into current clinical protocol for sepsis and halve the report time. The study also confirmed that minocycline and levofloxacin are the first-line antimicrobials for Elizabethkingia spp. infection.

An antibiotic concentration gradient microfluidic device integrating surface-enhanced Raman spectroscopy for multiplex antimicrobial susceptibility testing

Antimicrobial susceptibility testing (AST) is a key measure in clinical microbiology laboratories to enable appropriate antimicrobial administration. During an AST, the determination of the minimum inhibitory concentration (MIC) is an important step in which the bacterial responses to an antibiotic at a series of concentrations obtained in separate bacterial growth chambers or sites are compared. However, the preparation of different antibiotic concentrations is time-consuming and labor-intensive. In this paper, we present a microfluidic device that generates a concentration gradient for antibiotics that is produced by diffusion in the laminar flow regime along a series of lateral microwells to encapsulate bacteria for antibiotic treatment. All the AST preparation steps (including bacterium loading, antibiotic concentration generation, buffer washing, and isolated bacterial growth with an antibiotic) can be performed in a single chip. The viable bacterial cells in each microwell after the antibiotic treatment are then quantified by their surface-enhanced Raman scattering (SERS) signals that are acquired after placing a uniform SERS-active substrate in contact with all the microwells. For proof-of-concept, we demonstrated the AST performance of this system on ampicillin (AMP)-susceptible and -resistant E. coli strains. Compared with the parameters for conventional AST methods, the AST procedure based on this chip requires only 20 μL of bacteria solution and 5 h of operation time. This result indicates that this integrated system can greatly shorten and simplify the tedious and labor-intensive procedures required for current standard AST methods.

Diagnosis of Bloodstream Infections: An Evolution of Technologies towards Accurate and Rapid Identification and Antibiotic Susceptibility Testing

Bloodstream infections (BSI) are a leading cause of death worldwide. The lack of timely and reliable diagnostic practices is an ongoing issue for managing BSI. The current gold standard blood culture practice for pathogen identification and antibiotic susceptibility testing is time-consuming. Delayed diagnosis warrants the use of empirical antibiotics, which could lead to poor patient outcomes, and risks the development of antibiotic resistance. Hence, novel techniques that could offer accurate and timely diagnosis and susceptibility testing are urgently needed. This review focuses on BSI and highlights both the progress and shortcomings of its current diagnosis. We surveyed clinical workflows that employ recently approved technologies and showed that, while offering improved sensitivity and selectivity, these techniques are still unable to deliver a timely result. We then discuss a number of emerging technologies that have the potential to shorten the overall turnaround time of BSI diagnosis through direct testing from whole blood-while maintaining, if not improving-the current assay's sensitivity and pathogen coverage. We concluded by providing our assessment of potential future directions for accelerating BSI pathogen identification and the antibiotic susceptibility test. While engineering solutions have enabled faster assay turnaround, further progress is still needed to supplant blood culture practice and guide appropriate antibiotic administration for BSI patients.

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

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-intensive, complex, inaccurate, and slow (requiring several days, depending on the growth of pathogenic bacteria). Recently, Raman-based identification and AST technologies have played an increasingly important role in fighting AMR. This review summarizes major Raman-based techniques for bacterial identification and AST, including spontaneous Raman scattering, surface-enhanced Raman scattering (SERS), and coherent Raman scattering (CRS) imaging. Then, we discuss recent developments in rapid identification and AST methods based on Raman technology. Finally, we highlight the major challenges and potential future efforts to improve clinical outcomes through rapid bacterial identification and AST.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTX^r-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I₉₉₀/I₁₃₄₈, and the R² reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

Recent Developments in Phenotypic and Molecular Diagnostic Methods for Antimicrobial Resistance Detection in Staphylococcus aureus: A Narrative Review

