Development of prognostic indicators using classification and regression trees for survival

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

Treatment planning algorithm for patients with a terminal dentition

This article discusses the variables that affect the diagnostic process in patients with a compromised dentition and addresses the clinical decision of whether to extract or maintain teeth. A decision tree algorithm is proposed to guide clinicians in planning complete arch rehabilitations.

LONG-TERM OUTCOMES OF IMPLANT PLACEMENT VERSUS TOOTH PRESERVATION IN PERIODONTALLY COMPROMISED TEETH MAY BE COMPARABLE

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION

Sarafidou K, Lazaridi I, Gotsis S, Kirmanidou Y, Vasilaki D, Hirayama H, Michalakis K. Tooth preservation vs. extraction and implant placement in periodontally compromised patients: A systematic review and analysis of studies. J Prosthodont. 2022 Oct;31(8):e87-e99. doi:10.1111/jopr.13560. Epub 2022 Aug 2. PMID: 35794083 SOURCE OF FUNDING: No external funding was received for this research.

TYPE OF STUDY/DESIGN

Systematic review (without meta-analysis).

Probing pocket depth reduction after non-surgical periodontal therapy: Tooth-related factors

BACKGROUND

To investigate tooth-related factors that influence the reduction of probing pocket depths (PPD) after non-surgical periodontal therapy (NST).

METHODS

Seven hundred forty-six patients with a total of 16,825 teeth were included and retrospectively analyzed. PPD reduction after NST was correlated with the tooth-related factors; tooth type, number of roots, furcation involvement, vitality, mobility, and type of restoration; using logistic multilevel regression for statistical analysis.

RESULTS

NST was able to reduce probing depth overall stratified probing depths (1.20 ± 1.51 mm, p ≤ 0.001). The reduction was significantly higher at teeth with higher probing depths at baseline. At pockets with PPD ≥ 6 mm, PPD remains high after NST. Tooth type, number of roots, furcation involvement, vitality, mobility, and type of restoration are significantly and independently associated with the rate of pocket closure.

CONCLUSIONS

The tooth-related factors: tooth type, number of roots, furcation involvement, vitality, mobility, and type of restoration had a significant and clinically relevant influence on phase I and II therapy. Considering these factors in advance may enhance the prediction of sites not responding adequately and the potential need for additional treatment, such as re-instrumentation or periodontal surgery, to ultimately achieve the therapy end points.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme

Objective

To compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients.

Methods

131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index.

Results

After the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560).

Conclusion

This study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

The effect of bromelain in periodontal surgery: a double-blind randomized placebo-controlled trial

BACKGROUND

Periodontitis is a persistent inflammatory condition. Eliminating the infection and reducing its risk factors are the first steps in treating periodontitis. When the anti-infective therapy is complete, there may still be deep periodontal pockets and prolonged inflammation. Surgical pocket reduction or elimination is indicated under these circumstances. We aimed to evaluate the effect of bromelain on bleeding on probing (BOP), gingival index (GI), and plaque index (PI) after pocket elimination surgery.

METHODS

This double-blind randomized placebo-controlled trial included 28 candidates for pocket elimination surgery referred to the private office of a periodontist in Bandar Abbas, Iran, from April 18 to August 18, 2021. Patients' general characteristics, such as age and sex, were recorded. Additionally, periodontal indices including BOP, PI, GI, and pocket probing depth (PPD) were evaluated in all subjects. All patients underwent pocket elimination surgery. Afterwards, they were randomized into two groups. The first group received 500 mg Anaheal (bromelain) capsules twice a day before meal for one week. The second group received placebo, prepared in similar shape and color by the same pharmaceutical company. BOP, PI, GI, and PPD were assessed four weeks after completion of the treatment course (five weeks after surgery).

RESULTS

Four weeks after intervention, BOP was significantly lower with Anaheal compared to placebo (0% vs. 35.7%, P = 0.014). However, there was no significant difference in GI between groups (P = 0.120). Mean PI was lower (17.71 ± 2.12 vs. 18.28 ± 2.49) and mean PPD higher (3.10 ± 0.71 vs. 2.64 ± 0.45) in the Anaheal group, but the differences did not reach statistically significant levels (P = 0.520 and P = 0.051, respectively).

