Journal of oral biology and craniofacial research最新文献

{"title":"Artificial Intelligence–driven spatial transcriptomics in OSCC: Mapping the tumor microenvironment and personalizing therapy","authors":"Soundharya Manogaran ,&nbsp;Ramya Ramadoss ,&nbsp;Suganya Panneer Selvam ,&nbsp;Sandhya Sundar ,&nbsp;Nitya Krishnasamy ,&nbsp;Hemashree ,&nbsp;Karunya Krishnakumar ,&nbsp;Preethi Shankar","doi":"10.1016/j.jobcr.2025.10.015","DOIUrl":"10.1016/j.jobcr.2025.10.015","url":null,"abstract":"<div><div>Spatial transcriptomics (ST) represents a transformative approach in cancer research, offering high-resolution insights into the spatial organization of gene expression within tissues, particularly relevant for the complex tumor microenvironment (TME) of oral squamous cell carcinoma (OSCC). Unlike conventional bulk RNA sequencing, which masks spatial heterogeneity, ST retains the architectural context of tumors, enabling the mapping of molecular gradients, tumor–stroma interactions, and immune cell localization. Various ST platforms—such as 10x Genomics Visium, Slide-seqV2, MERFISH, NanoString GeoMx DSP, CosMx SMI, and BGI.</div><div>Stereo-seq—each offers unique advantages in resolution, sample compatibility, and transcriptome depth. Their application in OSCC has led to the identification of spatially distinct gene signatures, aiding in the stratification of tumor subtypes and uncovering novel prognostic markers. Furthermore, the integration of ST with artificial intelligence (AI) and machine learning has enhanced its analytical capabilities, enabling automated feature extraction, spatial clustering, and predictive modeling of disease progression. Despite these advancements, limitations such as high computational demands, limited access to fresh-frozen tissues, and platform-specific biases persist. Nonetheless, the synergy between ST and AI heralds a new era in precision pathology, with the potential to revolutionize diagnosis, risk assessment, and personalized therapeutic strategies for OSCC.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1862-1873"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The effect of thermocycling on fracture resistance of zirconia crowns cemented with polylysine modified resin cements (Comparative in vitro study)","authors":"Rasha H. Jehad ,&nbsp;Zainab M. Mansi ,&nbsp;Samar Abdul Hamed","doi":"10.1016/j.jobcr.2025.10.018","DOIUrl":"10.1016/j.jobcr.2025.10.018","url":null,"abstract":"<div><h3>Background</h3><div>resin cement type and intraoral temperature fluctuations may affect the fracture performance of successful zirconia restorations. To fill this gap, the purpose of this study is to evaluate and compare the influence of thermocycling on fracture resistance and mode of failure of monolithic zirconia crowns luted with Rely X™ U200 and Breeze^TMself-adhesive resin cements as well as imply the effect of adding 2 % of polylysine (PLS) to these cements.</div></div><div><h3>Materials</h3><div>64 maxillary premolars were milled out of zirconia blocks using CAD/CAM milling system. They were divided into four groups (n = 16) according to the cement type. Four different resin cements were used (RelyX^TMU200, Breeze™, RelyX™ U200 with 2 % PLS and Breeze™ with 2%PLS). Each group was further subdivided into experimental and control groups (n = 8). The experimental specimens were exposed to thermocycling protocol of 10,000 cycles in water bath at 5 °C and 55 °C.Each specimen was subjected to axial load until fracture using universal testing machine. Fracture modes were analyzed using digital microscope. Data were statistically analyzed using paired <em>t</em>-test at a level of significance of 0.05.</div></div><div><h3>Results</h3><div>there was a statistical significant difference in fracture load among groups (p &lt; 0.05) with the highest mean in Rely X cement. Although the fracture loads statistically decreased after thermocycling (p &lt; 0.05) there was no significant effect on the addition of 2 % PLS (p &gt; 0.05). Microscopical analysis demonstrated a majority of catastrophic mode of fracture.</div></div><div><h3>Conclusion</h3><div>both cement type and thermocycling exert significant effects on fracture resistance of premolars crowns restored with monolithic zirconia, while the addition of 2 % PLS exerted negligible effect.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1843-1850"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Possible applicability of flavonoid hesperetin for the treatment of dental biofilm-forming Candida glabrata","authors":"Bhavesh Sureendar ,&nbsp;Vinothini Gunasekaran ,&nbsp;Dhanraj Ganapathy,&nbsp;Palanivel Sathishkumar","doi":"10.1016/j.jobcr.2025.10.016","DOIUrl":"10.1016/j.jobcr.2025.10.016","url":null,"abstract":"<div><h3>Background</h3><div>Biofilm formation is a key virulence factor for <em>Candida glabrata</em> because it forms an extracellular matrix that prevents antifungal passage, which resists antifungal agents and causes treatment failure. To overcome this, the present study aimed to investigate the therapeutic potential of phytocompounds as an alternative choice in eliminating dental biofilm-forming <em>C. glabrata</em>.