Diagnosis of Bacterial Pathogens in the Urine of Urinary-Tract-Infection Patients Using Surface-Enhanced Raman Spectroscopy

Research progress and application of bacterial traceability technology

Bacterial traceability refers to the use of a range of techniques to trace the origins and transmission pathways of bacteria. It is crucial in controlling the spread of diseases, analyzing bioterrorism incidents, and advancing microbial forensics. In recent years, the frequency and scope of bacterial outbreaks have continued to escalate, exerting significant impacts on global biosecurity, public health, and other areas. Consequently, it is required to process traceability of bacteria timely and accurately around the globe. The rapid development of biological and physicochemical traceability techniques provides convenience for tracing bacteria. These techniques not only surpass traditional methods in terms of sensitivity, traceability and throughput, but also find more extensive applications in elucidating bacterial growth mechanisms, transmission routes, and geographical origins. This paper systematically reviews the latest research progress and applications of technologies of bacterial traceability, highlighting key advancements and projecting future trends, with the intent of providing a valuable reference for researchers, facilitating further studies and innovations in this field.

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.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications

BACKGROUND

The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.

AIM OF REVIEW

Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.

A rapid screening platform for antibiotic susceptibility testing based on a simple colorimetric method

Rapid screening platforms for antibiotic susceptibility testing (AST) are important in inhibiting bacterial resistance in clinical practice. Herein, a rapid screening platform is reported for AST, which is based on nanofiber membrane enrichment bacteria-assisted cell counting Kit-8 (CCK8) colorimetry. The absorbance of CCK8 formazan has a linear relationship with the number of bacteria. The interference of antibiotics in the absorbance of CCK8 formazan could be eliminated by separating planktonic bacteria from the culture medium using nanofiber membranes. The total detection time is 7-9 h, using the new screening platform, which is significantly shorter than that with the traditional method, and the limit of detection of this method is 10 CFU mL-1. The evaluation results of antibiotic susceptibility are identical when using the new screening method and traditional methods. This method meets the definition of "rapid testing" for antibiotic susceptibility by most microbiologists. Furthermore, the new screening platform for antibiotic susceptibility testing ability in vitro was proved using E. coli in urine and blood, and S. aureus in wound fluid as practical samples. All the results showed that the new screening platform is a promising method for rapid antibiotic susceptibility testing in vitro.

Evaluating the efficiency of a nomogram based on the data of neurosurgical intensive care unit patients to predict pulmonary infection of multidrug-resistant Acinetobacter baumannii

Background

Pulmonary infection caused by multidrug-resistant Acinetobacter baumannii (MDR-AB) is a common and serious complication after brain injury. There are no definitive methods for its prediction and it is usually accompanied by a poor prognosis. This study aimed to construct and evaluate a nomogram based on patient data from the neurosurgical intensive care unit (NSICU) to predict the probability of MDR-AB pulmonary infection.

Methods

In this study, we retrospectively collected patient clinical profiles, early laboratory test results, and doctors' prescriptions (66 variables). Univariate and backward stepwise regression analyses were used to screen the variables to identify predictors, and a nomogram was built in the primary cohort based on the results of a logistic regression model. Discriminatory validity, calibration validity, and clinical utility were evaluated using validation cohort 1 based on receiver operating characteristic curves, calibration curves, and decision curve analysis (DCA). For external validation based on predictors, we prospectively collected information from patients as validation cohort 2.

Results

Among 2115 patients admitted to the NSICU between December 1, 2019, and December 31, 2021, 217 were eligible for the study, including 102 patients with MDR-AB infections (102 cases) and 115 patients with other bacterial infections (115 cases). We randomly categorized the patients into the primary cohort (70%, N=152) and validation cohort 1 (30%, N=65). Validation cohort 2 consisted of 24 patients admitted to the NSICU between January 1, 2022, and March 31, 2022, whose clinical information was prospectively collected according to predictors. The nomogram, consisting of only six predictors (age, NSICU stay, Glasgow Coma Scale, meropenem, neutrophil to lymphocyte ratio, platelet to lymphocyte ratio), had significantly high sensitivity and specificity (primary cohort AUC=0.913, validation cohort 1 AUC=0.830, validation cohort 2 AUC=0.889) for early identification of infection and had great calibration (validation cohort 1,2 P=0.3801, 0.6274). DCA confirmed that the nomogram is clinically useful.

