Prediction of Chronic Periodontitis Severity Using Machine Learning Models Based On Salivary Bacterial Copy Number

Applications of artificial intelligence in regenerative dentistry: promoting stem cell therapy and the scaffold development

Tissue repair represents a critical concern within the domain of dentistry. On a daily basis, countless individuals seek dental clinic services due to inadequate dental care. Many of the treatments that patients receive have unfavorable side effects. The employment of innovative methodologies, including gene therapy, tissue engineering, and stem cell (SCs) applications for regenerative purposes, has garnered significant interest over the past years. In recent times, artificial intelligence, particularly neural networks, has emerged as a topic of considerable attention among many medical professionals. Artificial intelligence possesses the capability to analyze data patterns through learning algorithms. Research opportunities in the rapidly expanding field of health sciences have been made possible by the use of artificial intelligence (AI) technologies. Though its uses are not restricted to these situations, artificial intelligence (AI) has the potential to improve and accelerate many aspects of regenerative medicine research and development, especially when working with complicated patterns. This review article is to investigate how artificial intelligence might be used to enhance regenerative processes in dentistry by using scaffolds and stem cells, in light of the continuous advances in artificial intelligence in the fields of medicine and tissue regeneration. It highlights the difficulties that still exist in this developing sector and explores the possible uses of AI with a particular emphasis on dentistry practices.

Classification of periodontitis stage and grade using natural language processing techniques

Periodontitis is a complex and microbiome-related inflammatory condition impacting dental supporting tissues. Emphasizing the potential of Clinical Decision Support Systems (CDSS), this study aims to facilitate early diagnosis of periodontitis by extracting patients' information collected as dental charts and notes. We developed a CDSS to predict the stage and grade of periodontitis using natural language processing (NLP) techniques including bidirectional encoder representation for transformers (BERT). We compared the performance of BERT with that of a baseline feature-engineered model. A secondary data analysis was conducted using 309 anonymized patient periodontal charts and corresponding clinician's notes obtained from the university periodontal clinic. After data preprocessing, we added a classification layer on top of the pre-trained BERT model to classify the clinical notes into their corresponding stage and grades. Then, we fine-tuned the pre-trained BERT model on 70% of our data. The performance of the model was evaluated on 32 unseen new patients' clinical notes. The results were compared with the output of a baseline feature-engineered algorithm coupled with MLP techniques to classify the stage and grade of periodontitis. Our proposed BERT model predicted the patients' stage and grade with 77% and 75% accuracy, respectively. MLP model showed that the accuracy of correct classification of stage and grade of the periodontitis on a set of 32 new unseen data was 59.4% and 62.5%, respectively. The BERT model could predict the periodontitis stage and grade on the same new dataset with higher accuracy (66% and 72%, respectively). The utilization of BERT in this context represents a groundbreaking application in dentistry, particularly in CDSS. Our BERT model outperformed baseline models, even with reduced information, promising efficient review of patient notes. This integration of advanced NLP techniques with CDSS frameworks holds potential for timely interventions, preventing complications and reducing healthcare costs.

Embracing technological revolution: A panorama of machine learning in dentistry

BACKGROUND

The overarching aim of this study is to furnish dental experts and researchers with a comprehensive understanding of the role of machine learning in dentistry. This entails a nuanced understanding of prevailing technologies, discerning emerging trends, and providing strategic guidance for future research endeavors and practical implementations.

MATERIAL AND METHODS

We assessed the literature by looking for papers related to the issue after 2019 in the Pubmed, Web of Science, and Google Scholar databases. A narrative review of 29 papers satisfying the search criteria was undertaken, with an emphasis on the application of machine learning in dentistry.

