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mHealth for Oral Care in Aging: A Narrative Review of Mobile Applications for Older Adults and Caregivers
Mireya Martínez-García
,Guadalupe Gutiérrez-Esparza
,S. Aida Borges-Yañez
,Enrique Hernández-Lemus
Posted: 16 April 2026
Influence of the Dentin Cleaning and Conditioning Methods on Shear Strength
Thomas Klinke
,Ghassan Al Shalak
,Alexandra Amlang
,Bernd Kordass
Posted: 15 April 2026
Comparative Laboratory Measurement of Occlusal Contacts Registered by Articulating Paper and the T-Scan and Medit i500 Systems
Svetlana Angelova
Posted: 15 April 2026
Maxillary Forward Translation and Mandibular Immediate Shift Following 3D-Guided Midpalatal Piezocorticotomy Assisted MARPE in Adults: Lateral Cephalometric Pre- and Post-Treatment Study
Svitlana Koval
,Daria Chepanova
,Nika Stepanoff
,Andrii Babii
Posted: 14 April 2026
Adjunctive Xylitol Therapy Drives Targeted Oral Microbiome Modulation Without Disrupting Community Structure in Stunted Children: A 16S rRNA Sequencing Study
Gian Ernesto
,Ilmiawati Ilmiawati
,Desmawati Desmawati
,Hirowati Ali
,Nila Kasuma
Posted: 14 April 2026
GF-Predictability for Dental Implants (GF-PreDImp): A Multidomain Predictive Model for Dental Implant Success – Development, Structure, and Clinical Application
Gustavo Vicentis Oliveira Fernandes
,Juliana Campos Hasse Fernandes
,Sérgio A. Gehrke
Dental implant therapy demonstrates high long-term survival; however, biological, behavioral, and technical complications remain prevalent. The objective of this study was to introduce GF-Predictability for Dental Implants (GF-PreDImp), the first multidomain predictive tool in the literature, designed to quantify implant success predictability through a structured, evidence-based scoring system. The model integrates six domains: Biological, Behavioral, Hard tissue, Soft tissue, Implant, and Prosthetic, approaching systemic, behavioral, anatomical, surgical, and prosthetic variables into a 100-point composite index. The Biological/Systemic point (20 points) involves diabetes (HbA1c), bisphosphonates, head and neck radiation, cardiovascular disease, osteoporosis, and immunosuppression; the Behavioral/External topic (20 points) approaches post-implant smoking, oral hygiene, plaque/calculus index, brushing performance, alcohol usage, and patient’s compliance; the Hard Tissue (20 points) analyzed bone quality (densities: D1–D4), bone quantity, arch position, guided-bone regeneration (GBR) need, sinus lift, cone beam computed tomography (CBCT) height/width; the Soft Tissue evolution (15 points) observes keratinized mucosa width (KMW), periodontal history, gingival phenotype, bleeding on probing (BoP), and probing depth (PD); the Implant Parameters topic (15 points) assessed tooth position, loading timing, primary stability (ISQ), length/diameter, and surface treatment; and the last point analyzed, Prosthetic/Surgical (10 points), appraisal bruxism characteristic, occlusal contacts, crown-to-implant ratio, cantilever, surgeon experience, and antibiotic protocol. The final GF-PreDImp score could be excellent (≥ 85), good (70 – 84), moderate to guarded (55-69), guarded to high risk (40-54), and poor (<40). Results: Predictors were derived from literature on implant failure, peri-implant disease, and risk assessment. The tool generates dynamic visual outputs, including radar charts and domain-specific scores, enabling real-time clinical interpretation. Each domain can achieve up to 100%, and the average results predict the predictability of dental implant therapy. The final screen of the GF-PreDImp outcome presents a summary of the worst areas to clarify possible risks for clinicians and patients. The graphic and result can be printed for electronic filing and/or shown and given to the patient. The GF-PreDImp system can provide a comprehensive framework for individualized risk stratification and treatment optimization. Its implementation can improve clinical decision-making and enhance long-term implant outcomes. Further clinical assessments must be done to confirm the findings in future studies.