Conclusion

Our nomogram could help clinicians make early predictions regarding the onset of pulmonary infection caused by MDR-AB and implement targeted interventions.

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.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

Rapid identification of the resistance of urinary tract pathogenic bacteria using deep learning-based spectroscopic analysis

The resistance of urinary tract pathogenic bacteria to various antibiotics is increasing, which requires the rapid detection of infectious pathogens for accurate and timely antibiotic treatment. Here, we propose a rapid diagnosis strategy for the antibiotic resistance of bacteria in urinary tract infections (UTIs) based on surface-enhanced Raman scattering (SERS) using a positively charged gold nanoparticle planar solid SERS substrate. Then, an intelligent identification model for SERS spectra based on the deep learning technique is constructed to realize the rapid, ultrasensitive, and non-labeled detection of pathogenic bacteria. A total of 54,000 SERS spectra were collected from 18 isolates belonging to 6 species of common UTI bacteria in this work to realize identification of bacterial species, antibiotic sensitivity, and multidrug resistance (MDR) via convolutional neural networks (CNN). This method significantly simplify the Raman data processing processes without background removing and smoothing, however, achieving 96% above classification accuracy, which was significantly greater than the 85% accuracy of the traditional multivariate statistical analysis algorithm principal component analysis combined with the K-nearest neighbor (PCA-KNN). This work clearly elucidated the potential of combining SERS and deep learning technique to realize culture-free identification of pathogenic bacteria and their associated antibiotic sensitivity.

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.

Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

Urinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (10⁵ CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

Applications of surface-enhanced Raman spectroscopy in detection fields

Surface-enhanced Raman spectroscopy (SERS) is a Raman spectroscopy technique that has been widely used in food safety, environmental monitoring, medical diagnosis and treatment and drug monitoring because of its high selectivity, sensitivity, rapidness, simplicity and specificity in identifying molecular structures. This review introduces the detection mechanism of SERS and summarizes the most recent progress concerning the use of SERS for the detection and characterization of molecules, providing references for the later research of SERS in detection fields.

Rapid identification of uropathogens by combining Alfred 60 system with matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry technology

Rapid identification of uropathogens is needed to determine appropriate antimicrobial therapy. This study evaluated performance of the Alfred 60 system combined with matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS) technology for rapid identification of uropathogens. The Alfred 60 system was used to screen urine cultures, followed by identifying the microbial pathogen in positive cultures using MALDI-TOF MS. The Alfred 60 detected positive cultures by measuring the turbidity of urine samples, which were transferred automatically to vials containing liquid medium and incubated for 3.5 h at 35 °C in the Alfred 60 system. Vials that showed growth were removed and centrifuged. The pellet was subjected to MALDI-TOF MS identification. In parallel, positive urine samples were inoculated onto agar plates for identification by conventional culture. The time required to detect positive urine cultures with Alfred 60 and identify the uropathogens with MALDI-TOF MS ranged from 15 min to 3.5 h. Among 146 positive urine samples tested, conventional cultures showed three culture groups: group 1 included 101 samples with growth of a single type of microorganism; group 2 included 34 samples with 2 types of microorganisms; and group 3 included 11 samples with ≥ 3 types of microorganisms. Direct identification by MALDI-TOF MS was concordant with 95% of the samples in group 1, 100% of the principal microorganism in group 2, but could not identify microorganisms in group 3. This combination of methods provides rapid, reliable microbial identification for most positive urine cultures.

Enhancing Disease Diagnosis: Biomedical Applications of Surface-Enhanced Raman Scattering

Surface-enhanced Raman scattering (SERS) has recently gained increasing attention for the detection of trace quantities of biomolecules due to its excellent molecular specificity, ultrasensitivity, and quantitative multiplex ability. Specific single or multiple biomarkers in complex biological environments generate strong and distinct SERS spectral signals when they are in the vicinity of optically active nanoparticles (NPs). When multivariate chemometrics are applied to decipher underlying biomarker patterns, SERS provides qualitative and quantitative information on the inherent biochemical composition and properties that may be indicative of healthy or diseased states. Moreover, SERS allows for differentiation among many closely-related causative agents of diseases exhibiting similar symptoms to guide early prescription of appropriate, targeted and individualised therapeutics. This review provides an overview of recent progress made by the application of SERS in the diagnosis of cancers, microbial and respiratory infections. It is envisaged that recent technology development will help realise full benefits of SERS to gain deeper insights into the pathological pathways for various diseases at the molecular level.