RESULTS

A review was conducted, including 29 publications. The advent of emerging technologies holds promise for enhancing the accuracy and efficiency of dental diagnosis, treatment, and prognosis. Nevertheless, the intricate nature of oral disease diagnosis and outcome prediction mandates acknowledgment of variables such as individual idiosyncrasies, lifestyle, genetics, image quality, and tooth morphology. These factors may impact the precision of machine learning models. Dental professionals should not rely solely on AI-based results but rather use them as references. Integrating these findings with clinical examinations, assessing the patient's overall health, and oral condition is crucial for informed decision-making.

CONCLUSIONS

This review explores the clinical applications of machine learning in dentistry, encompassing disciplines like cariology, endodontics, periodontology, oral medicine, oral and maxillofacial surgery, prosthodontics and orthodontics. It serves as a valuable resource for dental practitioners and scholars in understanding the computer algorithms employed in each study, facilitating the clinical translation of machine learning research outcomes.

Exploring the interplay between Porphyromonas gingivalis KGP gingipain, herpes virus MicroRNA-6, and Icp4 transcript in periodontitis: Computational and clinical insights

BACKGROUND

Porphyromonas gingivalis, a major pathogen in periodontitis, produces KGP (Lys-gingipain), a cysteine protease that enhances bacterial virulence by promoting tissue invasion and immune evasion. Recent studies highlight microRNAs' role in viral latency, potentially affecting lytic replication through host mechanisms. Herpes virus (HSV) establishes latency via interactions between microRNA-6 (miRH-6) and the ICP4 transcription factor in neural ganglia. This suggests a potential link between periodontitis and HSV-induced latency. This study aims to identify and validate the insilico inhibitory interaction of P. gingivalis KGP with ICP4 transcripts and correlate the presence of viral latency-associated transcript micro-RNA-6 with periodontitis.

METHODS

Computational docking analysis was performed to investigate the potential interaction between ICP4 and KGP gingipain. The binding energy and RMSD ligand values were calculated to determine the interaction's strength. Ten patients with recurrent clinical attachment loss despite conventional therapy were included in the clinical study. Subgingival tissue samples were collected post-phase I therapy, and HSV microRNA-6 presence was detected via polymerase chain reaction and confirmed through gel electrophoresis.

RESULTS

Computational docking identified the ICP4-KGP gingipain complex with the lowest binding energy (-288.29 kJ mol^1) and an RMSD ligand of 1.5 Angstroms, indicating strong interaction potential. Gel electrophoresis confirmed miRH-6 presence in all samples.

CONCLUSION

The identification of miRNA-6 in periodontitis patients and the strong interaction potential between P. gingivalis KGP gingipain and ICP4 transcripts indicate a possible link between bacterial virulence factors and viral latency dynamics in periodontal tissues. These results highlight the complex interplay between oral pathogens, viral microRNAs, and host immune responses in periodontitis.

Anomaly detection of retention loss in fixed partial dentures using resonance frequency analysis and machine learning: An in vitro study

PURPOSE

This study aimed to determine the usefulness of machine learning techniques, specifically supervised and unsupervised learning, for assessing the cementation condition between a fixed partial denture (FPD) and its abutment using a resonance frequency analysis (RFA) system.

METHODS

An in vitro mandibular model was used with a single crown and three-unit bridge made of a high-gold alloy. Two cementation conditions for the single crown and its abutment were set: cemented and uncemented. Four cementation conditions were set for the bridge and abutments: both crowns were firmly cemented, only the premolar crown was cemented, only the molar crown was cemented, and both crowns were uncemented. For RFA under cementation conditions, 16 impulsive forces were directly applied to the buccal side of the tested tooth at a frequency of 4 Hz using a Periotest device. Frequency responses were measured using a 3D accelerometer mounted on the occlusal surface of the tested tooth. Both supervised and unsupervised learning methods were used to analyze the datasets.

RESULTS

Using supervised learning, the fully cemented condition had the highest feature importance scores at approximately 3000 Hz; the partially cemented condition had the highest scores between 1000 and 2000 Hz; and the highest scores for the uncemented condition were observed between 0 and 500 Hz. Using unsupervised learning, the uncemented and partially cemented conditions exhibited the highest anomaly scores.

