Biosensor and Lab-on-a-chip Biomarker-identifying Technologies for Oral and Periodontal Diseases

Engineering models of head and neck and oral cancers on-a-chip

Head and neck cancers (HNCs) rank as the sixth most common cancer globally and result in over 450 000 deaths annually. Despite considerable advancements in diagnostics and treatment, the 5-year survival rate for most types of HNCs remains below 50%. Poor prognoses are often attributed to tumor heterogeneity, drug resistance, and immunosuppression. These characteristics are difficult to replicate using in vitro or in vivo models, culminating in few effective approaches for early detection and therapeutic drug development. Organs-on-a-chip offer a promising avenue for studying HNCs, serving as microphysiological models that closely recapitulate the complexities of biological tissues within highly controllable microfluidic platforms. Such systems have gained interest as advanced experimental tools to investigate human pathophysiology and assess therapeutic efficacy, providing a deeper understanding of cancer pathophysiology. This review outlines current challenges and opportunities in replicating HNCs within microphysiological systems, focusing on mimicking the soft, glandular, and hard tissues of the head and neck. We further delve into the major applications of organ-on-a-chip models for HNCs, including fundamental research, drug discovery, translational approaches, and personalized medicine. This review emphasizes the integration of organs-on-a-chip into the repertoire of biological model systems available to researchers. This integration enables the exploration of unique aspects of HNCs, thereby accelerating discoveries with the potential to improve outcomes for HNC patients.

Green Synthesis of Bacopa monnieri-Mediated Magnesium Oxide Nanoparticles and Analysis of Their Antimicrobial, Antioxidant, and Cytotoxic Properties

Background The management of aggressive forms of periodontal disease has become an issue of concern due to the emergence of bacterial resistance. Nanoparticles (NPs) have emerged as a potential therapeutic agent with a multitude of biological functions. The green synthesis of these NPs is more eco-friendly than conventional methods. The present study aimed at the green synthesis of magnesium oxide nanoparticles using Bacopa monnieri (bMgO NPs) and its antibacterial, antioxidant, and cytotoxic analysis. Materials and methods Magnesium oxide NPs were green synthesized using B. monnieri extract using a wet chemical method. The resultant bMgO NPs were assessed for antibacterial activity against Staphylococcus aureus and Escherichia coli. Antioxidant activity was assessed using the 1,1-diphenyl-2-picrylhydrazyl (DPPH) assay and the hydrogen peroxide (H2O2) assay. Cytotoxicity was assessed using zebrafish viability on treatment with bMgO NPs. Results Compared to the antibiotic standard, the green synthesized bMgO NPs showed good antibacterial properties against S. aureus and E. coli. It also showed excellent antioxidant activity and biocompatibility. Conclusion The bMgO NPs have great potential as a local drug delivery agent and should be further explored for their antibacterial and antioxidant properties in vivo.

Integrated analysis of potential biomarkers associated with diabetic periodontitis development based on bioinformatics: An observational study

Based on the importance of chronic inflammation in the pathogenesis of periodontitis and diabetes, the bidirectional relationship between these 2 diseases has been widely confirmed. However, the molecular mechanisms of bidirectional relationship still need to be studied further. In this study, gene expression profile data for diabetes and periodontitis were obtained from Gene Expression Omnibus (GEO) database. Integrative analytical platform were constructed, including common differentially expressed genes (cDEGs), Gene Ontology-Kyoto Encyclopedia of Genes and Genomes (GO-KEGG), and protein-protein interaction. Hub genes and essential modules were detected via Cytoscape. Key hub genes and signaling pathway that mediate chronic inflammation were validated by qPCR and Western blot. Eleven cDEGs were identified. Function analysis showed that cDEGs plays an important role in inflammatory response, cytokine receptor binding, TNF signaling pathway. As hub genes, CXCR4, IL1B, IL6, CXCL2, and MMP9 were detected based on the protein-protein interactions network. IL1B, CXCR4 mRNA were up-regulated in gingivitis samples compared with normal tissues (P < .05). Western blot indicated that the levels of TNF were enhanced in gingivitis of type 2 diabetes compared with normal tissues (P < .01). Hub gene and TNF signaling pathway are helpful to elucidate the molecular mechanism of the bidirectional relationship between periodontitis and diabetes.