Dental implant therapy demonstrates high long-term survival; however, biological, behavioral, and technical complications remain prevalent. The objective of this study was to introduce GF-Predictability for Dental Implants (GF-PreDImp), the first multidomain predictive tool in the literature, designed to quantify implant success predictability through a structured, evidence-based scoring system. The model integrates six domains: Biological, Behavioral, Hard tissue, Soft tissue, Implant, and Prosthetic, approaching systemic, behavioral, anatomical, surgical, and prosthetic variables into a 100-point composite index. The Biological/Systemic point (20 points) involves diabetes (HbA1c), bisphosphonates, head and neck radiation, cardiovascular disease, osteoporosis, and immunosuppression; the Behavioral/External topic (20 points) approaches post-implant smoking, oral hygiene, plaque/calculus index, brushing performance, alcohol usage, and patient’s compliance; the Hard Tissue (20 points) analyzed bone quality (densities: D1–D4), bone quantity, arch position, guided-bone regeneration (GBR) need, sinus lift, cone beam computed tomography (CBCT) height/width; the Soft Tissue evolution (15 points) observes keratinized mucosa width (KMW), periodontal history, gingival phenotype, bleeding on probing (BoP), and probing depth (PD); the Implant Parameters topic (15 points) assessed tooth position, loading timing, primary stability (ISQ), length/diameter, and surface treatment; and the last point analyzed, Prosthetic/Surgical (10 points), appraisal bruxism characteristic, occlusal contacts, crown-to-implant ratio, cantilever, surgeon experience, and antibiotic protocol. The final GF-PreDImp score could be excellent (≥ 85), good (70 – 84), moderate to guarded (55-69), guarded to high risk (40-54), and poor (<40). Results: Predictors were derived from literature on implant failure, peri-implant disease, and risk assessment. The tool generates dynamic visual outputs, including radar charts and domain-specific scores, enabling real-time clinical interpretation. Each domain can achieve up to 100%, and the average results predict the predictability of dental implant therapy. The final screen of the GF-PreDImp outcome presents a summary of the worst areas to clarify possible risks for clinicians and patients. The graphic and result can be printed for electronic filing and/or shown and given to the patient. The GF-PreDImp system can provide a comprehensive framework for individualized risk stratification and treatment optimization. Its implementation can improve clinical decision-making and enhance long-term implant outcomes. Further clinical assessments must be done to confirm the findings in future studies.
Posted: 13 April 2026
Influence of Tooth Morphology on Local Mesh Density Distribution in Intraoral Scanner-Derived STL Models of Selected Maxillary Teeth
Dubravka Knezović Zlatarić
,Maja Žagar
,Egon Neskusil
,Daren Dreo Bračun
,Robert Ćelić
Background/Objectives: The quality of intraoral scanner-derived digital models depends not only on deviation-based accuracy, but also on how scanned surfaces are reconstructed into a polygonal mesh. The aim of this prospective within-subject observational study was to evaluate whether tooth morphology influences local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. Methods: Twenty participants underwent maxillary intraoral scanning using a Medit i900 wired intraoral scanner under standardized clinical conditions. For each participant, the buccal surfaces of the maxillary right central incisor (11), canine (13), first premolar (15), and first molar (16) were selected as regions of interest. Surface area (A), number of vertices (V), and number of faces (F) were recorded, and the surface-normalized mesh density parameters vertices per unit area (V/A) and faces per unit area (F/A) were calculated. Comparisons among tooth types were performed using repeated-measures analysis of variance (ANOVA) with Bonferroni post hoc correction. Results: Significant differences were found among tooth types for both V/A and F/A (p < 0.001). Mean V/A values were 18.2 ± 1.9 for tooth 11, 19.8 ± 1.4 for tooth 13, 23.8 ± 1.7 for tooth 15, and 22.9 ± 2.0 vertices/mm² for tooth 16. Mean F/A values were 34.3 ± 3.6, 37.5 ± 2.7, 44.4 ± 3.3, and 42.9 ± 3.8 faces/mm², respectively. Post hoc comparisons showed significant differences between teeth 11 and 13, 11 and 15, 11 and 16, 13 and 15, and 13 and 16, whereas no significant difference was observed between teeth 15 and 16. Conclusions: Tooth morphology significantly influenced local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. These findings suggest that local anatomical form affects STL mesh reconstruction under standardized in vivo scanning conditions and support local mesh density analysis as a useful complementary approach to conventional deviation-based digital assessment.