CONCLUSIONS

Machine learning combined with RFA exhibits good potential to assess the cementation condition of an FPD and hence facilitate the early diagnosis of FPD retention loss.

Accuracy of Artificial Intelligence Models in the Prediction of Periodontitis: A Systematic Review

INTRODUCTION

Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment planning for patients with periodontitis. This systematic review aims to examine the actual evidence on the accuracy of various AI models in predicting periodontitis.

METHODS

Using a mix of MeSH keywords and free text words pooled by Boolean operators ('AND', 'OR'), a search strategy without a time frame setting was conducted on the following databases: Web of Science, ProQuest, PubMed, Scopus, and IEEE Explore. The QUADAS-2 risk of bias assessment was then performed.

RESULTS

From a total of 961 identified records screened, 8 articles were included for qualitative analysis: 4 studies showed an overall low risk of bias, 2 studies an unclear risk, and the remaining 2 studies a high risk. The most employed algorithms for periodontitis prediction were artificial neural networks, followed by support vector machines, decision trees, logistic regression, and random forest. The models showed good predictive performance for periodontitis according to different evaluation metrics, but the presented methods were heterogeneous.

CONCLUSIONS

AI algorithms may improve in the future the accuracy and reliability of periodontitis prediction. However, to date, most of the studies had a retrospective design and did not consider the most modern deep learning networks. Although the available evidence is limited by a lack of standardized data collection and protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.

KNOWLEDGE TRANSFER STATEMENT

The use of AI in periodontics can lead to more accurate diagnosis and treatment planning, as well as improved patient education and engagement. Despite the current challenges and limitations of the available evidence, particularly the lack of standardized data collection and analysis protocols, the potential benefits of using AI in periodontics are significant and warrant further research and development in this area.

Periodontitis diagnosis: A review of current and future trends in artificial intelligence

BACKGROUND

Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency.

OBJECTIVE

This review aims to evaluate the current and future trends in AI for diagnosing periodontitis.

METHODS

A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included "artificial intelligence," "panoramic radiograph," "periodontitis," "periodontal disease," and "diagnosis."

RESULTS

The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency.

CONCLUSIONS

Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.

Highly accurate measurement of the relative abundance of oral pathogenic bacteria using colony-forming unit-based qPCR

PURPOSE

Quantitative polymerase chain reaction (qPCR) has recently been employed to measure the number of bacterial cells by quantifying their DNA fragments. However, this method can yield inaccurate bacterial cell counts because the number of DNA fragments varies among different bacterial species. To resolve this issue, we developed a novel optimized qPCR method to quantify bacterial colony-forming units (CFUs), thereby ensuring a highly accurate count of bacterial cells.

METHODS

To establish a new qPCR method for quantifying 6 oral bacteria namely, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Prevotella intermedia, Fusobacterium nucleatum, and Streptococcus mutans, the most appropriate primer-probe sets were selected based on sensitivity and specificity. To optimize the qPCR for predicting bacterial CFUs, standard curves were produced by plotting bacterial CFU against Ct values. To validate the accuracy of the predicted CFU values, a spiking study was conducted to calculate the recovery rates of the predicted CFUs to the true CFUs. To evaluate the reliability of the predicted CFU values, the consistency between the optimized qPCR method and shotgun metagenome sequencing (SMS) was assessed by comparing the relative abundance of the bacterial composition.

RESULTS

For each bacterium, the selected primer-probe set amplified serial-diluted standard templates indicative of bacterial CFUs. The resultant Ct values and the corresponding bacterial CFU values were used to construct a standard curve, the linearity of which was determined by a coefficient of determination (r²) >0.99. The accuracy of the predicted CFU values was validated by recovery rates ranging from 95.1% to 106.8%. The reliability of the predicted CFUs was reflected by the consistency between the optimized qPCR and SMS, as demonstrated by a Spearman rank correlation coefficient (ρ) value of 1 for all 6 bacteria.