</div></div><div><h3>Method</h3><div>The antifungal potential of various phytocompounds against <em>C. glabrata</em> was evaluated through zone of inhibition (ZOI) and minimum inhibitory concentration (MIC) studies. The antibiofilm potential of phytocompounds was evaluated against <em>C. glabrata</em> and confirmed through CV staining, MTT assay and CLSM analysis. The biocompatibility of hesperetin was checked by hemocompatibility test on human RBCs.</div></div><div><h3>Results</h3><div>Quercetin, morin, rutin, naringin, and hesperetin exhibits antifungal activity towards <em>C. glabrata</em>. Hesperetin shows slightly higher antifungal activity (ZOI: 14.6 ± 0.57 mm and MIC: 0.3 ± 0.01 mM) for <em>C. glabrata</em>, compared to other tested phytocompounds. The 100 % deadness of <em>C. glabrata</em> cells in biofilm was observed at 2MIC (0.6 mM) of hesperetin. Interestingly, hesperetin demonstrates acceptable level hemolysis (5 %) on RBCs up to 10 mM.</div></div><div><h3>Conclusions</h3><div>These findings suggest that hesperetin is a novel natural antifungal agent capable of effectively inhibiting the biofilm-forming <em>C. glabrata</em>, with the potential for development into safe, phyto-based therapeutics for managing dental infections.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1799-1805"},"PeriodicalIF":0.0,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145415752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correlation between gingival fibroblast cell viability and degree of conversion of resin composites at different light-curing tip distances","authors":"Atieh Hashemian ,&nbsp;Mahshid Hodjat ,&nbsp;Marjan Behroozibakhsh","doi":"10.1016/j.jobcr.2025.10.011","DOIUrl":"10.1016/j.jobcr.2025.10.011","url":null,"abstract":"<div><h3>Objective</h3><div>This study aimed to evaluate the effect of light-curing tip distance on the degree of conversion (DC), as well as on the cell viability. The study also aimed to assess the correlation between DC% and cell viability at different light-curing tip distances.</div></div><div><h3>Materials and methods</h3><div>Nanocomposite discs were cured using an LED light-curing unit at distances of 0 mm (G0), 2 mm (G2), 4 mm (G4), 6 mm (G6), and 8 mm (G8) for 20 s. The DC was measured using ATR-FTIR analysis. Cell viability was assessed through MTT assay on human gingival fibroblasts. The results were analyzed using one-way ANOVA and Pearson correlation analysis.</div></div><div><h3>Results</h3><div>The DC at the surface was significantly higher than the bottom of the samples in all groups (p &lt; 0.001). The DC on both surfaces decreased as the distance between the light-curing unit and the sample surface increased. Moreover, with an increase in distance, the cell viability decreased. This difference was statistically significant in all groups, except for the G0 and G2 groups (p &lt; 0.001). The results of Pearson correlation analysis showed a positive and statistically significant correlation between cell viability and DC% at both surfaces (p &lt; 0.05). Furthermore, a negative and statistically significant correlation was observed between tip distance and DC% at both top and bottom surfaces as well as cell viability(p &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Increasing the distance from the light-curing tip adversely affects composite polymerization and biocompatibility, likely due to insufficient curing and subsequent monomer and nanoparticle release. This study emphasizes the importance of optimal curing conditions.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1793-1798"},"PeriodicalIF":0.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based prediction of drug-gene interactions modulating tight junction integrity: A deep learning framework highlighting multiple therapeutic targets","authors":"Varun Keskar,&nbsp;Amrutha Shenoy,&nbsp;Shreya Desai","doi":"10.1016/j.jobcr.2025.10.014","DOIUrl":"10.1016/j.jobcr.2025.10.014","url":null,"abstract":"<div><h3>Introduction</h3><div>Tight junctions regulate epithelial and endothelial barrier function, and their dysfunction is linked to diseases such as inflammatory bowel disease, asthma, and cancer. Identifying drug-gene interactions influencing tight junctions is critical for therapeutic development. This study proposes a deep learning-based neural network framework to predict drug-induced modulation of tight junction integrity using multi-omics data.