Navigating therapeutic strategies: HPV classification in head and neck cancer.

The World Health Organisation recognised human papillomavirus (HPV) as the cause of multiple cancers, including head and neck cancers. HPV is a double-stranded DNA virus, and its viral gene expression can be controlled after infection by cellular and viral promoters. In cancer cells, the HPV genome is detected as either integrated into the host genome, episomal (extrachromosomal), or a mixture of integrated and episomal. Viral integration requires the breakage of both viral and host DNA, and the integration rate correlates with the level of DNA damage. Interestingly, patients with HPV-positive head and neck cancers generally have a good prognosis except for a group of patients with fully integrated HPV who show worst clinical outcomes. Those patients present with lowered expression of viral genes and limited infiltration of cytotoxic T cells. An impediment to effective therapy applications in the clinic is the sole testing for HPV positivity without considering the HPV integration status. This review will discuss HPV integration as a potential determinant of response to therapies in head and neck cancers and highlight to the field a novel therapeutic avenue that would reduce the cancer burden and improve patient survival.

Can predictive factors determine the time to treatment initiation for oral and oropharyngeal cancer? A classification and regression tree analysis.

This ecological study aimed to identify the factors with the greatest power to discriminate the proportion of oral and oropharyngeal cancer (OOC) records with time to treatment initiation (TTI) within 30 days of diagnosis in Brazilian municipalities. A descriptive analysis was performed on the variables grouped into five dimensions related to patient characteristics, access to health services, support for cancer diagnosis, human resources, and socioeconomic characteristics of 3,218 Brazilian municipalities that registered at least one case of OOC in 2019. The Classification and Regression Trees (CART) technique was adopted to identify the explanatory variables with greater discriminatory power for the TTI response variable. There was a higher median percentage of records in the age group of 60 years or older. The median percentage of records with stage III and IV of the disease was 46.97%, and of records with chemotherapy, radiation, or both as the first treatment was 50%. The median percentage of people with private dental and health insurance was low. Up to 75% had no cancer diagnostic support services, and up to 50% of the municipalities had no specialist dentists. Most municipalities (49.4%) started treatment after more than 30 days. In the CART analysis, treatment with chemotherapy, radiotherapy, or both explained the highest TTI in all municipalities, and it was the most relevant for predicting TTI. The final model also included anatomical sites in the oral cavity and oropharynx and the number of computed tomography services per 100,000. There is a need to expand the availability of oncology services and human resources specialized in diagnosing and treating OOC in Brazilian municipalities for a timely TTI of OOC.

Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators.

INTRODUCTION: Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. MATERIALS AND METHODS: A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. RESULTS: 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. CONCLUSION: A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.

Artificial intelligence and smile design: An e-Delphi consensus statement of ethical challenges.

PURPOSE: Smile design software increasingly relies on artificial intelligence (AI). However, using AI for smile design raises numerous technical and ethical concerns. This study aimed to evaluate these ethical issues. METHODS: An international consortium of experts specialized in AI, dentistry, and smile design was engaged to emulate and assess the ethical challenges raised by the use of AI for smile design. An e-Delphi protocol was used to seek the agreement of the ITU-WHO group on well-established ethical principles regarding the use of AI (wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency). Each principle included examples of ethical challenges that users might encounter when using AI for smile design. RESULTS: On the first round of the e-Delphi exercise, participants agreed that seven items should be considered in smile design (diversity, transparency, wellness, privacy protection, prudence, law and governance, and sustainable development), but the remaining four items (equity, accountability and responsibility, solidarity, and respect of autonomy) were rejected and had to be reformulated. After a second round, participants agreed to all items that should be considered while using AI for smile design. CONCLUSIONS: AI development and deployment for smile design should abide by the ethical principles of wellness, respect for autonomy, privacy protection, solidarity, governance, equity, diversity, expertise/prudence, accountability/responsibility, sustainability, and transparency.

A review of animal models for burning mouth syndrome: Mechanistic insights and knowledge gaps.

