Advances in Optical Detection of Human-Associated Pathogenic Bacteria

Recent advances in microfluidic-based spectroscopic approaches for pathogen detection

Rapid identification of pathogens with higher sensitivity and specificity plays a significant role in maintaining public health, environmental monitoring, controlling food quality, and clinical diagnostics. Different methods have been widely used in food testing laboratories, quality control departments in food companies, hospitals, and clinical settings to identify pathogens. Some limitations in current pathogens detection methods are time-consuming, expensive, and laborious sample preparation, making it unsuitable for rapid detection. Microfluidics has emerged as a promising technology for biosensing applications due to its ability to precisely manipulate small volumes of fluids. Microfluidics platforms combined with spectroscopic techniques are capable of developing miniaturized devices that can detect and quantify pathogenic samples. The review focuses on the advancements in microfluidic devices integrated with spectroscopic methods for detecting bacterial microbes over the past five years. The review is based on several spectroscopic techniques, including fluorescence detection, surface-enhanced Raman scattering, and dynamic light scattering methods coupled with microfluidic platforms. The key detection principles of different approaches were discussed and summarized. Finally, the future possible directions and challenges in microfluidic-based spectroscopy for isolating and detecting pathogens using the latest innovations were also discussed.

Emerging bacterial factors for understanding pathogenesis of endometriosis

The pathogenesis of endometriosis is a complex process, and recent research has introduced novel hypotheses in this field. This review summarizes recent studies on the pathogenesis of endometriosis. We focused on several classical hypotheses, as well as their interactions with the microenvironment of hormonal dependence and immunosuppression. Furthermore, we highlighted the emergence of bacterial factors associated with endometriosis. Recent advances in next-generation sequencing (NGS) have revealed the presence and detailed distribution of these bacteria as well as the involvement of specific bacteria in pathogenesis. These factors alter the microenvironment in the early stages of endometriosis development, leading to lesion formation. Understanding the mechanisms underlying the early development of endometriosis from a new perspective would be helpful for the development of novel therapeutic agents for endometriosis.

A Critical Review on Detection of Foodborne Pathogens Using Electrochemical Biosensors

An outbreak of foodborne pathogens would cause severe consequences. Detecting and diagnosing foodborne diseases is crucial for food safety, and it is increasingly important to develop fast, sensitive, and cost-effective methods for detecting foodborne pathogens. In contrast to traditional methods, such as medium-based culture, nucleic acid amplification test, and enzyme-linked immunosorbent assay, electrochemical biosensors possess the advantages of simplicity, rapidity, high sensitivity, miniaturization, and low cost, making them ideal for developing pathogen-sensing devices. The biorecognition layer, consisting of recognition elements, such as aptamers, antibodies and bacteriophages, and other biomolecules or polymers, is the most critical component to determine the selectivity, specificity, reproducibility, and lifetime of a biosensor when detecting pathogens in a biosample. Furthermore, nanomaterials have been frequently used to improve electrochemical biosensors for sensitively detecting foodborne pathogens due to their high conductivity, surface-to-volume ratio, and electrocatalytic activity. In this review, we survey the characteristics of biorecognition elements and nanomaterials in constructing electrochemical biosensors applicable for detecting foodborne pathogens during the past five years. As well as the challenges and opportunities of electrochemical biosensors in the application of foodborne pathogen detection are discussed.

Gold Nanoparticle-Based Colorimetric Biosensing for Foodborne Pathogen Detection

Ensuring safe high-quality food is an ongoing priority, yet consumers face heightened risk from foodborne pathogens due to extended supply chains and climate change in the food industry. Nanomaterial-based assays are popular and have recently been developed to ensure food safety and high quality. This review discusses strategies for utilizing gold nanoparticles in colorimetric biosensors. The visible-signal biosensor proves to be a potent sensing technique for directly measuring targets related to foodborne pathogens in the field of food analysis. Among visible-signal biosensors, the localized surface plasmon resonance (LSPR) biosensor has garnered increasing attention and experienced rapid development in recent years. This review succinctly introduces the origin of LSPR theory, providing detailed insights into its fundamental principles. Additionally, this review delves into the application of nanotechnology for the implementation of the LSPR biosensor, exploring methods for utilizing gold nanoparticles and elucidating the factors that influence the generation of visible signals. Several emerging technologies aimed at simple and rapid immunoassays for onsite applications have been introduced in the food industry. In the foreseeable future, field-friendly colorimetric biosensors could be adopted in food monitoring systems. The onsite and real-time detection of possible contaminants and biological substances in food and water is essential to ensure human health and safety.