Impact of peri-implantitis on the proteome biology of crevicular fluid: A pilot study

BACKGROUND

The proteome of the peri-implant crevicular fluid (PICF) has not been systematically investigated. The aim of the present study was to reveal the proteome biology of dental implants affected with peri-implantitis.

METHODS

Patients with at least one diseased implant were included (probing depth ≥6 mm, ≥3 mm peri-implant radiological bone loss). Using sterile paper strips, samples were collected from healthy implants (I), healthy teeth (T) and peri-implantitis affected implants (P). Proteome analysis was performed using liquid chromatography - tandem mass spectrometry (LC-MS/MS) and data independent acquisition, allowing the identification and quantification of human and bacterial proteins as well as semi-specific peptides.

RESULTS

A total of 38 samples from 14 patients were included in the study; 2332 different human proteins were identified across all samples. No differentially expressed proteins between T and I were found. Comparing P to I, 59 proteins were found upregulated and 31 downregulated in P with significance. Upregulated proteins included proinflammatory proteins such as immunoglobulins, dysferlin, and S100P, as well as antimicrobial proteins, for example, myeloperoxidase or azurocidin. Gene ontology analysis further revealed higher activity of immunological pathways. Proteolytic patterns indicated the activity of inflammatory proteins such as cathepsin G. A total of 334 bacterial proteins were identified and quantified. Peri-implantitis showed elevated proteolytic activity.

CONCLUSION

I and T share similarities in their proteome, while diseased implants deviate strongly from healthy conditions. The PICF proteome of peri-implantitis affected sites exhibits an inflammatory fingerprint, dominated by neutrophil activity when compared with healthy implants.

Effect of laser-assisted reconstructive surgical therapy of peri-implantitis on protein biomarkers and bacterial load

OBJECTIVES

This randomized clinical trial assessed changes in protein biomarker levels and bacterial profiles after surgical reconstructive therapy of peri-implantitis and investigated whether the adjunctive use of Er:YAG laser impacts protein biomarker and microbial outcomes.

MATERIALS AND METHODS

Twenty-four patients received surgical reconstructive therapy for peri-implantitis with guided bone regeneration following mechanical debridement with (test) or without (control) the adjunctive irradiation of Er:YAG laser. Bacterial and peri-implant crevicular fluid (PICF) samples were collected over 6 months and analyzed with bacterial qPCR and luminex multiplex assays.

RESULTS

Surgical reconstructive treatment significantly affected the concentration of PICF protein biomarkers, including a 50% reduction in IL-1β between 2 and 4 weeks (p < .0001). Both MMP-9 (p < .001) and VEGF (p < .05) levels steadily decreased after treatment. In the laser group, the peak increase in IL-1β was attenuated at 2 weeks, followed by significant reduction in MMP-9 (p < .01) and VEGF (p < .05) across all follow-up appointments compared with the control nonlaser group. The total bacterial load was reduced 2 weeks after treatment, especially in the laser group, but recolonized to presurgical levels after 4 weeks in both groups (p < .01). The composition of selective pathogens varied significantly over the follow-up, but recolonization patterns did not differ between groups.

CONCLUSIONS

Reconstructive therapy of peri-implantitis significantly altered PICF protein biomarker and microbial levels during the healing process. The adjunctive use of Er:YAG laser significantly modulated the inflammatory response through reduced levels of MMP-9 and VEGF during the postsurgical period. The bacterial load was reduced immediately after therapy, but recolonization was observed by 4 weeks in both groups.

