In Search of Spectroscopic Signatures of Periodontitis: A SERS-Based Magnetomicrofluidic Sensor for Detection of Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans

Microfluidic Electrochemical Integrated Sensor for Efficient and Sensitive Detection of Candida albicans

Traditional methods for the detection of pathogenic bacteria are time-consuming, less efficient, and sensitive, which affects infection control and bungles illness. Therefore, developing a method to remedy these problems is very important in the clinic to diagnose the pathogenic diseases and guide the rational use of antibiotics. Here, microfluidic electrochemical integrated sensor (MEIS) has been investigated, functionally for rapid, efficient separation and sensitive detection of pathogenic bacteria. Three-dimensional macroporous PDMS and Au nanotube-based electrode are successfully assembled into the modeling microchip, playing the functions of "3D chaotic flow separator" and "electrochemical detector," respectively. The 3D chaotic flow separator enhances the turbulence of the fluid, achieving an excellent bacteria capture efficiency. Meanwhile, the electrochemical detector provides a quantitative signal through enzyme-linked immunoelectrochemistry with improved sensitivity. The microfluidic electrochemical integrated sensor could successfully isolate Candida albicans (C. albicans) in the range of 30-3,000,000 CFU in the saliva matrix with over 95% capture efficiency and sensitively detect C. albicans in 1 h in oral saliva samples. The integrated device demonstrates great potential in the diagnosis of oral candidiasis and is also applicable in the detection of other pathogenic bacteria.

SERS-based microdevices for use as in vitro diagnostic biosensors

Advances in surface-enhanced Raman scattering (SERS) detection have helped to overcome the limitations of traditional in vitro diagnostic methods, such as fluorescence and chemiluminescence, owing to its high sensitivity and multiplex detection capability. However, for the implementation of SERS detection technology in disease diagnosis, a SERS-based assay platform capable of analyzing clinical samples is essential. Moreover, infectious diseases like COVID-19 require the development of point-of-care (POC) diagnostic technologies that can rapidly and accurately determine infection status. As an effective assay platform, SERS-based bioassays utilize SERS nanotags labeled with protein or DNA receptors on Au or Ag nanoparticles, serving as highly sensitive optical probes. Additionally, a microdevice is necessary as an interface between the target biomolecules and SERS nanotags. This review aims to introduce various microdevices developed for SERS detection, available for POC diagnostics, including LFA strips, microfluidic chips, and microarray chips. Furthermore, the article presents research findings reported in the last 20 years for the SERS-based bioassay of various diseases, such as cancer, cardiovascular diseases, and infectious diseases. Finally, the prospects of SERS bioassays are discussed concerning the integration of SERS-based microdevices and portable Raman readers into POC systems, along with the utilization of artificial intelligence technology.

Recent advances in nanomaterial-based biosensor for periodontitis detection

Periodontitis, a chronic inflammatory condition caused by bacteria, often causes gradual destruction of the components that support teeth, such as the alveolar bone, cementum, periodontal ligament, and gingiva. This ultimately results in teeth becoming loose and eventually falling out. Timely identification has a crucial role in preventing and controlling its progression. Clinical measures are used to diagnose periodontitis. However, now, there is a hunt for alternative diagnostic and monitoring methods due to the progress of technology. Various biomarkers have been assessed using multiple bodily fluids as sample sources. Furthermore, conventional periodontal categorization factors do not provide significant insights into the present disease activity, severity and amount of tissue damage, future development, and responsiveness to treatment. In recent times, there has been a growing utilization of nanoparticle (NP)-based detection strategies to create quick and efficient detection assays. Every single one of these platforms leverages the distinct characteristics of NPs to identify periodontitis. Plasmonic NPs include metal NPs, quantum dots (QDs), carbon base NPs, and nanozymes, exceptionally potent light absorbers and scatterers. These find application in labeling, surface-enhanced spectroscopy, and color-changing sensors. Fluorescent NPs function as photostable and sensitive instruments capable of labeling various biological targets. This article presents a comprehensive summary of the latest developments in the effective utilization of various NPs to detect periodontitis.

