SERS Biosensor Based on Engineered 2D-Aperiodic Nanostructure for In-Situ Detection of Viable Brucella Bacterium in Complex Matrix

Advanced Vibrational Spectroscopy and Bacteriophages Team Up: Dynamic Synergy for Medical and Environmental Applications

Bacteriophages are emerging as a promising alternative in combating antibiotic-resistant bacteria amidst the escalating global antimicrobial resistance crisis. Recently, there has been a notable resurgence of interest in phages, prompting extensive research into their therapeutic potential. Beyond conventional microbiology and virology techniques, such as genomics and proteomics, novel phenotypic and chemical characterization methods are being explored. Among these, there is a growing interest in vibrational spectroscopy, especially in advanced modalities such as surface-enhanced Raman spectroscopy (SERS), tip-enhanced Raman spectroscopy (TERS), and atomic force microscopy-infrared spectroscopy (AFM-IR), which offer improved sensitivity and spatial resolution. This review explores the spectrum of uses of vibrational spectroscopy for bacteriophages, including its role in diagnostics, biosensing, phage detection, assistance in phage-based therapy, and advancing basic research.

Raman spectrum combined with deep learning for precise recognition of Carbapenem-resistant Enterobacteriaceae

Carbapenem-resistant Enterobacteriaceae (CRE) is a major pathogen that poses a serious threat to human health. Unfortunately, currently, there are no effective measures to curb its rapid development. To address this, an in-depth study on the surface-enhanced Raman spectroscopy (SERS) of 22 strains of 7 categories of CRE using a gold silver composite SERS substrate was conducted. The residual networks with an attention mechanism to classify the SERS spectrum from three perspectives (pathogenic bacteria type, enzyme-producing subtype, and sensitive antibiotic type) were performed. The results show that the SERS spectrum measured by the composite SERS substrate was repeatable and consistent. The SERS spectrum of CRE showed varying degrees of species differences, and the strain difference in the SERS spectrum of CRE was closely related to the type of enzyme-producing subtype. The introduced attention mechanism improved the classification accuracy of the residual network (ResNet) model. The accuracy of CRE classification for different strains and enzyme-producing subtypes reached 94.0% and 96.13%, respectively. The accuracy of CRE classification by pathogen sensitive antibiotic combination reached 93.9%. This study is significant for guiding antibiotic use in CRE infection, as the sensitive antibiotic used in treatment can be predicted directly by measuring CRE spectra. Our study demonstrates the potential of combining SERS with deep learning algorithms to identify CRE without culture labels and classify its sensitive antibiotics. This approach provides a new idea for rapid and accurate clinical detection of CRE and has important significance for alleviating the rapid development of resistance to CRE.

Immuno-SPR biosensor for the detection of Brucella abortus

A proof of principle biosensor for the Brucella abortus recognition onsite is presented. The system is based on a plasmonic optical fiber probe functionalized with an oriented antibody layer immobilized on a short polyethyleneglycol (PEG) interface through carbodiimide chemistry and protein G as an intermediate layer. The biosensor is inserted in a holder built in 3D printing technology, obtaining a custom holder useful for housing the sample to be measured and the equipment. The removable sensor chip is a low-cost Surface Plasmon Resonance (SPR) platform based on D-shaped plastic optical fibers (POFs), built-in in 3D printed connectors, used here for the first time to detect bacteria via a bio-receptor layer specific for its membrane protein. The performances of the biosensor in Brucella abortus recognition are tested by using two different SPR-POF probes combined with the same bio-receptor layer. The best sensor configuration has presented a sensitivity at low concentrations of one order of magnitude greater than the other. A limit of detection (LoD) of 2.8 bacteria/mL is achieved well competitive with other systems but without the need for amplification or special sample treatments. Specificity has been tested using Salmonella bacteria, and reproducibility, regenerability and stability are moreover evaluated. These experimental results pave the way for building an efficient and specific biosensor system for Brucella abortus detection onsite and in a few minutes. Moreover, the proposed POF-based SPR biosensor device, with respect to the already available technologies, could be a Point-of-care-test (POCT), simple to use, small-size and portable, low-cost, don't necessary of a microfluidic system, and can be connected to the Internet (IoT).