Texture-based speciation of otitis media-related bacterial biofilms from optical coherence tomography images using supervised classification

Otitis media (OM) is primarily a bacterial middle-ear infection prevalent among children worldwide. In recurrent and/or chronic OM cases, antibiotic-resistant bacterial biofilms can develop in the middle ear. A biofilm related to OM typically contains one or multiple bacterial strains, the most common include Haemophilus influenzae, Streptococcus pneumoniae, Moraxella catarrhalis, Pseudomonas aeruginosa, and Staphylococcus aureus. Optical coherence tomography (OCT) has been used clinically to visualize the presence of bacterial biofilms in the middle ear. This study used OCT to compare microstructural image texture features from primary bacterial biofilms in vitro and in vivo. The proposed method applied supervised machine-learning-based frameworks (SVM, random forest (RF), and XGBoost) to classify and speciate multiclass bacterial biofilms from the texture features extracted from OCT B-Scan images obtained from in vitro cultures and from clinically-obtained in vivo images from human subjects. Our findings show that optimized SVM-RBF and XGBoost classifiers can help distinguish bacterial biofilms by incorporating clinical knowledge into classification decisions. Furthermore, both classifiers achieved more than 95% of AUC (area under receiver operating curve), detecting each biofilm class. These results demonstrate the potential for differentiating OM-causing bacterial biofilms through texture analysis of OCT images and a machine-learning framework, which could provide additional clinically relevant data during real-time in vivo characterization of ear infections.

Primer Exchange Reaction (PER)-Based Construction of Scaffold for Low-Speed Centrifugation-Based Isolation and Quantitative Analysis of P. aeruginosa and its application in analyzing uterine secretions with intrauterine adhesion

Efficient isolation and sensitive quantification of Pseudomonas aeruginosa (P. aeruginosa) are crucial for identifying intrauterine infections and preventing the occurrence of intrauterine adhesion (IUA). However, traditional approaches, such as culture-based approach, are time-consuming. Herein, we constructed a detection scaffold by using primer exchange reaction (PER) that integrated the low-speed centrifugation-based isolation and sensitive quantification of target pathogenic bacteria. The established approach possesses several advantages, including (i) the approach is capable of simultaneous isolation and sensitive quantification of target bacteria; (ii) low-speed centrifugation or even manual equipment could be used to isolate target bacteria; and (iii) a low limit of detection was obtained as 54 cfu/mL. Based on this, the approach is a promising approach in analyzing P. aeruginosa from uterine secretions with IUA.

Sensitive and Direct Analysis of Pseudomonas aeruginosa through Self-Primer-Assisted Chain Extension and CRISPR-Cas12a-Based Color Reaction

Pseudomonas aeruginosa (P. aeruginosa) is a common opportunistic Gram-negative pathogen that may cause infections to immunocompromised patients. However, sensitive and reliable analysis of P. aeruginosa remains a huge challenge. In this method, target recognition assists the formation of a self-primer and initiates single-stranded chain production. The produced single-stranded DNA chain is identified by CRISPR-Cas12a, and consequently, the trans-cleavage activity of the Cas12a enzyme is activated to parallelly digest Ag+ aptamer sequences that are chelated with silver ions (Ag+). The released Ag+ reacted with 3,3',5,5'-tetramethylbenzidine (TMB) for coloring. Compared with the traditional color developing strategies, which mainly rely on the DNA hybridization, the color developing strategy in this approach exhibits a higher efficiency due to the robust trans-cleavage activity of the Cas12a enzyme. Consequently, the method shows a low limit of detection of a wide detection of 5 orders of magnitudes and a low limit of detection of 21 cfu/mL, holding a promising prospect in early diagnosis of infections. Herein, we develop a sensitive and reliable method for direct and colorimetric detection of P. aeruginosa by integrating self-primer-assisted chain production and CRISPR-Cas12a-based color reaction and believe that the established approach will facilitate the development of bacteria-analyzing sensors.

