ABSTRACT Objective This study aimed to evaluate the hydration‐induced volumetric expansion and elasticity of collagen‐based soft tissue substitutes following exposure to blood using three‐dimensional (3D) analysis and high‐frequency ultrasonography (HFUS). Materials and Methods Seven commercially available collagen matrices were analyzed: Fibro‐Gide (6 mm and 3 mm), Mucogain (5 mm and 3 mm), Volumax, Collagen Graft II, and Mucograft. The investigation consisted of two experimental phases. In the in vitro phase, samples were immersed in stabilized porcine blood to assess absorption index (AI), 3D volumetric changes, and ultrasonographic (HFUS) parameters, including density and elasticity. In the ex vivo phase, matrices were placed beneath envelope flaps in porcine maxillae, with optical 3D scanning performed before and after blood saturation. Statistical analysis included generalized estimating equations for repeated measures and nonparametric testing, with significance set at α = 0.05. Results Volume stable matrices exhibited significantly greater absorption and volumetric expansion compared to non‐stable matrices. Fibro‐Gide 6 mm demonstrated the highest absorption index (17.0 ± 0.6 mg) and volumetric increase (268.4 ± 54.8 mm3), while Mucogain 5 mm showed the greatest vertical expansion (0.83 ± 0.08 mm). Non‐stable matrices displayed reduced expansion or partial collapse. A positive, though non‐significant, correlation was observed between density (HFUS) and volumetric expansion (r = 0.29, p = 0.535). Conclusions Volume‐stable collagen matrices show pronounced fluid uptake and expansion. Clinically, this effect should be considered, as excessive swelling may increase flap tension and compromise wound stability, necessitating careful trimming and adequate flap release.
ABSTRACT Objectives To evaluate the association between patient behavior and clinician emotional burden, perceived clinical performance, interaction difficulty, and decision‐making in implant dentistry. Material and Methods A cross‐sectional online questionnaire was distributed via individual invitations to 150 dental professionals involved in implant dentistry. A total of 127 clinicians completed the survey (response rate: 84.7%). The questionnaire assessed demographic and professional characteristics, clinicians’ perceptions of patient behavior, emotional responses, and the perceived impact of patient‐related factors on clinical performance and decision‐making. Composite domains for emotional burden and interaction difficulty were developed, and their internal consistency was assessed using Cronbach's α. Associations were evaluated using Spearman's correlation and linear regression analyses. Results More challenging patient behavior was significantly associated with increased emotional burden (ρ = 0.50, p < 0.001) and interaction difficulty (ρ = 0.46, p < 0.001). Similar findings were observed when patient behavior was compared with usual clinical cases (both p < 0.001). Procedural difficulty showed only weak or non‐significant associations with emotional burden and interaction difficulty. Emotional burden was strongly associated with interaction difficulty (ρ = 0.84, p < 0.001), avoidance of scheduling similar patients (ρ = 0.47, p < 0.001), and lower perceived clinical performance (ρ = 0.27, p = 0.002). Regression analyses confirmed that patient behavior was significantly associated with emotional burden (p < 0.001). Conclusions Patient behavior appears to be associated with clinician emotional burden and perceived clinical performance more strongly than case difficulty in implant dentistry.
ABSTRACT Objectives The aim was to evaluate how treatment supplied in public oral health care services is associated with the prevalence and incidence of periodontitis. Methods The data for this study comprised patients aged 20 years or older who had visited publicly funded oral health care services in the Wellbeing Services County of North Karelia (Siun sote) during the years 2021–2022 (N = 50,469). These patients were retrospectively followed in the patient register starting from 2014. Associations of treatments received during the follow‐up period (years 2014–2020) with ICD‐10 diagnoses of chronic and acute periodontitis at follow‐up (in 2021–2022) were analyzed by logistic regressions. Results The prevalence of diagnoses of both chronic and acute periodontitis in 2021–2022 was quite low. The number of years of having received different treatments in 2014–2020 was associated with the odds of being diagnosed with periodontitis in 2021–2022. In the case of chronic periodontitis, an increasing number of years with preventive and restorative treatments was particularly associated with lower prevalence and incidence. Conversely, an increasing number of years with periodontal treatments and tooth extractions, in addition to earlier diagnosis, were associated with higher prevalence and incidence. For acute periodontitis, in contrast, an increasing number of years with preventive care among all participants and periodontal treatment in incident cases appeared to slightly increase the odds, whereas more years of examinations and tooth extractions were associated with reduced odds. In addition, smoking and diagnosis of diabetes mellitus were associated with higher odds of periodontitis diagnosis. Conclusions While the association of treatments was similar for prevalence and incidence, it differed between chronic and acute periodontitis, highlighting their distinct pathophysiological profiles. Although preventive care is offered less frequently than other treatments and often for reasons unrelated to periodontal health, it shows a positive impact in reducing the risk of chronic but not acute periodontitis.