</div></div><div><h3>Materials and methods</h3><div>Transcriptomic data from NCBI GEO underwent preprocessing, with DEGs identified and key hub genes extracted via network analysis. A feedforward neural network was trained using these features, with performance evaluated through AUC, CA, F1-score, precision, recall, and specificity, ensuring robust predictive accuracy.</div></div><div><h3>Results</h3><div>The neural network model achieved an AUC of 0.947, CA of 0.980, and F1-score of 0.969, indicating excellent classification performance. Among the predicted candidates, Cimifugin was highlighted for its modulatory effects on CLDN1; additional candidates included Baicalein and Berberine.</div></div><div><h3>Discussion</h3><div>The deep learning model demonstrated superior predictive power compared to traditional methods, with strong precision and recall metrics. The framework provides a scalable, data-driven solution for predicting drug-induced changes in tight junction function, with significant implications for drug discovery and personalized medicine.</div></div><div><h3>Conclusion</h3><div>This study presents a powerful AI-based approach for discovering drug candidates targeting tight junctions, offering potential therapeutic strategies for diseases involving tight junction disruption.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1786-1792"},"PeriodicalIF":0.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved classification of oral cancer through a personalized transfer learning CNN architecture","authors":"Udita J. Monani ,&nbsp;Tae Soo Yun ,&nbsp;Mangal Sain ,&nbsp;Prasant Kumar Pattnaik","doi":"10.1016/j.jobcr.2025.10.002","DOIUrl":"10.1016/j.jobcr.2025.10.002","url":null,"abstract":"<div><h3>Introduction</h3><div>Oral cancer presents a significant danger to worldwide health, resulting in high death rates and substantial suffering. Early detection is crucial to improving treatment outcomes. This study uses deep learning techniques, particularly Convolutional Neural Networks (CNNs) and transfer learning methodologies, to propose a dependable machine learning system for oral cancer detection.</div></div><div><h3>Materials and methods</h3><div>The proposed model leverages CNNs and transfer learning, followed by two sequential fully connected operations: FC1 (Feature Embedding) to consolidate learned features and FC2 (Classification Head) for final classification. Despite a small dataset and imbalanced class distribution, the model was trained and evaluated using carefully selected performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC. These metrics were specifically chosen to address the challenges of imbalanced datasets, where accuracy alone can be misleading. Class imbalance was addressed through the Synthetic Minority Oversampling Technique (SMOTE), data augmentation, and careful preprocessing strategies. The performance was validated through confusion matrices and AUC-ROC analyses to ensure reliability. However, external validation was not performed, representing a limitation of this study.</div></div><div><h3>Results</h3><div>The model achieved an F1-score of 81.48 %, accuracy of 81.38 %, precision of 84.62 %, recall of 78.57 %, and a ROC-AUC score of 0.9082 on the test dataset. During training, it achieved higher metrics: accuracy of 96.94 %, precision of 97.92 %, recall of 96.17 %, F1-score of 97.04 %, and a ROC-AUC score of 0.9967.</div></div><div><h3>Conclusion</h3><div>This research highlights how artificial intelligence can impact clinical workflows in detecting cancer early. The results offer a hopeful path for advancements in automated cancer diagnosis technologies.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1779-1785"},"PeriodicalIF":0.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324559","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amnion mesenchymal stem cell metabolites reduce inflammation in diabetic salivary gland defect rat models","authors":"Diah Savitri Ernawati ,&nbsp;Karlina Puspasari ,&nbsp;Gremita Kusuma Dewi ,&nbsp;Desiana Radithia ,&nbsp;Nurina Febriyanti Ayuningtyas ,&nbsp;Reiska Kumala Bakti ,&nbsp;Satutya Wicaksono ,&nbsp;Meircurius Dwi Condro Surboyo ,&nbsp;Madhu Shrestha ,&nbsp;Alexander Patera Nugraha ,&nbsp;Igo Saiful Ihsan ,&nbsp;Wibi Riawan ,&nbsp;Annissaqiella Maharani ,&nbsp;Sri Dewanthy Putri ,&nbsp;Adhistya Viany","doi":"10.1016/j.jobcr.2025.10.013","DOIUrl":"10.1016/j.jobcr.2025.10.013","url":null,"abstract":"<div><h3>Objectives</h3><div>Hyposalivation or xerostomia are well-established intraoral complications of diabetes mellitus (DM). Amniotic Mesenchymal Stem Cell Metabolite Products (AMSC-MPs) are being widely studied for their immunomodulatory action. However, their potential in a salivary gland defect model has yet to be explored. This study aims to determine the potential of AMSC-MPs in modulating the inflammatory response in the salivary glands of rats with persistent hyperglycemia, mimicking the pathogenesis of salivary gland disorders in DM.