Optimizing Excitation Light for Accurate Rapid Bacterial Species Identification with Autofluorescence

Rapid identification of bacterial species in patient samples is essential for the treatment of infectious diseases and the economics of health care. In this study, we investigated an algorithm to improve the accuracy of bacterial species identification with fluorescence spectroscopy based on autofluorescence from bacteria, and excitation wavelengths suitable for identification. The diagnostic accuracy of each algorithm for ten bacterial species was verified in a machine learning classifier algorithm. The three machine learning algorithms with the highest diagnostic accuracy, extra tree (ET), logistic regression (LR), and multilayer perceptron (MLP), were used to determine the number and wavelength of excitation wavelengths suitable for the diagnosis of bacterial species. The key excitation wavelengths for the diagnosis of bacterial species were 280 nm, 300 nm, 380 nm, and 480 nm, with 280 nm being the most important. The median diagnostic accuracy was equivalent to that of 200 excitation wavelengths when two excitation wavelengths were used for ET and LR, and three excitation wavelengths for MLP. These results demonstrate that there is an optimum wavelength range of excitation wavelengths required for spectroscopic measurement of bacterial autofluorescence for bacterial species identification, and that measurement of only a few wavelengths in this range is sufficient to achieve sufficient accuracy for diagnosis of bacterial species.

Responsive Janus droplets as modular sensory layers for the optical detection of bacteria

The field of biosensor development is fueled by innovations in new functional transduction materials and technologies. Material innovations promise to extend current sensor hardware limitations, reduce analysis costs, and ensure broad application of sensor methods. Optical sensors are particularly attractive because they enable sensitive and noninvasive analyte detection in near real-time. Optical transducers convert physical, chemical, or biological events into detectable changes in fluorescence, refractive index, or spectroscopic shifts. Thus, in addition to sophisticated biochemical selector designs, smart transducers can improve signal transmission and amplification, thereby greatly facilitating the practical applicability of biosensors, which, to date, is often hampered by complications such as difficult replication of reproducible selector-analyte interactions within a uniform and consistent sensing area. In this context, stimuli-responsive and optically active Janus emulsions, which are dispersions of kinetically stabilized biphasic fluid droplets, have emerged as a novel triggerable material platform that provides as a versatile and cost-effective alternative for the generation of reproducible, highly sensitive, and modular optical sensing layers. The intrinsic and unprecedented chemical-morphological-optical coupling inside Janus droplets has facilitated optical signal transduction and amplification in various chemo- and biosensor paradigms, which include examples for the rapid and cost-effective detection of major foodborne pathogens. These initial demonstrations resulted in detection limits that rival the capabilities of current commercial platforms. This trend article aims to present a conceptual summary of these initial efforts and to provide a concise and comprehensive overview of the pivotal kinetic and thermodynamic principles that govern the ability of Janus droplets to sensitively and selectively respond to and interact with bacteria.

Covalent organic frameworks (COFs) and metal-organic frameworks (MOFs) as electrochemical sensors for the efficient detection of pharmaceutical residues

Pharmaceutical residues are the undecomposed remains from drugs used in the medical and food industries. Due to their potential adverse effects on human health and natural ecosystems, they are of increasing worldwide concern. The acute detection of pharmaceutical residues can give a rapid examination of their quantity and then prevent them from further contamination. Herein, this study summarizes and discusses the most recent porous covalent-organic frameworks (COFs) and metal-organic frameworks (MOFs) for the electrochemical detection of various pharmaceutical residues. The review first introduces a brief overview of drug toxicity and its effects on living organisms. Subsequently, different porous materials and drug detection techniques are discussed with materials' properties and applications. Then the development of COFs and MOFs has been addressed with their structural properties and sensing applications. Further, the stability, reusability, and sustainability of MOFs/COFs are reviewed and discussed. Besides, COFs and MOFs' detection limits, linear ranges, the role of functionalities, and immobilized nanoparticles are analyzed and discussed. Lastly, this review summarized and discussed the MOF@COF composite as sensors, the fabrication strategies to enhance detection potential, and the current challenges in this area.