Use of bioinformatic strategies as a predictive tool in implant-supported oral rehabilitation: A scoping review

STATEMENT OF PROBLEM

The use of bioinformatic strategies is growing in dental implant protocols. The current expansion of Omics sciences and artificial intelligence (AI) algorithms in implant dentistry applications have not been documented and analyzed as a predictive tool for the success of dental implants.

PURPOSE

The purpose of this scoping review was to analyze how artificial intelligence algorithms and Omics technologies are being applied in the field of oral implantology as a predictive tool for dental implant success.

MATERIAL AND METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist was followed. A search strategy was created at PubMed and Web of Science to answer the question "How is bioinformatics being applied in the area of oral implantology as a predictive tool for implant success?"

RESULTS

Thirteen articles were included in this review. Only 3 applied bioinformatic models combining AI algorithms and Omics technologies. These studies highlighted 2 key points for the creation of precision medicine: deep population phenotyping and the integration of Omics sciences in clinical protocols. Most of the studies identified applied AI only in the identification and classification of implant systems, quantification of peri-implant bone loss, and 3-dimensional bone analysis, planning implant placement.

CONCLUSIONS

The conventional criteria currently used as a technique for the diagnosis and monitoring of dental implants are insufficient and have low accuracy. Models that apply AI algorithms combined with precision methodologies-biomarkers-are extremely useful in the creation of precision medicine, allowing medical dentists to forecast the success of the implant. Tools that integrate the different types of data, including imaging, molecular, risk factor, and implant characteristics, are needed to make a more accurate and personalized prediction of implant success.

Changes in salivary biomarkers associated with periodontitis and diabetic neuropathy in individuals with type 1 diabetes

The objective of this pilot clinical study was to identify salivary biomarkers that are associated with periodontal disease and measures of diabetic autonomic dysfunction. Saliva samples from 32 participants were obtained from 3 groups: healthy (H), type 1 diabetes mellitus (DM), and type 1 diabetes mellitus with neuropathy (DMN). Based on the periodontal examination, individuals’ mean Periodontal Screening and Recording scores were categorized into two groups (periodontally healthy and gingivitis), and correlated to specific salivary inflammatory biomarkers assessed by a customized protein array and enzyme assay. The mean salivary IgA level in DM was 9211.5 ± 4776.4 pg/ml, which was significantly lower than H (17,182.2 ± 8899.3 pg/ml). IgA in DMN with healthy periodontium was significantly lower (5905.5 ± 3124.8 pg/ml) compared to H, although IgA levels in DMN patients with gingivitis (16,894. 6 ± 7084.3) were not. According to the result of a logistic regression model, IgA and periodontal condition were the indicators of the binary response given by H versus DM, and H versus DMN, respectively. These data suggest that selected salivary biomarkers, such as IgA, combined with a periodontal examination prior to obtaining salivary samples can offer a non-invasive method to assess risk for developing diabetic neuropathy.

Cost-effective and disposable label-free voltammetric immunosensor for sensitive detection of interleukin-6

IL-6 detection is highly desirable since can monitor many diseases in humans and assess the response to treatments. Herein, two novel label-free voltammetric immunosensors for rapid and accurate interleukin-6 (IL-6) detection in human serum are presented. The immunosensors are fabricated by immobilising two different IL-6 antibodies, identified as mAb-IL-6 clone-5 and clone-7, on in-house produced screen-printed electrodes modified with inexpensive recycling biochar (Bio-SPEs). To ensure high structural fidelity and performance, an in-depth electrochemical characterization of the layer-by-layer assembly of the immunosensor was conducted by cyclic voltammetry (CV) and sensing was performed using square wave voltammetry (SWV). The two immunosensors showed good analytical performances in human serum, exhibiting a wide linear range (LR) between 26-125 and 30-138 pg/mL, a good limit of detection (LOD) of 4.8 and 5.4 pg/mL and selectivity for IL-6 over other common cytokines, including IL-1β and TNF-α. Performance comparison of IL-6 immunosensors with those of a commercial spectrophotometric ELISA kit (LOD of 20 pg/mL, RSD% of 15%) denotes a better sensitivity and reproducibility of the proposed label-free devices, associated with a reduced detection time (30 min instead of more than 3 h for ELISA test). Furthermore, the proposed immunosensors were successfully applied in blood samples (with only a dilution of 1:100 v/v in PBS and without additional treatments) with good sensitivity (LOD of 14.3 pg/mL) and reproducibility (RSD% < 11%), thus paving the way for their application as viable diagnostic and therapeutic point-of-care tools alternative to the IL-6 detection techniques routinely used (ELISA and Western Blot).