CONCLUSIONS

The CFU-based qPCR quantification method provides highly accurate and reliable quantitation of oral pathogenic bacteria.

Long intergenic non-coding RNA 01126 activates IL-6/JAK2/STAT3 pathway to promote periodontitis pathogenesis

OBJECTIVES

Periodontitis seriously affects oral-related quality of life and overall health. Long intergenic non-coding RNA 01126 (LINC01126) is aberrantly expressed in periodontitis tissues. This study aimed to explore the possible pathogenesis of LINC01126 in periodontitis.

METHODS

Inflammatory model of human gingival fibroblasts (HGFs) was established. Cell Counting Kit-8 (CCK-8), wound healing assay, and flow cytometry were utilized to detect biological roles of LINC01126. Binding site of miR-655-3p to LINC01126 and IL-6 was predicted. Then, subcellular localization of LINC01126 and the binding ability of miR-655-3p to LINC01126 and IL-6 in HGFs were verified. Hematoxylin-Eosin (H&E) staining and immunohistochemistry (IHC) staining were utilized to detect tissue morphology and proteins expression of clinical samples.

RESULTS

LINC01126 silencing can alleviate cell inflammation induced by lipopolysaccharide derived from Porphyromonas gingivalis, reduce cell apoptosis, and promote cell migration. As a "sponge" for miR-655-3p, LINC01126 inhibits its binding to mRNA of IL-6, thereby promoting inflammation progression and JAK2/STAT3 pathway activation. Quantitative real-time PCR, Western Blot, and IHC results of clinical tissue samples further confirmed that miR-655-3p expression was down-regulated and IL-6/JAK2/STAT3 was abnormally activated in periodontitis tissues.

CONCLUSIONS

In summary, serving as an endogenous competitive RNA of miR-655-3p, LINC01126 promotes IL-6/JAK2/STAT3 pathway activation, thereby promoting periodontitis pathogenesis.

Artificial Intelligence in Adult and Pediatric Dentistry: A Narrative Review

Artificial intelligence (AI) has been recently introduced into clinical dentistry, and it has assisted professionals in analyzing medical data with unprecedented speed and an accuracy level comparable to humans. With the help of AI, meaningful information can be extracted from dental databases, especially dental radiographs, to devise machine learning (a subset of AI) models. This study focuses on models that can diagnose and assist with clinical conditions such as oral cancers, early childhood caries, deciduous teeth numbering, periodontal bone loss, cysts, peri-implantitis, osteoporosis, locating minor apical foramen, orthodontic landmark identification, temporomandibular joint disorders, and more. The aim of the authors was to outline by means of a review the state-of-the-art applications of AI technologies in several dental subfields and to discuss the efficacy of machine learning algorithms, especially convolutional neural networks (CNNs), among different types of patients, such as pediatric cases, that were neglected by previous reviews. They performed an electronic search in PubMed, Google Scholar, Scopus, and Medline to locate relevant articles. They concluded that even though clinicians encounter challenges in implementing AI technologies, such as data management, limited processing capabilities, and biased outcomes, they have observed positive results, such as decreased diagnosis costs and time, as well as early cancer detection. Thus, further research and development should be considered to address the existing complications.

Identifying predictors of tooth loss using a rule-based machine learning approach: A retrospective study at university-setting clinics

BACKGROUND

This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach.

METHODS

Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss prediction model. The primary outcome is tooth loss count. The prediction model was built on significant factors (single or combination) selected by the RuleFit algorithm, and these factors were further adopted by the count regression model. Model performance was evaluated by root-mean-squared error (RMSE). Associations between predictors and tooth loss were also assessed by a classical statistical approach to validate the performance of the machine learning model.

RESULTS

In total, 7840 patients were included. The machine learning model predicting tooth loss count achieved RMSE of 2.71. Age, smoking, frequency of brushing, frequency of flossing, periodontal diagnosis, bleeding on probing percentage, number of missing teeth at baseline, and tooth mobility were associated with tooth loss in both machine learning and classical statistical models.