An efficient DNAzyme-based DNA scaffold for label-free and sensitive bacterial pathogen detection

Even though it is very important, it is still rather difficult to detect minuscule levels of the bacterial pathogen in clinical practice, such as samples from dental implants. We construct here an efficient scaffold for label-free and sensitive Staphylococcus aureus (S. aureus) detection. The precise recognition of target bacteria by the detection scaffold leads to the self-assembly of Chain i and DNAzyme based cleavage of Chain iii. In detail, active DNAzyme conformation is formed based on the hybridization of Chain iii and Chain ii, and a nicking site is generated in Chain iii, making it possible to form a self-primer in Chain i. With the assistance of DNA polymerase, a single-strand DNA chain is added to the 3' terminal of Chain i, in which process the bacteria is released for the complex to bind with a next detection scaffold, forming a signal recycle. Following DNAzyme-based cleavage, the liberated sequences unroll MB and release G-rich sequences that can specifically bind with the fluorescent dye Thioflavin T (ThT), initiating ThT's fluorescence signal production. The approach demonstrates a wide detection range of 10² CFU/mL and 10⁶ CFU/mL with a low limit of detection of 45 CFU/mL based on the developed detection scaffold, offering good prospects in the diagnosis of bacterial illnesses.

Bacterial Colony Phenotyping with Hyperspectral Elastic Light Scattering Patterns

The elastic light-scatter (ELS) technique, which detects and discriminates microbial organisms based on the light-scatter pattern of their colonies, has demonstrated excellent classification accuracy in pathogen screening tasks. The implementation of the multispectral approach has brought further advantages and motivated the design and validation of a hyperspectral elastic light-scatter phenotyping instrument (HESPI). The newly developed instrument consists of a supercontinuum (SC) laser and an acousto-optic tunable filter (AOTF). The use of these two components provided a broad spectrum of excitation light and a rapid selection of the wavelength of interest, which enables the collection of multiple spectral patterns for each colony instead of relying on single band analysis. The performance was validated by classifying microflora of green-leafed vegetables using the hyperspectral ELS patterns of the bacterial colonies. The accuracy ranged from 88.7% to 93.2% when the classification was performed with the scattering pattern created at a wavelength within the 473-709 nm region. When all of the hyperspectral ELS patterns were used, owing to the vastly increased size of the data, feature reduction and selection algorithms were utilized to enhance the robustness and ultimately lessen the complexity of the data collection. A new classification model with the feature reduction process improved the overall classification rate to 95.9%.

Use of polymers as wavenumber calibration standards in deep-UVRR

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400-1900 cm^-1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.

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

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

Recent Progress in Spectroscopic Methods for the Detection of Foodborne Pathogenic Bacteria

Detection of foodborne pathogens at an early stage is very important to control food quality and improve medical response. Rapid detection of foodborne pathogens with high sensitivity and specificity is becoming an urgent requirement in health safety, medical diagnostics, environmental safety, and controlling food quality. Despite the existing bacterial detection methods being reliable and widely used, these methods are time-consuming, expensive, and cumbersome. Therefore, researchers are trying to find new methods by integrating spectroscopy techniques with artificial intelligence and advanced materials. Within this progress report, advances in the detection of foodborne pathogens using spectroscopy techniques are discussed. This paper presents an overview of the progress and application of spectroscopy techniques for the detection of foodborne pathogens, particularly new trends in the past few years, including surface-enhanced Raman spectroscopy, surface plasmon resonance, fluorescence spectroscopy, multiangle laser light scattering, and imaging analysis. In addition, the applications of artificial intelligence, microfluidics, smartphone-based techniques, and advanced materials related to spectroscopy for the detection of bacterial pathogens are discussed. Finally, we conclude and discuss possible research prospects in aspects of spectroscopy techniques for the identification and classification of pathogens.