Staphylococcus aureus is an opportunistic pathogen responsible for a wide range of infections in humans, such as skin and soft tissue infections, pneumonia, food poisoning or sepsis. Historically, S. aureus was able to rapidly adapt to anti-staphylococcal antibiotics and become resistant to several classes of antibiotics. Today, methicillin-resistant S. aureus (MRSA) is a multidrug-resistant pathogen and is one of the most common bacteria responsible for hospital-acquired infections and outbreaks, in community settings as well. The rapid and accurate diagnosis of antimicrobial resistance in S. aureus is crucial to the early initiation of directed antibiotic therapy and to improve clinical outcomes for patients. In this narrative review, I provide an overview of recent phenotypic and molecular diagnostic methods for antimicrobial resistance detection in S. aureus, with a particular focus on MRSA detection. I consider methods for resistance detection in both clinical samples and isolated S. aureus cultures, along with a brief discussion of the advantages and the challenges of implementing such methods in routine diagnostics.

Antibacterial Resistance Pattern in Clinical and Non-clinical Bacteria by Phenotypic and Genotypic Assessment

Bacterial infections represent a very serious problem that threatens human health, antibiotics were designed to attack the causative agents of infectious diseases, but some bacterial pathogens became virulent and resistant to antibiotics by different mechanisms, resistance genes represented one of those mechanisms. This study attempts to screen the existence of five different resistance genes (mecA, TEM, FemA, MexD, and AmpC) among 25 bacterial isolates divided into two groups the first was non-clinical bacterial type strains including Bacillus subtilis, Escherichia coli, Klebsiella pneumonia, Pseudomonas aeruginosa, Salmonella typhi, and the other group includes some clinical bacterial isolates. Evaluation of their susceptibilities to different 12 antibiotic discs and attempting to find the relationship between genotype and phenotype assessment. Different responses were reported which varied from slightly susceptible to multidrug-resistant such as P. aeruginosa and K. pneumonia which could be considered as multidrug-resistant strains. Therefore, detection of resistance gene became crucial and critical to recognize the mechanism of resistance, five pairs of primers were included to investigate five responsible genes belonging to beta-lactamases, efflux pump, and methicillin resistance. Conclusively, the PCR technique is a very accurate tool to check the genetic resistance whether being expressed to phenotype or not. Moreover, the clinical bacterial isolates appeared more resistant that reflecting the impact of the surrounding environment on bacterial behavior.

Major bacterial isolate and antibiotic resistance from routine clinical samples in Southern Ethiopia

Currently, antibiotic-resistant bacterial infections are a challenge for the health care system. Although physicians demand timely drug resistance data to guide empirical treatment, local data is rather scarce. Hence, this study performed a retrospective analysis of microbiological findings at the Hawassa public hospital. Secondary data were retrieved to assess the prevalence and level of drug resistance for the most common bacterial isolates from clinical samples processed at Hawassa University Comprehensive Specialized Hospital. Out of 1085 clinical samples processed in the microbiology laboratory, the prevalence of bacterial infection was 32.6%. Bacterial bloodstream infection was higher in children than in adults (OR, 4; 95% CI 1.8-14.6; p = 0.005). E. coli and K. pneumoniae were the commonest bacterial isolate both in children (36.8%, 26.3%) and in adults (33.3%, 26.7%) from the urine sample while, the leading bacteria identified from the CSF sample was P. aeruginosa, 37% in children and 43% in adult. In this study, all identified bacterial isolates were multi-drug resistant (MDR) ranging from 50 to 91%. The highest proportion of MDR was S. aureus 91.1 followed by K. pneumoniae 87.6%. Since the nationwide investigation of bacterial isolate, and drug resistance is rare in Ethiopia, a report from such type of local surveillance is highly useful to guide empirical therapy by providing awareness on the level resistance of isolates.