</div></div><div><h3>Methods</h3><div>Forty-eight male pre-conditioned diabetic rats were divided into treatment and control groups. The treatment group received AMSC-MPs intraglandular injections, while the control group received phosphate-buffered saline intraglandular injections. Both groups were injected for 3, 5, 7, and 10 consecutive days. Subsequently, the submandibular salivary glands were biopsied and processed for formalin-fixed paraffin-embedded tissue for immunohistochemical assessment. The inflammatory response was assessed by quantifying the expression of tumor necrosis factor α (TNF-α), interleukin 6 (IL-6), interleukin 10 (IL-10), and transforming growth factor β (TGF-β).</div></div><div><h3>Results</h3><div>This study found significant downregulation of inflammatory cytokines, TNF-α and IL-6, in the AMSC-MPs intraglandular injections compared to the control (p &lt; 0.05). Moreover, IL-10 and TGF-β expression, which may act as anti-inflammatory cytokines in this pathology, was significantly upregulated compared to the control (p &lt; 0.05).</div></div><div><h3>Conclusion</h3><div>Our study demonstrated the anti-inflammatory potential of AMSC-MPs intraglandular injections in a diabetes-induced salivary gland defect rat model.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1767-1772"},"PeriodicalIF":0.0,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI performance in oral cytology for differentiating poorly defined tumor cells from reactive atypia","authors":"Kaori Oya ,&nbsp;Kazuma Kokomoto ,&nbsp;Mami Okamoto ,&nbsp;Yuko Kondo ,&nbsp;Sunao Sato ,&nbsp;Kazunori Nozaki ,&nbsp;Mitsunobu Kishino ,&nbsp;Satoru Toyosawa","doi":"10.1016/j.jobcr.2025.10.003","DOIUrl":"10.1016/j.jobcr.2025.10.003","url":null,"abstract":"<div><h3>Background</h3><div>Despite its investigative potential, few studies have reported the use of artificial intelligence (AI) in oral cytology. Oral mucosal cells display significant cellular atypia due to inflammatory stimulation or denaturation, whereas well-differentiated oral squamous cell carcinomas do not always show remarkable cellular atypia. The presence of noncancerous atypical cells alongside ill-defined tumor cells poses significant challenges to the development of effective AI tools. Thus, we aimed to investigate the effect of these atypical cells on AI performance.</div></div><div><h3>Materials and methods</h3><div>We used 29 cases of non-neoplastic lesions, including gingivitis, stomatitis, and 17 squamous cell carcinomas for supervised learning and validation. The cells were classified into four categories: normal, cancer, orange-suspicious, and green-suspicious. Orange and green suspicious cells indicated tumor cells lacking definitive morphological features. Annotation was performed using VoTT v2.2.0, and YOLOv7 as the object detection model, with model training being performed in six ways.</div></div><div><h3>Results</h3><div>The model that learned orange- and green-suspicious cells as cancer exhibited the highest detection capabilities, but also yielded a high number of false positives. In contrast, the model that excluded information about suspicious cells could rightfully identify some suspicious cells as cancer with fewer false positives.</div></div><div><h3>Conclusions</h3><div>Discriminating ill-defined tumor cells from atypical non-neoplastic cells based solely on morphology is challenging. Classifying suspicious cells as cancer often results in numerous false positives. Conversely, AI trained on normal and cancer can reveal previously unnoticed cancerous features in suspicious cells.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1773-1778"},"PeriodicalIF":0.0,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145325169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence for maxillofacial prosthodontics: A technological shift in craniofacial rehabilitation- a scoping review","authors":"Anupama Aradya ,&nbsp;Koduru Sravani ,&nbsp;M.B. Ravi ,&nbsp;K.N. Raghavendra Swamy ,&nbsp;S. Ganesh ,&nbsp;K. Pradeep Chandra ,&nbsp;H.K. Sowmya ,&nbsp;B.V. Jayashankar ,&nbsp;Nisarga Vinod Kumar ,&nbsp;K.M. Sangeeta","doi":"10.1016/j.jobcr.2025.10.006","DOIUrl":"10.1016/j.jobcr.2025.10.006","url":null,"abstract":"<div><h3>Introduction</h3><div>Artificial intelligence (AI) transforms dentistry and holds considerable promise for maxillofacial prosthodontics (MFP). Applications in imaging, computer-aided design and manufacturing (CAD/CAM), and additive manufacturing are improving diagnosis, treatment planning, and prosthetic rehabilitation for patients with craniofacial abnormalities. Despite advances in materials and digital workflows, challenges remain in achieving optimal accuracy, efficiency, and customisation in prosthetic design. The integration of AI in maxillofacial prosthodontics is still in its early stages. Currently, there is no review detailing the scope, trends, potential, and limitations of AI in this field. A scoping review is therefore necessary to consolidate existing evidence, identify knowledge gaps, and suggest directions for future research and clinical application. This review objective is to systematically map and analyse the current literature on AI in maxillofacial prosthodontics, focusing on its role in craniofacial rehabilitation.