Direct Identification of Urinary Tract Pathogens by MALDI-TOF/TOF Analysis and De Novo Peptide Sequencing

For mass spectrometry-based diagnostics of microorganisms, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used to identify urinary tract pathogens. However, it requires a lengthy culture step for accurate pathogen identification, and is limited by a relatively small number of available species in peptide spectral libraries (≤3329). Here, we propose a method for pathogen identification that overcomes the above limitations, and utilizes the MALDI-TOF/TOF MS instrument. Tandem mass spectra of the analyzed peptides were obtained by chemically activated fragmentation, which allowed mass spectrometry analysis in negative and positive ion modes. Peptide sequences were elucidated de novo, and aligned with the non-redundant National Center for Biotechnology Information Reference Sequence Database (NCBInr). For data analysis, we developed a custom program package that predicted peptide sequences from the negative and positive MS/MS spectra. The main advantage of this method over a conventional MALDI-TOF MS peptide analysis is identification in less than 24 h without a cultivation step. Compared to the limited identification with peptide spectra libraries, the NCBI database derived from genome sequencing currently contains 20,917 bacterial species, and is constantly expanding. This paper presents an accurate method that is used to identify pathogens grown on agar plates, and those isolated directly from urine samples, with high accuracy.

Automated classification of otitis media with OCT: augmenting pediatric image datasets with gold-standard animal model data

Otitis media (OM) is an extremely common disease that affects children worldwide. Optical coherence tomography (OCT) has emerged as a noninvasive diagnostic tool for OM, which can detect the presence and quantify the properties of middle ear fluid and biofilms. Here, the use of OCT data from the chinchilla, the gold-standard OM model for the human disease, is used to supplement a human image database to produce diagnostically relevant conclusions in a machine learning model. Statistical analysis shows the datatypes are compatible, with a blended-species model reaching ∼95% accuracy and F1 score, maintaining performance while additional human data is collected.

Fluorescence immunoassay for the targeted determination of trace Listeria monocytogenes based on immunomagnetic separation and CdZnTe quantum dot indication

Infections from invasive Listeria monocytogenes (L. monocytogenes) frequently occur in food and can cause high morbidity and death. Thus, the sensitive, specific, and rapid detection of L. monocytogenes is critical for ensuring food safety and public health. Herein, a fluorescence immunoassay for trace L. monocytogenes detection was designed based on guinea pig antibody-functionalized magnetic nanoparticles (Fe₃O₄ NPs/pAb1) and rabbit antibody-anchored CdZnTe quantum dots (CdZnTe QDs/pAb2). Because of the antibody-directed magnetic separation and long-wave fluorescent emission for CdZnTe QD indication, the constructed immunoassay strategy presented excellent anti-interference performance toward a biological matrix. The immunosensor exhibited a wide detection range of 1 to 10⁹ CFU mL^-1 for L. monocytogenes and a low limit of detection (LOD) of 1 CFU mL^-1, achieving an exceptionally sensitive detection of trace L. monocytogenes. Meanwhile, the immunosensor showed good specificity and had a short time-consumption of 60 min to realize the accurate determination of trace Listeria monocytogenes in spiked tap water and pasteurized milk samples.

Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis

Images of laser scattering patterns generated by bacteria in urine are promising resources for deep learning. However, floating bacteria in urine produce dynamic scattering patterns and require deep learning of spatial and temporal features. We hypothesized that bacteria with variable bacterial densities and different Gram staining reactions would generate different speckle images. After deep learning of speckle patterns generated by various densities of bacteria in artificial urine, we validated the model in an independent set of clinical urine samples in a tertiary hospital. Even at a low bacterial density cutoff (1,000 CFU/mL), the model achieved a predictive accuracy of 90.9% for positive urine culture. At a cutoff of 50,000 CFU/mL, it showed a better accuracy of 98.5%. The model achieved satisfactory accuracy at both cutoff levels for predicting the Gram staining reaction. Considering only 30 min of analysis, our method appears as a new screening tool for predicting the presence of bacteria before urine culture. IMPORTANCE This study performed deep learning of multiple laser scattering patterns by the bacteria in urine to predict positive urine culture. Conventional urine analyzers have limited performance in identifying bacteria in urine. This novel method showed a satisfactory accuracy taking only 30 min of analysis without conventional urine culture. It was also developed to predict the Gram staining reaction of the bacteria. It can be used as a standalone screening tool for urinary tract infection.