CONCLUSION

The two-step machine learning pipeline is feasible to predict tooth loss in periodontitis patients. Compared to classical statistical methods, this rule-based machine learning approach improves model explainability. However, the model's generalizability needs to be further validated by external datasets.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.

Optimizing a Diagnostic Model of Periodontitis by Using Targeted Proteomics

Periodontitis (PD), a widespread chronic infectious disease, compromises oral health and is associated with various systemic conditions and hematological alterations. Yet, to date, it is not clear whether serum protein profiling improves the assessment of PD. We collected general health data, performed dental examinations, and generated serum protein profiles using novel Proximity Extension Assay technology for 654 participants of the Bialystok PLUS study. To evaluate the incremental benefit of proteomics, we constructed two logistic regression models assessing the risk of having PD according to the CDC/AAP definition; the first one contained established PD predictors, and in addition, the second one was enhanced by extensive protein information. We then compared both models in terms of overall fit, discrimination, and calibration. For internal model validation, we performed bootstrap resampling (n = 2000). We identified 14 proteins, which improved the global fit and discrimination of a model of established PD risk factors, while maintaining reasonable calibration (area under the curve 0.82 vs 0.86; P < 0.001). Our results suggest that proteomic technologies offer an interesting advancement in the goal of finding easy-to-use and scalable diagnostic applications for PD that do not require direct examination of the periodontium.

Artificial intelligence in dentistry—A review

Artificial Intelligence (AI) is the ability of machines to perform tasks that normally require human intelligence. AI is not a new term, the concept of AI can be dated back to 1950. However, it has not become a practical tool until two decades ago. Owing to the rapid development of three cornerstones of current AI technology—big data (coming through digital devices), computational power, and AI algorithm—in the past two decades, AI applications have been started to provide convenience to people's lives. In dentistry, AI has been adopted in all dental disciplines, i.e., operative dentistry, periodontics, orthodontics, oral and maxillofacial surgery, and prosthodontics. The majority of the AI applications in dentistry go to the diagnosis based on radiographic or optical images, while other tasks are not as applicable as image-based tasks mainly due to the constraints of data availability, data uniformity, and computational power for handling 3D data. Evidence-based dentistry (EBD) is regarded as the gold standard for the decision-making of dental professionals, while AI machine learning (ML) models learn from human expertise. ML can be seen as another valuable tool to assist dental professionals in multiple stages of clinical cases. This review narrated the history and classification of AI, summarised AI applications in dentistry, discussed the relationship between EBD and ML, and aimed to help dental professionals to understand AI as a tool better to assist their routine work with improved efficiency.

Artificial Intelligence in Periodontology: A Scoping Review

Artificial intelligence (AI) is the development of computer systems whereby machines can mimic human actions. This is increasingly used as an assistive tool to help clinicians diagnose and treat diseases. Periodontitis is one of the most common diseases worldwide, causing the destruction and loss of the supporting tissues of the teeth. This study aims to assess current literature describing the effect AI has on the diagnosis and epidemiology of this disease. Extensive searches were performed in April 2022, including studies where AI was employed as the independent variable in the assessment, diagnosis, or treatment of patients with periodontitis. A total of 401 articles were identified for abstract screening after duplicates were removed. In total, 293 texts were excluded, leaving 108 for full-text assessment with 50 included for final synthesis. A broad selection of articles was included, with the majority using visual imaging as the input data field, where the mean number of utilised images was 1666 (median 499). There has been a marked increase in the number of studies published in this field over the last decade. However, reporting outcomes remains heterogeneous because of the variety of statistical tests available for analysis. Efforts should be made to standardise methodologies and reporting in order to ensure that meaningful comparisons can be drawn.