OBJECTIVES: The underlying mechanisms of burning mouth syndrome (BMS) remain unclear leading to challenges and unsatisfactory management. Current treatments focus primarily on symptom relief, with few consistently achieving a 50% reduction in pain. This review aims to explore animal models of BMS to gain a better understanding of the underlying mechanisms and to discuss potential and existing knowledge gaps. METHODS: A comprehensive review of PubMed® , Google Scholar, and Scopus was performed to assess advances and significant gaps of existing rodent models that mimic BMS-related symptoms. RESULTS: Rodent models of BMS involve reproduction of dry-tongue, chorda tympani transection, or overexpression of artemin protein. Existing preclinical models tend to highlight one specific etiopathogenesis and often overlook sex- and hormone-specific factors. CONCLUSION: Combining aspects from various BMS models could prove beneficial in developing comprehensive experimental designs and outcomes encompassing the multifaceted nature of BMS.

Resorbable bilayer membrane made of L-lactide-ε-caprolactone in guided bone regeneration: an in vivo experimental study.

PURPOSE: Guided bone regeneration (GBR) is an accepted method in dental practice that can successfully increase the bone volume of the host at sites chosen for implant placement; however, existing GBR membranes exhibit rapid absorption and lack of adequate space maintenance capabilities. We aimed to compare the effectiveness of a newly developed resorbable bilayer membrane composed of poly (L-lactic acid) and poly (-caprolactone) (PLACL) with that of a collagen membrane in a rat GBR model. METHODS: The rat calvaria was used as an experimental model, in which a plastic cylinder was placed. We operated on 40 male Fisher rats and subsequently performed micro-computed tomography and histomorphometric analyses to assess bone regeneration. RESULTS: Significant bone regeneration was observed, which was and similar across all the experimental groups. However, after 24 weeks, the PLACL membrane demonstrated significant resilience, and sporadic partial degradation. This extended preservation of the barrier effect has great potential to facilitate optimal bone regeneration. CONCLUSIONS: The PLACL membrane is a promising alternative to GBR. By providing a durable barrier and supporting bone regeneration over an extended period, this resorbable bilayer membrane could address the limitations of the current membranes. Nevertheless, further studies and clinical trials are warranted to validate the efficacy and safety of The PLACL membrane in humans.

Associations among gene polymorphisms, crestal bone loss, and bone mineral density in patients receiving dental implants.

Objectives: Interleukin 1 (IL-1) and interleukin 6 (IL-6) gene polymorphisms have been suggested to be responsible for diminished bone mineral density (BMD) and high crestal bone loss (CBL) in some individuals. However, the effects of systemic BMD on variations in peri-implant CBL are unclear. Hence, this study was aimed at investigating the association of IL-1 and IL-6 gene polymorphisms with systemic BMD and CBL around dental implants. Methods: A total of 190 participants undergoing dental implantation in the mandibular posterior region were selected according to predetermined selection criteria and divided into a normal BMD group (NBD, 93 participants, T-score ≥ -1) and low BMD group (LBD, including both osteoporosis and osteopenia, 97 participants, T-score < -1 standard deviation) according to the BMD of the right femoral neck, measured with dual-energy X-ray absorptiometry. Dental implants were placed through the standard surgical protocol, and CBL was calculated after 6 months with cone beam computed tomography scans before second-stage surgery. Genotyping was performed on all participants for IL-1A-889 A/G, IL-1B-511G/A, IL-1B+3954, and IL-6-572 C/G gene polymorphisms. Results: The demographic and clinical characteristics of the participants in both groups were compared with t-test and chi-square test (χ2). The associations of NBD and LBD with the different genotypes and CBL was determined with odds ratios, and p < 0.05 was considered statistically significant. The frequency of IL-1B-511AA and IL-6-572 GG genotypes was significantly higher in LBD than in NBD (p < 0.05). In LBD, the IL-1B-511 AA (AA vs GA + GG; p ≤ 0.001) and IL-6-572 GG (GG vs CC + GC; p = 0.001) genotypes were significantly associated with higher peri-implant CBL. Conclusions: Individuals with the IL-1B-511 AA or IL-6-572 GG genotype had elevated risk of osteoporosis/osteopenia and were more susceptible to CBL around dental implants.

Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis.