Multimodal Handheld Probe for Characterizing Otitis Media - Integrating Raman Spectroscopy and Optical Coherence Tomography

Otitis media (OM) is a common disease of the middle ear, affecting 80% of children before the age of three. The otoscope, a simple illuminated magnifier, is the standard clinical diagnostic tool to observe the middle ear. However, it has limited contrast to detect signs of infection, such as clearly identifying and characterizing middle ear fluid or biofilms that accumulate within the middle ear. Likewise, invasive sampling of every subject is not clinically indicated nor practical. Thus, collecting accurate noninvasive diagnostic factors is vital for clinicians to deliver a precise diagnosis and effective treatment regimen. To address this need, a combined benchtop Raman spectroscopy (RS) and optical coherence tomography (OCT) system was developed. Together, RS-OCT can non-invasively interrogate the structural and biochemical signatures of the middle ear under normal and infected conditions.In this paper, in vivo RS scans from pediatric clinical human subjects presenting with OM were evaluated in parallel with RS-OCT data of physiologically relevant in vitro ear models. Component-level characterization of a healthy tympanic membrane and malleus bone, as well as OM-related middle ear fluid, identified the optimal position within the ear for RS-OCT data collection. To address the design challenges in developing a system specific to clinical use, a prototype non-contact multimodal handheld probe was built and successfully tested in vitro. Design criteria have been developed to successfully address imaging constraints imposed by physiological characteristics of the ear and optical safety limits. Here, we present the pathway for translation of RS-OCT for non-invasive detection of OM.

Reducing Biofilm Infections in Burn Patients' Wounds and Biofilms on Surfaces in Hospitals, Medical Facilities and Medical Equipment to Improve Burn Care: A Systematic Review

Biofilms in burns are major problems: bacterial communities rapidly develop antibiotic resistance, and 60% of burn mortality is attributed to biofilms. Key pathogens are Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and multidrug-resistant Acinetobacter baumanii. Purpose: identify current and novel interventions to reduce biofilms on patients' burns and hospital surfaces and equipment. Medline and Embase were searched without date or language limits, and 31 possible interventions were prioritised: phages, nano-silver, AgSD-NLs@Cur, Acticoat and Mepilex silver, acetic acid, graphene-metal combinations, CuCo2SO4 nanoparticles, Chlorhexidene acetate nanoemulsion, a hydrogel with moxifloxacin, carbomer, Chitosan and Boswellia, LED light therapy with nano-emodin or antimicrobial blue light + Carvacrol to release reactive oxygen species, mannosidase + trypsin, NCK-10 (a napthalene compound with a decyl chain), antimicrobial peptide PV3 (includes two snake venoms), and polypeptides P03 and PL2. Most interventions aimed to penetrate cell membranes and reported significant reductions in biofilms in cfu/mL or biofilm mass or antibiotic minimal inhibitory concentrations or bacterial expression of virulence or quorum sensing genes. Scanning electron microscopy identified important changes in bacterial surfaces. Patients with biofilms need isolating and treating before full admission to hospital. Cleaning and disinfecting needs to include identifying biofilms on keyboards, tablets, cell phones, medical equipment (especially endoscopes), sinks, drains, and kitchens.

Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis

In recent years, bioanalytical surface-enhanced Raman spectroscopy (SERS) has blossomed into a fast-growing research area. Owing to its high sensitivity and outstanding multiplexing ability, SERS is an effective analytical technique that has excellent potential in bioanalysis and diagnosis, as demonstrated by its increasing applications in vivo. SERS allows the rapid detection of molecular species based on direct and indirect strategies. Because it benefits from the tunable surface properties of nanostructures, it finds a broad range of applications with clinical relevance, such as biological sensing, drug delivery and live cell imaging assays. Of particular interest are early-stage-cancer detection and the fast detection of pathogens. Here, we present a comprehensive survey of SERS-based assays, from basic considerations to bioanalytical applications. Our main focus is on SERS-based pathogen detection methods as point-of-care solutions for early bacterial infection detection and chronic disease diagnosis. Additionally, various promising in vivo applications of SERS are surveyed. Furthermore, we provide a brief outlook of recent endeavours and we discuss future prospects and limitations for SERS, as a reliable approach for rapid and sensitive bioanalysis and diagnosis.

Combination of Direct Viable Count and Fluorescent In Situ Hybridization (DVC-FISH) as a Potential Method for Identifying Viable Vibrio parahaemolyticus in Oysters and Mussels

Vibrio parahaemolyticus is a human food-borne pathogen with the ability to enter the food chain. It is able to acquire a viable, non-cultivable state (VBNC), which is not detected by traditional methods. The combination of the direct viable count method and a fluorescent in situ hybridization technique (DVC-FISH) makes it possible to detect microorganisms that can present VBNC forms in complex samples The optimization of the in vitro DVC-FISH technique for V. parahaemolyticus was carried out. The selected antibiotic was ciprofloxacin at a concentration of 0.75 μg/mL with an incubation time in DVC broth of 5 h. The DVC-FISH technique and the traditional plate culture were applied to detect and quantify the viable cells of the affected pathogen in artificially contaminated food matrices at different temperatures. The results obtained showed that low temperatures produced an important logarithmic decrease of V. parahaemolyticus, while at 22 °C, it proliferated rapidly. The DVC-FISH technique proved to be a useful tool for the detection and quantification of V. parahaemolyticus in the two seafood matrices of oysters and mussels. This is the first study in which this technique has been developed to detect viable cells for this microorganism.

Optical multi-channel interrogation instrument for bacterial colony characterization

A single instrument that includes multiple optical channels was developed to simultaneously measure various optical and associated biophysical characteristics of a bacterial colony. The multi-channel device can provide five distinct optical features without the need to transfer the sample to multiple locations or instruments. The available measurement channels are bright-field light microscopy, 3-D colony-morphology map, 2-D spatial optical-density distribution, spectral forward-scattering pattern, and spectral optical density. The series of multiple morphological interrogations is beneficial in understanding the bio-optical features of a bacterial colony and the correlations among them, resulting in an enhanced power of phenotypic bacterial discrimination. To enable a one-shot interrogation, a confocal laser scanning module was built as an add-on to an upright microscope. Three different-wavelength diode lasers were used for the spectral analysis, and high-speed pin photodiodes and CMOS sensors were utilized as detectors to measure the spectral OD and light-scatter pattern. The proposed instrument and algorithms were evaluated with four bacterial genera, Escherichia coli, Listeria innocua, Salmonella Typhimurium, and Staphylococcus aureus; their resulting data provided a more complete picture of the optical characterization of bacterial colonies.

Using elastic scattering to determination of diseases via urine samples

<abstract> <p>In diagnosing the urinary tract and related diseases, the problem of light scattering from human urine was examined on the basis of classical electromagnetic theory. Numerical calculations were made for the designed cylindrical model with the help of optical parameters in the literature obtained from the laboratory test results of urine samples. In the designed model, the changes of the scattered intensity of the light from the urine solution according to the size parameter of the particles and the angular distribution of the system (including forward, side and back scattering) in the equatorial plane were obtained, in both transverse electric (TE) and transverse magnetic (TM) of the polarization states of the light. It was observed that the molecular density changes caused by the materials in the urine sample changed primarily the optical parameters and indirectly the intensity distribution of the scattered light. Thus, with the contribution of standard data provided as a result of light scatter calculations from urine samples taken from people with normal and different diseases, it will be easier to diagnose diseases that will be encountered later.</p> </abstract>