Saliva - a new opportunity for fluid biopsy

Saliva is a complex biological fluid with a variety of biomolecules, such as DNA, RNA, proteins, metabolites and microbiota, which can be used for the screening and diagnosis of many diseases. In addition, saliva has the characteristics of simple collection, non-invasive and convenient storage, which gives it the potential to replace blood as a new main body of fluid biopsy, and it is an excellent biological diagnostic fluid. This review integrates recent studies and summarizes the research contents of salivaomics and the research progress of saliva in early diagnosis of oral and systemic diseases. This review aims to explore the value and prospect of saliva diagnosis in clinical application.

Bidirectional association between polycystic ovary syndrome and periodontal diseases

Polycystic ovary syndrome (PCOS) and periodontal disease (PDD) share common risk factors. The bidirectional interaction between PCOS and PDD has been reported, but until now, the underlying molecular mechanisms remain unclear. Endocrine disorders including hyperandrogenism (HA) and insulin resistance (IR) in PCOS disturb the oral microbial composition and increase the abundance of periodontal pathogens. Additionally, PCOS has a detrimental effect on the periodontal supportive tissues, including gingiva, periodontal ligament, and alveolar bone. Systemic low-grade inflammation status, especially obesity, persistent immune imbalance, and oxidative stress induced by PCOS exacerbate the progression of PDD. Simultaneously, PDD might increase the risk of PCOS through disturbing the gut microbiota composition and inducing low-grade inflammation and oxidative stress. In addition, genetic or epigenetic predisposition and lower socioeconomic status are the common risk factors for both diseases. In this review, we will present the latest evidence of the bidirectional association between PCOS and PDD from epidemiological, mechanistic, and interventional studies. A deep understanding on their bidirectional association will be beneficial to provide novel strategies for the treatment of PCOS and PDD.

Periodontitis: An Oral Disease with Severe Consequences

Periodontitis, being a multifactorial disorder is found to be the most common oral disease denoted by the inflammation of gingiva and resorption of tooth supporting alveolar bone. The disease being closely linked with fast life style and determined by unhygienic behavioural factors, the internal milieu of oral cavity and formation of plaque biofilm on the dental and gingival surfaces. Porphyromonas gingivalis, being the major keystone pathogen of the periodontal biofilm evokes host immune responses that causes damage of gingival tissues and resorption of bones. The biofilm associated microbial community progressively aggravates the condition resulting in chronic inflammation and finally tooth loss. The disease often maintains bidirectional relationship with different systemic, genetic, autoimmune, immunodeficiency diseases and even psychological disorders. The disease can be diagnosed and predicted by various genetic, radiographic and computer-aided design (CAD) & computer-aided engineering (CAE) and artificial neural network (ANN). The elucidation of genetic background explains the inheritance of the disease. The therapeutic approaches commonly followed include mechanical removal of dental plaque with the use of systemic antibiotics. Awareness generation amongst local people, adoption of good practice of timely tooth brushing preferably with fluoride paste or with nanoconjugate pastes will reduce the chance of periodontal plaque formation. Modern tissue engineering technology like 3D bioprinting of periodontal tissue may help in patient specific flawless regeneration of tooth structures and associated bones.

Oral cleaning habits and the copy number of periodontal bacteria in pregnant women and its correlation with birth outcomes: an epidemiological study in Mibilizi, Rwanda

BACKGROUND

Since 1996, many studies have reported that periodontal disease during pregnancy may be a risk factor for preterm birth and low birth weight; however, in Africa, periodontal disease is considered a non-high-priority disease. In addition, there are few dental facilities in rural Rwanda; thus, the oral condition of pregnant women has not been investigated. The objective of this study was to assess the tooth brushing habits of pregnant women in rural Rwanda and evaluate whether periodontal bacteria in the oral cavity of pregnant women are related to birth outcomes or oral cleaning habits.