Deep learning (DL) offers promising performance in computer vision tasks and is highly suitable for dental image recognition and analysis. We evaluated the accuracy of DL algorithms in identifying and classifying dental implant systems (DISs) using dental imaging. In this systematic review and meta-analysis, we explored the MEDLINE/PubMed, Scopus, Embase, and Google Scholar databases and identified studies published between January 2011 and March 2022. Studies conducted on DL approaches for DIS identification or classification were included, and the accuracy of the DL models was evaluated using panoramic and periapical radiographic images. The quality of the selected studies was assessed using QUADAS-2. This review was registered with PROSPERO (CRDCRD42022309624). From 1,293 identified records, 9 studies were included in this systematic review and meta-analysis. The DL-based implant classification accuracy was no less than 70.75% (95% confidence interval [CI], 65.6%-75.9%) and no higher than 98.19 (95% CI, 97.8%-98.5%). The weighted accuracy was calculated, and the pooled sample size was 46,645, with an overall accuracy of 92.16% (95% CI, 90.8%-93.5%). The risk of bias and applicability concerns were judged as high for most studies, mainly regarding data selection and reference standards. DL models showed high accuracy in identifying and classifying DISs using panoramic and periapical radiographic images. Therefore, DL models are promising prospects for use as decision aids and decision-making tools; however, there are limitations with respect to their application in actual clinical practice.

Accuracy of digital and conventional systems in locating occlusal contacts: A clinical study.

STATEMENT OF PROBLEM: The accuracy of methods used for locating occlusal contacts throughout the entire clinical procedure has been poorly studied. PURPOSE: The purpose of this clinical study was to determine the reproducibility and criterion validity for different methods of locating occlusal contacts. MATERIAL AND METHODS: Thirty-two adults with natural dentitions participated in this cross-sectional test-retest study. In total, occlusal contacts at maximum intercuspation were recorded by using 15 methods: silicone transillumination with Occlufast Rock (40, 50, 100, and 200 µm) and Occlufast CAD (40 and 50 µm); virtual occlusion (100, 200, 300, and 400 µm); articulating film (12-, 40-, 100-, and 200-µm-thick); and T-Scan III. Images of the occlusal records were scaled and calibrated spatially, and the occlusal contacts of the right posterior mandibular teeth were delimited by using the FIJI software program. Reproducibility was expressed as 95% confidence intervals (95% CI) of the percentage of agreement in the location of the occlusal contacts between images from the test sessions against retest sessions using the same method. Criterion validity was expressed as 95% CI of the percentage of agreement in the location of the occlusal contacts between images from the test sessions against images from Occlufast Rock (criterion standard). RESULTS: Occlufast Rock achieved 85% to 95% agreement in the location of the occlusal contacts between the 2 sessions, whereas Occlufast CAD, 200-µm articulating film, and T-Scan offered 79% to 86%, 68% to 75%, and 65% to 75% agreement, respectively. The most valid method was Occlufast CAD (74% to 80%) followed by the 200-µm articulating film (57% to 63%), 400-µm virtual occlusion (53% to 62%), 100-µm articulating film (52% to 60%), and T-Scan (48% to 56%). CONCLUSIONS: Conventional methods, such as 100- and 200-µm articulating film and digital methods, including 400 µm virtual occlusion and T-Scan, offer sufficient accuracy in locating the occlusal contacts. However, strategies are needed to improve accuracy.

A Systematic Review of the Use of mHealth in Oral Health Education among Older Adults.

Oral diseases are largely preventable. However, as the number of older adults is expected to increase, along with the high cost and various barriers to seeking continuous professional care, a sustainable approach is needed to assist older adults in maintaining their oral health. Mobile health (mHealth) technologies may facilitate oral disease prevention and management through oral health education. This review aims to provide an overview of existing evidence on using mHealth to promote oral health through education among older adults. A literature search was performed across five electronic databases. A total of five studies were identified, which provided low to moderate evidence to support using mHealth among older adults. The selected studies showed that mHealth could improve oral health management, oral health behavior, and oral health knowledge among older adults. However, more quality studies regarding using mHealth technologies in oral health management, oral health behavior, and oral health knowledge among older adults are needed.

Growth differentiation factor-15 and circulating biomarkers as predictors of periodontal treatment effects in patients with periodontitis: a randomized-controlled clinical trial.