ABSTRACT Background Interest in antimicrobial surfaces for dental devices, such as aligners and brackets, is growing. These surfaces are designed to retard biofilm formation. One approach is to load essential oils (EOs) into the material. The focus of this study was the antibacterial, anti‐biofilm, and anti‐inflammatory activities of an EO solution. Methods The EO solution contained cinnamaldehyde, methyl salicylate, eucalyptol, limonene, and trans‐anethole dissolved in distilled water (dH2O) with polyglycerol‐4‐laurate/sabacate (basic solution). The positive control was 0.2% chlorhexidine digluconate (CHX). The antibacterial activity of the EO and the basic solution was tested against selected oral bacteria. Multispecies biofilms were formed on specimens of a cellulose‐based (test) material loaded with the EO solution or the basic solution, and on a polyurethane‐based (reference) material loaded with CHX. The level of IL‐8 released from gingival fibroblasts (GF) exposed to the solutions was measured. Results The EO solution clearly inhibited bacterial growth. However, the basic solution also acted as a growth inhibitor at higher concentrations. The EO solution reduced the metabolic activity of the formed biofilm. In addition, the basic solution decreased biofilm mass and colony‐forming unit counts. The basic solution was not inferior to CHX on the reference material. However, only the basic solution, not the EO solution, suppressed the release of IL‐8 from GF when stimulated with bacteria. Conclusion Applying essential oils (EOs) to surfaces may be an effective way to inhibit biofilm formation. The basic solution, which is polyglyceryl‐laurate‐based, exhibits anti‐biofilm and anti‐inflammatory properties. Further in vitro research should aim to gain a deeper understanding of the solubilizer's mechanisms and optimize the composition of the added essential oils before testing in clinical trials.
ABSTRACT Objectives To develop interpretable machine learning (ML) models for risk stratification of clinically meaningful anterior overbite decrease after stabilization splint therapy in temporomandibular disorder (TMD) patients, and to compare ML performance with conventional logistic regression. Materials and Methods This retrospective cohort included 87 TMD patients treated with stabilization splints, physical therapy, and pharmacologic therapy. Thirty‐five pre‐treatment cephalometric and demographic features were used. The primary outcome was overbite difference (OD = post‐treatment minus pre‐treatment overbite). For classification, patients were labeled as Decrease (OD < −0.5 mm; n = 37) versus Non‐Decrease (OD ≥ −0.5 mm; n = 50), combining No Change and Increase due to severe imbalance in the Increase subgroup. Six classifiers (logistic regression, random forest, extreme gradient boosting (XGBoost), Light Gradient Boosting Machine, support vector machine, k‐nearest neighbors) were evaluated using stratified fivefold cross‐validation and leave‐one‐out cross‐validation. Class imbalance was handled using class‐weighting and the Synthetic Minority Over‐sampling Technique (SMOTE) applied within the cross‐validation loop. For regression, nine algorithms were tested to predict continuous OD. Model explainability was assessed using SHapley Additive exPlanations (SHAP). Results In fivefold cross‐validation, XGBoost achieved the best discrimination (AUC = 0.734), outperforming logistic regression (AUC = 0.611). With SMOTE, the random forest achieved a comparable AUC (0.735) with improved sensitivity. Across regression models, cross‐validated R2 values were near‐zero or negative, indicating limited ability to predict the exact magnitude of overbite change from baseline variables alone. SHAP ranked pre‐treatment overbite, ramus height, upper occlusal plane to SN angle, sella‐to‐condylion distance, and condylar positional indices among the most influential predictors for classification. Conclusion Interpretable ML enables improved risk stratification for clinically meaningful anterior overbite decrease after stabilization splint therapy in TMD patients, whereas predicting the exact magnitude of overbite change remains limited using pre‐treatment cephalometrics alone. These models may support individualized counseling and closer monitoring for patients at higher risk of open‐bite development during splint therapy.