METHODS

A questionnaire survey and saliva collection were conducted for pregnant women in the catchment area population of Mibilizi Hospital located in the western part of Rwanda. Real-time PCR was performed to quantitatively detect total bacteria and 4 species of periodontal bacteria. The relationship of the copy number of each bacterium and birth outcomes or oral cleaning habits was statistically analyzed.

RESULTS

Among the participants, high copy numbers of total bacteria, Tannerella forsythia, and Treponema denticola were correlated with lower birth weight (p = 0.0032, 0.0212, 0.0288, respectively). The sex ratio at birth was higher in women who had high copy numbers of Porphyromonas gingivalis and T. denticola during pregnancy (p = 0.0268, 0.0043). Furthermore, regarding the correlation between oral cleaning habits and the amount of bacteria, the more frequently teeth were brushed, the lower the level of P. gingivalis (p = 0.0061); the more frequently the brush was replaced, the lower the levels of P. gingivalis and T. forsythia (p = 0.0153, 0.0029).

CONCLUSIONS

This study suggested that improving tooth brushing habits may reduce the risk of periodontal disease among pregnant women in rural Rwanda. It also indicated that the amount of bacteria is associated with various birth outcomes according to the bacterial species. Both access to dental clinics and the oral cleaning habits of pregnant women should be important considerations in efforts to alleviate reproductive-related outcomes in rural Africa.

Machine Learning-Based Quantification of (-)-trans-Δ-Tetrahydrocannabinol from Human Saliva Samples on a Smartphone-Based Paper Microfluidic Platform

(-)-trans-Δ-Tetrahydrocannabinol (THC) is a major psychoactive component in cannabis. Despite the recent trends of THC legalization for medical or recreational use in some areas, many THC-driven impairments have been verified. Therefore, convenient, sensitive, quantitative detection of THC is highly needed to improve its regulation and legalization. We demonstrated a biosensor platform to detect and quantify THC with a paper microfluidic chip and a handheld smartphone-based fluorescence microscope. Microfluidic competitive immunoassay was applied with anti-THC-conjugated fluorescent nanoparticles. The smartphone-based fluorescence microscope counted the fluorescent nanoparticles in the test zone, achieving a 1 pg/mL limit of detection from human saliva samples. Specificity experiments were conducted with cannabidiol (CBD) and various mixtures of THC and CBD. No cross-reactivity to CBD was found. Machine learning techniques were also used to quantify the THC concentrations from multiple saliva samples. Multidimensional data were collected by diluting the saliva samples with saline at four different dilutions. A training database was established to estimate the THC concentration from multiple saliva samples, eliminating the sample-to-sample variations. The classification algorithms included k-nearest neighbor (k-NN), decision tree, and support vector machine (SVM), and the SVM showed the best accuracy of 88% in estimating six different THC concentrations. Additional validation experiments were conducted using independent validation sample sets, successfully identifying positive samples at 100% accuracy and quantifying the THC concentration at 80% accuracy. The platform provided a quick, low-cost, sensitive, and quantitative point-of-care saliva test for cannabis.

Deep learning in periodontology and oral implantology: A scoping review

Deep learning (DL) has been employed for a wide range of tasks in dentistry. We aimed to systematically review studies employing DL for periodontal and implantological purposes. A systematic electronic search was conducted on four databases (Medline via PubMed, Google Scholar, Scopus, and Embase) and a repository (ArXiv) for publications after 2010, without any limitation on language. In the present review, we included studies that reported deep learning models' performance on periodontal or oral implantological tasks. Given the heterogeneities in the included studies, no meta-analysis was performed. The risk of bias was assessed using the QUADAS-2 tool. We included 47 studies: focusing on imaging data (n = 20) and non-imaging data in periodontology (n = 12), or dental implantology (n = 15). The detection of periodontitis and gingivitis or periodontal bone loss, the classification of dental implant systems, or the prediction of treatment outcomes in periodontology and implantology were major use cases. The performance of the models was generally high. However, it varied given the employed methods (which includes various types of convolutional neural networks (CNN) and multi-layered perceptron (MLP)), the variety in specific modeling tasks, as well as the chosen and reported outcomes, outcome measures and outcome level. Only a few studies (n = 7) showed a low risk of bias across all assessed domains. A growing number of studies evaluated DL for periodontal or implantological objectives. Heterogeneity in study design, poor reporting and a high risk of bias severely limit the comparability of studies and the robustness of the overall evidence.