</div></div><div><h3>Methods</h3><div>This scoping review adhered to the methodological framework of Arksey and O'Malley (2005) and was guided by the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis (2020). Reporting complied with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) guidelines to ensure clarity and reproducibility. The review was registered with the Open Science Framework (registration number: <span><span>www.osf.io/3b9jr</span><svg><path></path></svg></span>). Electronic databases, including Medline via PubMed, Scopus, Cochrane Database, Science Direct, Google Scholar, and Semantic Scholar, were searched up to 7 June 2025. Full-text English articles containing the keywords “Artificial Intelligence and Maxillofacial Prosthodontics” and related terms were included.</div></div><div><h3>Results</h3><div>This scoping review included 35 articles from diverse geographic regions. The studies addressed several specific applications of AI in maxillofacial prosthodontics, including the production of implant-supported auricular prostheses, coloration of maxillofacial prostheses, evaluation of facial attractiveness in patients with clefts, capture of 3D impressions of cleft palates, identification of hypernasality, assessment of lip symmetry, and detection of teeth in cleft lip and palate cases<strong>.</strong></div></div><div><h3>Conclusion</h3><div>Artificial intelligence offers significant opportunities for maxillofacial prosthodontics, especially in imaging, digital design, and prosthesis production. Progress in this area requires interdisciplinary teamwork, large-scale clinical trials, and the development of standardized validation methods to ensure safe and effective clinical application.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1749-1766"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modulating cortical excitability through transcranial direct current stimulation combined with therapeutic exercise for craniofacial myofascial pain: Randomized controlled trial","authors":"Ramesh Chandra Patra,&nbsp;Deepti Agrawal Garg,&nbsp;A. Yashudas","doi":"10.1016/j.jobcr.2025.10.005","DOIUrl":"10.1016/j.jobcr.2025.10.005","url":null,"abstract":"<div><h3>Background</h3><div>Temporomandibular dysfunction (TMD) involves pain, motor impairment and psychological distress, often sustained by maladaptive central neuroplasticity. Transcranial direct current stimulation (tDCS) can modulate cortical excitability, enhance descending inhibitory control, and improve function, making it a promising adjunct to multimodal rehabilitation.</div></div><div><h3>Objective</h3><div>To examine the effects of tDCS combined with multimodal rehabilitation on pain, motor performance and psychosocial outcomes in TMD and to explore associated neurophysiological mechanism.</div></div><div><h3>Methods</h3><div>In this randomized, assessor-blinded trial, participants received either active tDCS plus rehabilitation or Sham tDCS plus rehabilitation. Assessments occurred at baseline, post-intervention (8 weeks) and at 3 and 6-month follow-up. Primary outcomes were EEG Alpha Modulation Index (AMI), Pressure pain threshold (PPT) and Jaw functional limitation scale (JFLS); secondary outcomes included neck disability index (NDI), patient global impression of change (PGIC) data were analyzed via repeated-measures ANOVA with intention-to-treat principles.</div></div><div><h3>Results</h3><div>Active tDCS with rehabilitation produced greater improvements than sham in all outcomes (p &lt; 0.001). EEG Alpha Modulation Index increased (<em>η</em>² = 0.24), indicating enhanced cortical excitability. PPT improved (<em>η</em>² = 0.22) showed significant and sustained functional gains. PGIC scores indicated large perceived improvement (r = 0.72), maintained at 6 months. No serious adverse events were reported. Minor events (headaches, tingling, skin irritation) occurred in &lt;10 % of sessions and were transient.</div></div><div><h3>Conclusion</h3><div>tDCS combined with therapeutic exercise safely enhances cortical excitability, pain modulation, function and patient outcomes in TMD, supporting Multimodal rehabilitation. Further research should refine protocals and confirm long-term benefits across populations.</div></div>","PeriodicalId":16609,"journal":{"name":"Journal of oral biology and craniofacial research","volume":"15 6","pages":"Pages 1731-1741"},"PeriodicalIF":0.0,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}