Raman spectroscopy-based microfluidic platforms: A promising tool for detection of foodborne pathogens in food products

Rapid and sensitive detection of foodborne pathogens in food products is paramount for ensuring food safety and public health. In the ongoing effort to tackle this issue, detection methods are continually researched and upgraded to achieve rapidity, sensitivity, portability, and cost-effectiveness. This review addresses the critical need for improved technique by focusing on Raman spectroscopy-based microfluidic platforms, which have shown potential in revolutionizing the field of foodborne pathogen analysis offering point-of-care diagnosis and multiplex detection. The key problem lies in the persistent threat of compromised food quality and public health due to inadequate pathogen detection. The review elucidates the various trapping strategies employed in a microfluidic platform, including optical trapping, electrical trapping, mechanical trapping, and acoustic trapping for the capture of microbial cells. Subsequently, the review delves into the key aspects of the application of microbial detection in food products, highlighting recent advances and challenges in the field. The integrated technique allows point-of-care application assessment, which is an attractive quality for in-line and real-time detection of foodborne pathogens. However, the application of the technique in food products is limited and requires further research to combat the complexity of the food matrix, reduced costs of production, and ensure real-time use for diverse pathogens. Ultimately, this review aims to propel advancements in microbial detection, thus promoting enhanced food safety through state-of-the-art technologies.

"Magnet" Based on Activated Silver Nanoparticles Adsorbed Bacteria to Predict Refractory Apical Periodontitis Via Surface-Enhanced Raman Scattering

Refractory apical periodontitis (RAP) is an endodontic apical inflammatory disease caused by Enterococcus faecalis (E. faecalis). Bacterial detection using surface-enhanced Raman scattering (SERS) technology is a hot research topic, but the specific and direct detection of oral bacteria is a challenge, especially in real clinical samples. In this paper, we develop a novel SERS-based green platform for label-free detection of oral bacteria. The platform was built on silver nanoparticles with a two-step enhancement way using NaBH4 and sodium (Na+) to form "hot spots," which resulted in an enhanced SERS fingerprint of E. faecalis with fast, quantitative, lower-limit, reproducibility, and stability. In combination with machine learning, four different oral bacteria (E. faecalis, Porphyromonas gingivalis, Streptococcus mutans, and Escherichia coli) could be intelligently distinguished. The unlabeled detection method emphasized the specificity of E. faecalis in simulated saliva, serum, and even real samples from patients with clinical root periapical disease. In addition, the assay has been shown to be environmentally friendly and without secondary contamination through antimicrobial assays. The proposed label-free, rapid, safe, and green SERS detection strategy for oral bacteria provided an innovative solution for the early diagnosis and prevention of RAP and other perioral diseases.

Machine learning enabled multiplex detection of periodontal pathogens by surface-enhanced Raman spectroscopy

Periodontitis is a chronic inflammation of the periodontium caused by a persistent bacterial infection, resulting in destruction of the supporting structures of teeth. Analysis of microbial composition in saliva can inform periodontal status. Actinobacillus actinomycetemcomitans (Aa), Porphyromonas gingivalis (Pg), and Streptococcus mutans (Sm) are among reported periodontal pathogens, and were used as model systems in this study. Our atomic force microscopic (AFM) study revealed that these pathogens are biological nanorods with dimensions of 0.6-1.1 μm in length and 500-700 nm in width. Current bacterial detection methods often involve complex preparation steps and require labeled reporting motifs. Employing surface-enhanced Raman spectroscopy (SERS), we revealed cell-type specific Raman signatures of these pathogens for label-free detection. It overcame the complexity associated with spectral overlaps among different bacterial species, relying on high signal-to-noise ratio (SNR) spectra carefully collected from pure species samples. To enable simple, rapid, and multiplexed detection, we harnessed advanced machine learning techniques to establish predictive models based on a large set of raw spectra of each bacterial species and their mixtures. Using these models, given a raw spectrum collected from a bacterial suspension, simultaneous identification of all three species in the test sample was achieved at 95.6 % accuracy. This sensing modality can be applied to multiplex detection of a broader range and a larger set of periodontal pathogens, paving the way for hassle-free detection of oral bacteria in saliva with little to no sample preparation.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

Omics for deciphering oral microecology

The human oral microbiome harbors one of the most diverse microbial communities in the human body, playing critical roles in oral and systemic health. Recent technological innovations are propelling the characterization and manipulation of oral microbiota. High-throughput sequencing enables comprehensive taxonomic and functional profiling of oral microbiomes. New long-read platforms improve genome assembly from complex samples. Single-cell genomics provides insights into uncultured taxa. Advanced imaging modalities including fluorescence, mass spectrometry, and Raman spectroscopy have enabled the visualization of the spatial organization and interactions of oral microbes with increasing resolution. Fluorescence techniques link phylogenetic identity with localization. Mass spectrometry imaging reveals metabolic niches and activities while Raman spectroscopy generates rapid biomolecular fingerprints for classification. Culturomics facilitates the isolation and cultivation of novel fastidious oral taxa using high-throughput approaches. Ongoing integration of these technologies holds the promise of transforming our understanding of oral microbiome assembly, gene expression, metabolites, microenvironments, virulence mechanisms, and microbe-host interfaces in the context of health and disease. However, significant knowledge gaps persist regarding community origins, developmental trajectories, homeostasis versus dysbiosis triggers, functional biomarkers, and strategies to deliberately reshape the oral microbiome for therapeutic benefit. The convergence of sequencing, imaging, cultureomics, synthetic systems, and biomimetic models will provide unprecedented insights into the oral microbiome and offer opportunities to predict, prevent, diagnose, and treat associated oral diseases.