Molecular biomarker research in periodontology: A roadmap for translation of science to clinical assay validation

The number of studies that aims to apply host‐ or microbe‐derived biochemical biomarkers to periodontal disease diagnosis has increased significantly during the last three decades. The biochemical markers can reflect the presence, severity, and activity of periodontal diseases; however, heterogeneities in applied laboratory methods, data presentation, statistical analysis, and data interpretation prevent the translation of candidate host‐ or microbe‐derived biochemical biomarkers to clinical assay validation. Here, we propose a roadmap for making the research outcomes comparable and re‐analysable with the ultimate goal of translating research to clinical practice. This roadmap presents reporting recommendations for host‐ or microbe‐derived biochemical biomarker studies in periodontology. We aim to make essential elements of the research work (including diagnostic criteria, clinical endpoint definitions, participant recruitment criteria, sample collection and storage techniques, biochemical and microbiological detection methods, and applied statistical analysis) visible and comparable.

Recent Advances of Point-of-Care Devices Integrated with Molecularly Imprinted Polymers-Based Biosensors: From Biomolecule Sensing Design to Intraoral Fluid Testing

Recent developments of point-of-care testing (POCT) and in vitro diagnostic medical devices have provided analytical capabilities and reliable diagnostic results for rapid access at or near the patient's location. Nevertheless, the challenges of reliable diagnosis still remain an important factor in actual clinical trials before on-site medical treatment and making clinical decisions. New classes of POCT devices depict precise diagnostic technologies that can detect biomarkers in biofluids such as sweat, tears, saliva or urine. The introduction of a novel molecularly imprinted polymer (MIP) system as an artificial bioreceptor for the POCT devices could be one of the emerging candidates to improve the analytical performance along with physicochemical stability when used in harsh environments. Here, we review the potential availability of MIP-based biorecognition systems as custom artificial receptors with high selectivity and chemical affinity for specific molecules. Further developments to the progress of advanced MIP technology for biomolecule recognition are introduced. Finally, to improve the POCT-based diagnostic system, we summarized the perspectives for high expandability to MIP-based periodontal diagnosis and the future directions of MIP-based biosensors as a wearable format.

Biomarkers and Periodontal Regenerative Approaches

The ultimate goal in periodontal therapy is the complete re-establishment of the lost tissues. Dental researchers and clinicians are continuously working to develop current therapeutic techniques and technologies that can regenerate damaged periodontal tissues. Predicting the outcome of the treatment is a challenging endeavor, because a variety of local and systemic variables can affect the success of the applied regenerative therapy. To real-time monitor the biological changes during periodontitis or after periodontal treatment, various biomarkers have been studied in periodontology. This article discusses the available evidence on the use of biomarkers in the detection of periodontal regeneration.

Organic Bioelectronic Devices for Metabolite Sensing

Electrochemical detection of metabolites is essential for early diagnosis and continuous monitoring of a variety of health conditions. This review focuses on organic electronic material-based metabolite sensors and highlights their potential to tackle critical challenges associated with metabolite detection. We provide an overview of the distinct classes of organic electronic materials and biorecognition units used in metabolite sensors, explain the different detection strategies developed to date, and identify the advantages and drawbacks of each technology. We then benchmark state-of-the-art organic electronic metabolite sensors by categorizing them based on their application area (in vitro, body-interfaced, in vivo, and cell-interfaced). Finally, we share our perspective on using organic bioelectronic materials for metabolite sensing and address the current challenges for the devices and progress to come.