Background/Objectives: The quality of intraoral scanner-derived digital models depends not only on deviation-based accuracy, but also on how scanned surfaces are reconstructed into a polygonal mesh. The aim of this prospective within-subject observational study was to evaluate whether tooth morphology influences local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. Methods: Twenty participants underwent maxillary intraoral scanning using a Medit i900 wired intraoral scanner under standardized clinical conditions. For each participant, the buccal surfaces of the maxillary right central incisor (11), canine (13), first premolar (15), and first molar (16) were selected as regions of interest. Surface area (A), number of vertices (V), and number of faces (F) were recorded, and the surface-normalized mesh density parameters vertices per unit area (V/A) and faces per unit area (F/A) were calculated. Comparisons among tooth types were performed using repeated-measures analysis of variance (ANOVA) with Bonferroni post hoc correction. Results: Significant differences were found among tooth types for both V/A and F/A (p < 0.001). Mean V/A values were 18.2 ± 1.9 for tooth 11, 19.8 ± 1.4 for tooth 13, 23.8 ± 1.7 for tooth 15, and 22.9 ± 2.0 vertices/mm² for tooth 16. Mean F/A values were 34.3 ± 3.6, 37.5 ± 2.7, 44.4 ± 3.3, and 42.9 ± 3.8 faces/mm², respectively. Post hoc comparisons showed significant differences between teeth 11 and 13, 11 and 15, 11 and 16, 13 and 15, and 13 and 16, whereas no significant difference was observed between teeth 15 and 16. Conclusions: Tooth morphology significantly influenced local mesh density distribution in intraoral scanner-derived STL models of selected maxillary teeth. These findings suggest that local anatomical form affects STL mesh reconstruction under standardized in vivo scanning conditions and support local mesh density analysis as a useful complementary approach to conventional deviation-based digital assessment.
Posted: 09 April 2026
Effects of Intraoperative Photobiomodulation on Osteogenic Differentiation of Human Jaw Bone Explants Harvested During Cyst Surgery: A Paired Ex Vivo Translational Experimental Study
Grigore Ioan Vlad
,Păcurar Mariana
,Ovidiu Pop
,Sorana Maria Bucur
,Elina Teodorescu
,Anca Oana Dragomirescu
,Ștefan Milicescu
,Alina Ormenișan
Posted: 09 April 2026
Detecting Residual Root Canal Filling Material After Retreatment: Cone-Beam Computed Tomography and Digital Microscopy Compared with Micro-Computed Tomography
Detecting Residual Root Canal Filling Material After Retreatment: Cone-Beam Computed Tomography and Digital Microscopy Compared with Micro-Computed Tomography
Mohamad Alouda
,Samar Akil
,Mohammad Tamer Abbara
,Ammar Eid
,Imad-Addin Almasri
,Yasser Alsayed Tolibah
,Ziad D. Baghdadi
Posted: 08 April 2026
Prevalence of Dental Anxiety and the Influence of Various Factors in Children Age 12 to 18 Years in Zagreb, Croatia
Lucija Koturić Čabraja
,Walter Dukić
,Matea Lapas Barisic
Posted: 06 April 2026
Concept of Transmucosal Biological Complex and Its Clinical Implications in Dental Implantology
Zhao Yang
Posted: 31 March 2026
Analysis of the State of Psycho-Emotional Adaptation of Patients with Various Forms of Oncological Diseases of the Maxillofacial Region at Certain Stages of Their Treatment and Rehabilitation Process
Oleksandr Belikov
,Oleksandra Roshchuk
,Natalia Belikova
,Liudmyla Belikova
,Maksym Bernik
Posted: 24 March 2026
Dose-Dependent Osteoinduction by rhBMP-2-Loaded β-Tricalcium Phosphate Scaffolds in Rabbit Critical-Sized Calvarial Defects: Histological, Histomorphometric, CD31 Immunohistochemical Evaluation
Solaf Abdulqadir Mustafa
,Chenar Anwar Mohammad
,Rafal Abdulrazaq Alrawi
Posted: 23 March 2026
Informed Consent in Artificial Intelligence-Augmented Dentistry: Clinical Care, Research, and the Dentist–Patient–AI Relationship: A Scoping Review
Tamara Mihut
,Corina Marilena Cristache
,Luminita Oancea
,Victor Nimigean
Posted: 23 March 2026
Tooth Shape as a Psychomorphological Indicator of Personality: Clinical Observations
Aleksandra Pecheva