The role of periodontitis-associated bacteria in Alzheimer's disease: A narrative review

Alzheimer's disease causes memory loss and dementia in older adults through a neurodegenerative mechanism. Despite the pathophysiological clarification of this cognitive disorder, novel molecular and cellular pathways should be identified to determine its exact mechanism. Alzheimer's disease (AD) is pathologically characterized by senile plaques comprising beta-amyloid and neurofibrillary tangles (NFTs) formed by hyperphosphorylated tau as a microtubule-associated protein with a key role in the pathogenesis of AD. Periodontitis through inflammatory pathways is a risk factor for deteriorating cognitive impairment in AD patients. Poor oral hygiene coupled with immunocompromised status in older adults causes periodontal diseases and chronic inflammations through an oral bacterial imbalance. Toxic bacterial products, including bacteria themselves, can reach the central nervous system through the bloodstream and evoke inflammatory responses. The present review was conducted to investigate relationships between AD and periodontitis-involved bacteria as a risk factor.

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 on Solid Substrates for Surface-Enhanced Raman Spectroscopy Analysis

Surface-enhanced Raman spectroscopy (SERS) is a powerful vibrational spectroscopy technique with distinguished features of non-destructivity, ultra-sensitivity, rapidity, and fingerprint characteristics for analysis and sensors. The SERS signals are mainly dependent on the engineering of high-quality substrates. Recently, solid SERS substrates with diverse forms have been attracting increasing attention due to their promising features, including dense hot spot, high stability, controllable morphology, and convenient portability. Here, we comprehensively review the recent advances made in the field of solid SERS substrates, including their common fabrication methods, basic categories, main features, and representative applications, respectively. Firstly, the main categories of solid SERS substrates, mainly including membrane substrate, self-assembled substrate, chip substrate, magnetic solid substrate, and other solid substrate, are introduced in detail, as well as corresponding construction strategies and main features. Secondly, the typical applications of solid SERS substrates in bio-analysis, food safety analysis, environment analysis, and other analyses are briefly reviewed. Finally, the challenges and perspectives of solid SERS substrates, including analytical performance improvement and largescale production level enhancement, are proposed.

Raman Spectroscopy: A Potential Diagnostic Tool for Oral Diseases

Oral diseases impose a major health burden worldwide and have a profound effect on general health. Dental caries, periodontal diseases, and oral cancers are the most common oral health conditions. Their occurrence and development are related to oral microbes, and effective measures for their prevention and the promotion of oral health are urgently needed. Raman spectroscopy detects molecular vibration information by collecting inelastic scattering light, allowing a “fingerprint” of a sample to be acquired. It provides the advantages of rapid, sensitive, accurate, and minimally invasive detection as well as minimal interference from water in the “fingerprint region.” Owing to these characteristics, Raman spectroscopy has been used in medical detection in various fields to assist diagnosis and evaluate prognosis, such as detecting and differentiating between bacteria or between neoplastic and normal brain tissues. Many oral diseases are related to oral microbial dysbiosis, and their lesions differ from normal tissues in essential components. The colonization of keystone pathogens, such as Porphyromonas gingivalis, resulting in microbial dysbiosis in subgingival plaque, is the main cause of periodontitis. Moreover, the components in gingival crevicular fluid, such as infiltrating inflammatory cells and tissue degradation products, are markedly different between individuals with and without periodontitis. Regarding dental caries, the compositions of decayed teeth are transformed, accompanied by an increase in acid-producing bacteria. In oral cancers, the compositions and structures of lesions and normal tissues are different. Thus, the changes in bacteria and the components of saliva and tissue can be used in examinations as special markers for these oral diseases, and Raman spectroscopy has been acknowledged as a promising measure for detecting these markers. This review summarizes and discusses key research and remaining problems in this area. Based on this, suggestions for further study are proposed.