Molecular Screening and Analysis Reveal Novel Oral Site-Specific Locations for the Cariogenic Pathogen Scardovia wiggsiae

Introduction: Scardovia wiggsiae (SW) is a newly identified cariogenic pathogen associated with severe early childhood caries and oral disease. New studies have confirmed the presence of this organism among clinical samples from both pediatric and adult patients. However, the recent discovery of this organism has left researchers with only limited information available regarding the prevalence of this organism—and virtually no information regarding oral site-specific locations. Based upon this lack of information, the overall objective of this study was to perform an oral site-specific analysis of SW prevalence from clinical samples. Methods: Using an approved human subjects protocol, samples (n = 60) from an existing saliva and site-specific biorepository were identified and screened for SW presence using quantitative polymerase chain reaction (qPCR). These data were summarized and subsequently analyzed for correlations with demographic (age, sex, race or ethnicity) or clinical (body mass index or BMI, primary/mixed/permanent dentition, orthodontic brackets) variables. Results: These data revealed that average DNA concentrations from all sample sites (saliva, dorsum of tongue, gingival crevicular fluid (GCF), biofilm of upper buccal molar, and biofilm of lower lingual incisor) ranged between 13.74 and 14.69 μg/μL, with an overall average of 14.30 μg/μL ± 1.12 (standard error or SE). qPCR screening revealed a total of n = 34/60 or 56.7% of patient samples harboring SW. A total of n = 71/170 specific oral sites harbored this organism, with the majority of the SW-positive participant samples harboring SW at more than one oral site, n = 22/34 or 64.7%, including non-traditional sites such as GCF and the dorsum of the tongue. Weak correlations were found between specific SW outcomes in GCF and type of dentition (permanent; R = 0.2444), as well as SW outcomes in saliva with age (R = 0.228) and presence of orthodontic brackets (R = 0.2118). Conclusions: This study may be among the first to provide oral site-specific analysis to reveal the prevalence and location of Scardovia among clinical patient samples. Moreover, these data also provide some of the first evidence to suggest this organism may be present not only in traditional supragingival tooth-associated biofilm sites, but also in non-traditional oral sites including the dorsum of the tongue and the gingival crevice. Based upon these results, these data may represent a significant advance in our understanding of the potential sites and locations that harbor this organism and may help contribute to our understanding of the prevalence, distribution and potential for the development of oral disease among clinic patients.

The Chairside Periodontal Diagnostic Toolkit: Past, Present, and Future

Periodontal diseases comprise a group of globally prevalent, chronic oral inflammatory conditions caused by microbial dysbiosis and the host immune response. These diseases specifically affect the tooth-supporting tissues (i.e., the periodontium) but are also known to contribute to systemic inflammation. If left untreated, periodontal diseases can ultimately progress to tooth loss, lead to compromised oral function, and negatively impact the overall quality of life. Therefore, it is important for the clinician to accurately diagnose these diseases both early and accurately chairside. Currently, the staging and grading of periodontal diseases are based on recording medical and dental histories, thorough oral examination, and multiple clinical and radiographic analyses of the periodontium. There have been numerous attempts to improve, automate, and digitize the collection of this information with varied success. Recent studies focused on the subgingival microbiome and the host immune response suggest there is an untapped potential for non-invasive oral sampling to assist clinicians in the chairside diagnosis and, potentially, prognosis. Here, we review the available toolkit available for diagnosing periodontal diseases, discuss commercially available options, and highlight the need for collaborative research initiatives and state-of-the-art technology development across disciplines to overcome the challenges of rapid periodontal disease diagnosis.