BACKGROUND: During the last decades, in patients with periodontitis, periodontal treatment has been shown to reduce the potential release of local and systemic biomarkers linked to an early risk of systemic inflammatory disorders. This study evaluated the efficacy of non-surgical-periodontal treatment (NSPT) on growth differentiation factor 15 (GDF-15) and related circulating biomarkers such as glutathione peroxidase 1 (GPx-1), c-reactive protein (hs-CRP), and surfactant protein D (SP-D) in periodontal patients and explored whether subjects who had high GDF-15 levels at baseline showed increased clinical benefits following NSPT at 6-months follow-up. METHODS: For this two-arm, parallel randomized clinical trial, patients with periodontitis were randomly allocated to receive quadrant scaling and root-planing (Q-SRP, n = 23, median age 51 years old) or full-mouth disinfection (FMD, n = 23, median age 50 years old) treatment. Clinical and periodontal parameters were recorded in all enrolled patients. The primary outcome was to analyse serum concentrations changes of GDF-15 and of GPx-1, hs-CRP, and SP-D at baseline and at 30, 90, and 180-days follow-up after NSPT through enzyme-linked immunosorbent assay (ELISA) and nephelometric assay techniques. RESULTS: In comparison with FMD, patients of the Q-SRP group showed a significant improvement in clinical periodontal parameters (p < 0.05) and a reduction in the mean levels of GDF-15 (p = 0.005), hs-CRP (p < 0.001), and SP-D (p = 0.042) and an increase of GPx-1 (p = 0.025) concentrations after 6 months of treatment. At 6 months of treatment, there was a significant association between several periodontal parameters and the mean concentrations of GDF-15, GPx-1, hs-CRP, and SP-D (p < 0.05 for all parameters). Finally, the ANOVA analysis revealed that, at 6 months after treatment, the Q-SRP treatment significantly impacted the reduction of GDF-15 (p = 0.015), SP-D (p = 0.026) and the upregulation of GPx-1 (p = 0.045). CONCLUSION: The results evidenced that, after 6 months of treatment, both NSPT protocols improved the periodontal parameters and analyzed biomarkers, but Q-SRP was more efficacious than the FMD approach. Moreover, patients who presented high baseline GDF-15 and SP-D levels benefited more from NSPT at 6-month follow-up. TRIAL REGISTRATION: NCT05720481.

Ethical considerations on artificial intelligence in dentistry: A framework and checklist.

OBJECTIVE: Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS: Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS: The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS: Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE: While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.

Deep learning for detection and 3D segmentation of maxillofacial bone lesions in cone beam CT.

OBJECTIVES: To develop an automated deep-learning algorithm for detection and 3D segmentation of incidental bone lesions in maxillofacial CBCT scans. METHODS: The dataset included 82 cone beam CT (CBCT) scans, 41 with histologically confirmed benign bone lesions (BL) and 41 control scans (without lesions), obtained using three CBCT devices with diverse imaging protocols. Lesions were marked in all axial slices by experienced maxillofacial radiologists. All cases were divided into sub-datasets: training (20,214 axial images), validation (4530 axial images), and testing (6795 axial images). A Mask-RCNN algorithm segmented the bone lesions in each axial slice. Analysis of sequential slices was used for improving the Mask-RCNN performance and classifying each CBCT scan as containing bone lesions or not. Finally, the algorithm generated 3D segmentations of the lesions and calculated their volumes. RESULTS: The algorithm correctly classified all CBCT cases as containing bone lesions or not, with an accuracy of 100%. The algorithm detected the bone lesion in axial images with high sensitivity (95.9%) and high precision (98.9%) with an average dice coefficient of 83.5%. CONCLUSIONS: The developed algorithm detected and segmented bone lesions in CBCT scans with high accuracy and may serve as a computerized tool for detecting incidental bone lesions in CBCT imaging. CLINICAL RELEVANCE: Our novel deep-learning algorithm detects incidental hypodense bone lesions in cone beam CT scans, using various imaging devices and protocols. This algorithm may reduce patients' morbidity and mortality, particularly since currently, cone beam CT interpretation is not always preformed. KEY POINTS: • A deep learning algorithm was developed for automatic detection and 3D segmentation of various maxillofacial bone lesions in CBCT scans, irrespective of the CBCT device or the scanning protocol. • The developed algorithm can detect incidental jaw lesions with high accuracy, generates a 3D segmentation of the lesion, and calculates the lesion volume.

Deep convolutional neural network algorithm for the automatic segmentation of oral potentially malignant disorders and oral cancers.