Optimization of the immunorecognition layer towards Brucella sp. on gold surface for SPR platform

A successful immunosensor is characterized by a proper antibody immobilization and orientation in order to enhance the antigen recognition. In this work, a thorough characterization of the antibody functionalized gold surface is performed to set up the best conditions to implement in an optical platform for the detection of Brucella sp. Two different strategies are evaluated, based on a random immobilization and on an oriented one: a direct antibody immobilization on carboxylic mixed polyethylene (PEG) self-assembled monolayer (SAM) or only carboxylic PEG SAM interface is compared to an oriented immobilization on a layer of protein G on the same PEG SAM interfaces. X-ray Photoelectron Spectroscopy (XPS), Time of Flight Secondary Ion Mass Spectrometry (ToF-SIMS) and contact angle (CA) are used to chemically characterize the gold functionalized surface and ToF-SIMS is also used to confirm the right antibody orientation. Optical characterization is applied to monitor the functionalization steps and fluorescence measurements are used to set up the proper experimental conditions and also to detect Brucella bacteria on the surface. Best results are obtained with a 10 ng/μl incubation solution of antibody immobilized, in an oriented way, on a mixed PEG SAM interface.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Mg-Doped ZnO Nanoparticles with Tunable Band Gaps for Surface-Enhanced Raman Scattering (SERS)-Based Sensing

Semiconductors have great potential as surface-enhanced Raman scattering (SERS) substrates due to their excellent physiochemical properties. However, they provide low signal enhancements relative to their plasmonic counterparts, which necessitates innovation in their synthesis and application. Substitutional atomic doping is proposed to improve SERS enhancement by controlling electronic properties, such as the band gap. In this work, zinc oxide (ZnO) nanoparticles were synthesized by co-precipitation and doped with magnesium (Mg) at concentrations ranging from 2-10%. Nanoparticle morphology and size were obtained by scanning electron microscopy (SEM). Elemental composition and chemical states were determined using X-ray photoelectron spectroscopy (XPS). Optical properties were obtained with a UV-vis spectrophotometer, while a Raman spectrometer was used to acquire Raman signal enhancements. Stability was assessed by UV-vis spectroscopy, while cytotoxicity was evaluated by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. The results showed that the absorption edge of Mg-doped ZnO nanoparticles was red-shifted compared to pure ZnO nanoparticles. The band gap decreased (3.3-3.01 eV) with increasing Mg doping, while the highest Raman enhancement was observed at 2% doping. No significant cytotoxic effects were observed at low concentrations (3-12 μg/mL). Overall, this study provides evidence for the tunability of ZnO substrates and may serve as a platform for applications in molecular biosensing.

Nanotechnology in food and water security: on-site detection of agricultural pollutants through surface-enhanced Raman spectroscopy

Agricultural pollutants are harmful components threatening human health, wildlife, the environment, and the ecosystem. To avoid their exposure, developing prevention and detection systems with high sensitivity and selectivity is required. Most conventional methods, including molecular and chromatographic techniques, cannot be adopted for outdoor on-site detection even though they can provide sensitive and selective detection. Thus, detection platforms that can provide on-site detection via miniaturized and high throughput systems should be developed. As an alternative method, surface-enhanced Raman scattering (SERS) provides unique information about the substances in the presence of plasmonic nanostructures, and it can be portable with the use of portable detection systems and spectrometers. In this study, on-site detection of agricultural pollutants through SERS is reviewed. Three different types of agricultural pollutants were pointed out. On-site detection of biological pollutants, including bacteria and viruses, is reviewed as the first type of pollutant. As a second type, the detection of pesticides, antibiotics, and additives are focused on as chemical pollutants. The third group includes the detection of microplastics and also nanoparticles from the environment.

Surface plasmon resonance aptasensor for Brucella detection in milk

A Surface Plasmon Resonance (SPR) aptasensor was developed for the detection of Brucella melitensis (B. melitensis) in milk samples. Brucellosis is a bacterial zoonotic disease with global distribution caused mostly by contaminated milk or their products. Aptamers recognizing B. melitensis were selected following a whole bacteria-SELEX procedure. Two aptamers were chosen for high affinity and high specificity. The high affinity aptamer (B70 aptamer) was immobilized on the surface of magnetic silica core-shell nanoparticles for initial purification of the target bacteria cells from milk matrix. Another aptamer, highly specific for B. melitensis cells (B46 aptamer), was used to prepare SPR sensor chips for sensitive determination of Brucella in eluted samples from magnetic purification since direct injection of milk samples to SPR sensor chips is known for a high background unspecific signal. Thus, we integrated a quick and efficient magnetic isolation step for subsequent instant detection of B. melitensis contamination in one ml of milk sample by SPR with a LOD value as low as 27 ± 11 cells.