Apitherapy and Periodontal Disease: Insights into In Vitro, In Vivo, and Clinical Studies

Periodontal diseases are caused mainly by inflammation of the gums and bones surrounding the teeth or by dysbiosis of the oral microbiome, and the Global Burden of Disease study (2019) reported that periodontal disease affects 20-50% of the global population. In recent years, more preference has been given to natural therapies compared to synthetic drugs in the treatment of periodontal disease, and several oral care products, such as toothpaste, mouthwash, and dentifrices, have been developed comprising honeybee products, such as propolis, honey, royal jelly, and purified bee venom. In this study, we systematically reviewed the literature on the treatment of periodontitis using honeybee products. A literature search was performed using various databases, including PubMed, Web of Science, ScienceDirect, Scopus, clinicaltrials.gov, and Google Scholar. A total of 31 studies were reviewed using eligibility criteria published between January 2016 and December 2021. In vitro, in vivo, and clinical studies (randomized clinical trials) were included. Based on the results of these studies, honeybee products, such as propolis and purified bee venom, were concluded to be effective and safe for use in the treatment of periodontitis mainly due to their antimicrobial and anti-inflammatory activities. However, to obtain reliable results from randomized clinical trials assessing the effectiveness of honeybee products in periodontal treatment with long-term follow-up, a broader sample size and assessment of various clinical parameters are needed.

Fusobacterium nucleatum Acts as a Pro-carcinogenic Bacterium in Colorectal Cancer: From Association to Causality

Colorectal cancer (CRC) is a common cancer worldwide with complex etiology. Fusobacterium nucleatum (F. nucleatum), an oral symbiotic bacterium, has been linked with CRC in the past decade. A series of gut microbiota studies show that CRC patients carry a high abundance of F. nucleatum in the tumor tissue and fecal, and etiological studies have clarified the role of F. nucleatum as a pro-carcinogenic bacterium in various stages of CRC. In this review, we summarize the biological characteristics of F. nucleatum and the epidemiological associations between F. nucleatum and CRC, and then highlight the mechanisms by which F. nucleatum participates in CRC progression, metastasis, and chemoresistance by affecting cancer cells or regulating the tumor microenvironment (TME). We also discuss the research gap in this field and give our perspective for future studies. These findings will pave the way for manipulating gut F. nucleatum to deal with CRC in the future.

Microbiome of Saliva and Plaque in Children According to Age and Dental Caries Experience

Dental caries are one of the chronic diseases caused by organic acids made from oral microbes. However, there was a lack of knowledge about the oral microbiome of Korean children. The aim of this study was to analyze the metagenome data of the oral microbiome obtained from Korean children and to discover bacteria highly related to dental caries with machine learning models. Saliva and plaque samples from 120 Korean children aged below 12 years were collected. Bacterial composition was identified using Illumina HiSeq sequencing based on the V3-V4 hypervariable region of the 16S rRNA gene. Ten major genera accounted for approximately 70% of the samples on average, including Streptococcus, Neisseria, Corynebacterium, and Fusobacterium. Differential abundant analyses revealed that Scardovia wiggsiae and Leptotrichia wadei were enriched in the caries samples, while Neisseria oralis was abundant in the non-caries samples of children aged below 6 years. The caries and non-caries samples of children aged 6-12 years were enriched in Streptococcus mutans and Corynebacterium durum, respectively. The machine learning models based on these differentially enriched taxa showed accuracies of up to 83%. These results confirmed significant alterations in the oral microbiome according to dental caries and age, and these differences can be used as diagnostic biomarkers.