This study aimed to develop an algorithm to automatically segment the oral potentially malignant diseases (OPMDs) and oral cancers (OCs) of all oral subsites with various deep convolutional neural network applications. A total of 510 intraoral images of OPMDs and OCs were collected over 3 years (2006-2009). All images were confirmed both with patient records and histopathological reports. Following the labeling of the lesions the dataset was arbitrarily split, using random sampling in Python as the study dataset, validation dataset, and test dataset. Pixels were classified as the OPMDs and OCs with the OPMD/OC label and the rest as the background. U-Net architecture was used and the model with the best validation loss was chosen for the testing among the trained 500 epochs. Dice similarity coefficient (DSC) score was noted. The intra-observer ICC was found to be 0.994 while the inter-observer reliability was 0.989. The calculated DSC and validation accuracy across all clinical images were 0.697 and 0.805, respectively. Our algorithm did not maintain an excellent DSC due to multiple reasons for the detection of both OC and OPMDs in oral cavity sites. A better standardization for both 2D and 3D imaging (such as patient positioning) and a bigger dataset are required to improve the quality of such studies. This is the first study which aimed to segment OPMDs and OCs in all subsites of oral cavity which is crucial not only for the early diagnosis but also for higher survival rates.

Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs.

OBJECTIVE: Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. METHODS: We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. RESULTS: For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. CONCLUSION: If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. CLINICAL SIGNIFICANCE: This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.

Advances in tissue engineering of cancer microenvironment-from three-dimensional culture to three-dimensional printing.

Cancer treatment development is a complex process, with tumor heterogeneity and inter-patient variations limiting the success of therapeutic intervention. Traditional two-dimensional cell culture has been used to study cancer metabolism, but it fails to capture physiologically relevant cell-cell and cell-environment interactions required to mimic tumor-specific architecture. Over the past three decades, research efforts in the field of 3D cancer model fabrication using tissue engineering have addressed this unmet need. The self-organized and scaffold-based model has shown potential to study the cancer microenvironment and eventually bridge the gap between 2D cell culture and animal models. Recently, three-dimensional (3D) bioprinting has emerged as an exciting and novel biofabrication strategy aimed at developing a 3D compartmentalized hierarchical organization with the precise positioning of biomolecules, including living cells. In this review, we discuss the advancements in 3D culture techniques for the fabrication of cancer models, as well as their benefits and limitations. We also highlight future directions associated with technological advances, detailed applicative research, patient compliance, and regulatory challenges to achieve a successful bed-to-bench transition.

Machine Learning in Dentistry: A Scoping Review.

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.

Melatonin Mitigates iNOS-Related Effects of HEMA and Camphorquinone in Human Dental Pulp Cells: Relevance for Postoperative Sensitivity Mechanism in Type 2 Diabetes.

High elution and diffusion of 2-hydroxylethyl methacrylate (HEMA) and camphorquinone (CQ) through dentinal tubules may induce pulp injury and postoperative sensitivity. We aimed to investigate the melatonin protective effect in HEMA- and CQ-treated human dental pulp cells (hDPCs) as well as its relevance in a mechanism for postoperative sensitivity in diabetic patients. hDPCs were exposed to HEMA (5 mM) and/or CQ (1 mM) in the absence and presence of melatonin (MEL) (0.1 mM and 1 mM). Heme oxygenase-1 (HMOX1), NADPH oxidase-4 (NOX4), BCL-2-associated X-protein (BAX), B-cell lymphoma-2 (BCL-2) and caspase-3 (CASP3) gene expression levels, and superoxide dismutase (SOD) activity were measured in hDPCs while inducible nitric oxide synthase (iNOS) and melatonin protein expression were measured in human dental pulp as well, by RT-PCR, by ELISA, and spectrophotometrically. Bioinformatic analyses were performed by using the ShinyGO (v.0.75) application. Type 2 diabetic patients showed a higher incidence of postoperative sensitivity and lower melatonin and higher iNOS content in dental pulp tissue compared with non-diabetic patients. Melatonin, when co-added in hDPC culture, reverses HEMA and CQ cytotoxic effects via anti-apoptotic and anti-inflammatory/antioxidant iNOS-related effects. Enrichment analyses showed that genes/proteins, altered by HEMA and CQ and normalized by melatonin, are the most prominently overrepresented in type 2 diabetes mellitus pathways and that they share subcellular localization in different oligomeric protein complexes consisting of anti- and pro-apoptotic regulators. This is the first evidence of the ability of melatonin to counteract iNOS-mediated inflammatory and stress effects in HEMA- and CQ-treated hDPCs, which could be of significance for the modulation of presently observed immediate postoperative sensitivity after composite restoration in type 2 diabetic patients.

Artificial intelligence for oral and dental healthcare: Core education curriculum.

OBJECTIVES: Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI. METHODS: Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process. RESULTS: Four domains of learning outcomes emerged, with most outcomes being on the "knowledge" level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted. CONCLUSION: Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education. CLINICAL SIGNIFICANCE: A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers' literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.