Rapid Electrochemical-Based PCR-Less Microbial Quantification and Antimicrobial Susceptibility Profiling Directly From Blood and Urine With Unknown Microbial Load or Species

Novel molecular platforms are available for identifying (ID) the causative agents of microbial infections and generating antimicrobial susceptibility testing (AST) profiles, which can inform the suitable course of treatment. Many methods claim to perform AST in minutes or hours, often ignoring the need for time-consuming steps such as enrichment cultures and isolation of pure cultures. In clinical microbiology laboratories, an infectious microbial must first be cultured (overnight to days) and identified at the species level, followed by a subsequent AST with an additional turnaround time of 12-48 h due to the need for regrowth of the organism in the absence and presence of relevant antibiotics. Here, we present an electrochemical-based direct-from-specimen ID/AST method for reporting directly from unprocessed urine and blood in hours. In a limit of detection study of 0.5-ml whole blood samples for point-of-care and pediatric applications, 16.7% (4/24) of samples contrived at 2 CFU/ml and 100% (24/24) of samples contrived at 6 CFU/ml were reported positive in 6.5 h, indicating a limit of detection of 6 CFU/ml. In a separate direct-from-specimen AST study, the categorical susceptibility was reported correctly for blinded susceptible, intermediate, resistant, and polymicrobial contrived specimens in 4 h.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Speciation Analysis of Cr(VI) and Cr(III) in Water with Surface-Enhanced Raman Spectroscopy

Identifying and quantifying chromium in water are important for the protection of precious water resources from chromium pollution. Standard methods however are unable to easily distinguish toxic hexavalent chromium, Cr(VI), from innocuous trivalent chromium, Cr(III), are time-consuming, or require large sample quantity. We show in this report that Cr(VI) and Cr(III) in water can be differentiated based on their distinct spectral features of surface-enhanced Raman scattering (SERS). Their SERS signals exhibit different pH dependences: the SERS features of Cr(VI) and Cr(III) are most prominent at pH values of 10 and 5.5, respectively. The obtained limit of detection of Cr(VI) in water is below 0.1 mg/L. Both concentration curves of their SERS signals show Langmuir sorption isotherm behavior. A procedure was developed to quantify Cr(VI) concentration based on the direct retrieval or addition method with an error of 10%. Finally, the SERS detection of Cr(VI) is shown to be insensitive to co-present Cr(III). The developed SERS procedure offers potential to monitor toxic chromium in fields.

Sensible Functional Linear Discriminant Analysis Effectively Discriminates Enhanced Raman Spectra of Mycobacterium Species

Tuberculosis caused by Mycobacterium tuberculosis complex (MTBC) is one of the major infectious diseases in the world. Identification of MTBC and differential diagnosis of nontuberculous mycobacteria (NTM) species impose challenges because of their taxonomic similarity. This study describes a differential diagnosis method using the surface-enhanced Raman scattering (SERS) measurement of molecules released by Mycobacterium species. Conventional principal component analysis and linear discriminant analysis methods successfully separated the acquired spectrum of MTBC from those of NTM species but failed to distinguish between the spectra of different NTM species. A novel sensible functional linear discriminant analysis (SLDA), projecting the averaged spectrum of a bacterial specie to the subspace orthogonal to the within-species random variation, thereby eliminating its influence in applying linear discriminant analysis, was employed to effectively discriminate not only MTBC but also species of NTM. The successful demonstration of this SERS-SLDA method opens up new opportunities for the rapid differentiation of Mycobacterium species.

Microfluidic Systems for Antimicrobial Susceptibility Testing

Human health is threatened by the spread of antimicrobial resistance and resulting infections. One reason for the resistance spread is the treatment with inappropriate and ineffective antibiotics because standard antimicrobial susceptibility testing methods are time-consuming and laborious. To reduce the antimicrobial susceptibility detection time, minimize treatments with empirical broad-spectrum antibiotics, and thereby combat the further spread of antimicrobial resistance, faster and point-of-care methods are needed. This requires many different research approaches. Microfluidic systems for antimicrobial susceptibility testing offer the possibility to reduce the detection time, as small sample and reagent volumes can be used and the detection of single cells is possible. In some cases, the aim is to use human samples without pretreatment or pre-cultivation. This chapter first provides an overview of conventional detection methods. It then presents the potential of and various current approaches in microfluidics. The focus is on microfluidic methods for phenotypic antimicrobial susceptibility testing.

Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing

Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.