Background/Objectives: Harmony between tooth morphology and facial features is a key factor in aesthetic dentistry. The sufficient smile design requires the integration of functional, biological aspects, individual identity and personality traits. The present study investigated the relationship between the shape of teeth and personality characteristics. Methods: A cross-sectional observational study was conducted among 142 participants. Data were collected through a standardized questionnaire, including demographic indicators, assessment of the shape of the teeth and personality characteristics according to two models: type 1 (choleric, sanguine, melancholic, phlegmatic) and type 2 (extraversion, openness, conscientiousness, focus on others, neuroticism). Pearson’s χ² test and Cramer’s coefficient (V) were applied to analyze the dependencies. Results: A statistically significant relationship was found between tooth shape and personality type 1 (χ²(9)=61.96, p<0.001; V=0.38), with a medium to strong effect. The oval shape was associated mainly with melancholic temperament, the triangular shape with sanguine, the rectangular shape with choleric, and the square shape with melancholic and phlegmatic types. A significant relationship was also observed between tooth shape and personality type 2 (χ²(12)=41.82, p<0.001; V=0.31), with a medium effect, with the different morphological shapes showing specific distribution profiles according to personality traits. No statistically significant relationship was found between the two personality models (χ²(12)=18.10, p=0.113). Conclusions: The shape of the frontal teeth is associated with temperament-based and trait-oriented personality characteristics, with the relationship being stronger in the classical temperament typology. This supports the hypothesis that dental morphology may reflect biologically determined aspects of personality and be relevant to individualized aesthetic dental design.
Background/Objectives: Harmony between tooth morphology and facial features is a key factor in aesthetic dentistry. The sufficient smile design requires the integration of functional, biological aspects, individual identity and personality traits. The present study investigated the relationship between the shape of teeth and personality characteristics. Methods: A cross-sectional observational study was conducted among 142 participants. Data were collected through a standardized questionnaire, including demographic indicators, assessment of the shape of the teeth and personality characteristics according to two models: type 1 (choleric, sanguine, melancholic, phlegmatic) and type 2 (extraversion, openness, conscientiousness, focus on others, neuroticism). Pearson’s χ² test and Cramer’s coefficient (V) were applied to analyze the dependencies. Results: A statistically significant relationship was found between tooth shape and personality type 1 (χ²(9)=61.96, p<0.001; V=0.38), with a medium to strong effect. The oval shape was associated mainly with melancholic temperament, the triangular shape with sanguine, the rectangular shape with choleric, and the square shape with melancholic and phlegmatic types. A significant relationship was also observed between tooth shape and personality type 2 (χ²(12)=41.82, p<0.001; V=0.31), with a medium effect, with the different morphological shapes showing specific distribution profiles according to personality traits. No statistically significant relationship was found between the two personality models (χ²(12)=18.10, p=0.113). Conclusions: The shape of the frontal teeth is associated with temperament-based and trait-oriented personality characteristics, with the relationship being stronger in the classical temperament typology. This supports the hypothesis that dental morphology may reflect biologically determined aspects of personality and be relevant to individualized aesthetic dental design.
Posted: 23 March 2026
Management of Deep Caries in Primary Molars: A Comparative Analysis of the American, European, and International Guidelines
Bashayer Ayed Alhersh
Posted: 18 March 2026
Observational Study of the Association Between Oral Helicobacter pylori, Fixed Orthodontic Appliances, and Gastric Cancer Risk
Ioana Maria Crișan
,Alex Crețu
,Sorana-Maria Bucur
Posted: 17 March 2026
The Adjunctive Role of Probiotics in Periodontal Therapy: A Narrative Review
The Adjunctive Role of Probiotics in Periodontal Therapy: A Narrative Review
Natalia de Campos Kajimoto
,Cristhiam de Jesus Hernandez Matinez
,Peter Michael Loomer
,Yvonne de Paiva Buischi
,Ana Carolina Punhagui Hernandes
Posted: 13 March 2026
Ultrasound-Guided Intra-Articular Infiltration of Hyaluronic Acid, Lidocaine, and Cortisone in Patients with Temporomandibular Disorders (TMD): Our Experience
Giuseppe Messina
,Francesco Mantia
,Pietro Cataldo
,Angelo Iovane
Posted: 12 March 2026
Risk Assessment of Transcrestal Maxillary Sinus Floor Elevation: A Narrative Review
Zhao Yang
Posted: 12 March 2026
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