CONCLUSIONS

One-week treatment with Anaheal at a dose of 1 g/d after pocket elimination surgery resulted in significantly lower BOP compared to placebo.

TRIAL REGISTRATION

Iranian Registry of Clinical Trials (IRCT), IRCT20201106049289N1. Registered 06/04/2021. Registered prospectively, https://www.irct.ir/trial/52181 .

Application of machine learning in the prediction of deficient mismatch repair in patients with colorectal cancer based on routine preoperative characterization

Simple summary

Detecting deficient mismatch repair (dMMR) in patients with colorectal cancer is essential for clinical decision-making, including evaluation of prognosis, guidance of adjuvant chemotherapy and immunotherapy, and primary screening for Lynch syndrome. However, outside of tertiary care centers, existing detection methods are not widely disseminated and highly depend on the experienced pathologist. Therefore, it is of great clinical significance to develop a broadly accessible and low-cost tool for dMMR prediction, particularly prior to surgery. In this study, we developed a convenient and reliable model for predicting dMMR status in CRC patients on routine preoperative characterization utilizing multiple machine learning algorithms. This model will work as an automated screening tool for identifying patients suitable for mismatch repair testing and consequently for improving the detection rate of dMMR, while reducing unnecessary labor and cost in patients with proficient mismatch repair.

Background

Deficient mismatch repair (dMMR) indicates a sustained anti-tumor immune response and has a favorable prognosis in patients with colorectal cancer (CRC). Although all CRC patients are recommended to undergo dMMR testing after surgery, current diagnostic approaches are not available for all country hospitals and patients. Therefore, efficient and low-cost predictive models for dMMR, especially for preoperative evaluations, are warranted.

Methods

A large scale of 5596 CRC patients who underwent surgical resection and mismatch repair testing were enrolled and randomly divided into training and validation cohorts. The clinical features exploited for predicting dMMR comprised the demographic characteristics, preoperative laboratory data, and tumor burden information. Machine learning (ML) methods involving eight basic algorithms, ensemble learning methods, and fusion algorithms were adopted with 10-fold cross-validation, and their performance was evaluated based on the area under the receiver operating characteristic curve (AUC) and calibration curves. The clinical net benefits were assessed using a decision curve analysis (DCA), and a nomogram was developed to facilitate model clinical practicality.

Results

All models achieved an AUC of nearly 0.80 in the validation cohort, with the stacking model exhibiting the best performance (AUC = 0.832). Logistical DCA revealed that the stacking model yielded more clinical net benefits than the conventional regression models. In the subgroup analysis, the stacking model also predicted dMMR regardless of the clinical stage. The nomogram showed a favorable consistence with the actual outcome in the calibration curve.

Conclusion

With the aid of ML algorithms, we developed a novel and robust model for predicting dMMR in CRC patients with satisfactory discriminative performance and designed a user-friendly and convenient nomogram.

Evaluation of furcation involvement with diagnostic imaging methods: a systematic review

OBJECTIVE

Multirooted teeth respond less favorably to non-surgical periodontal treatment and long-term tooth prognosis is influenced by the degree of furcation involvement (FI). Therapeutic strategy for multirooted teeth is essentially based on accurate diagnosis of the FI. The aim of this systematic review is to evaluate the accuracy of the different furcation assessment methods and to determine if radiographic help is needed to determine early stage of FI.

METHODS

Electronic databases were searched up to March 2021. Comparative studies describing the reliability of different clinical and/or radiological furcation assessment methods were identified.

RESULTS

A total of 22 studies comparing at least 2 furcation assessment methods, among which 15 retrospective studies, 5 prospective studies, 1 randomized controlled trial and 1 case series, were included in this review. The reliability of cone beam CT (CBCT), intraoral radiographs (IOs), orthopantomograms (OPGs) and MRI to identify FI was evaluated. Using OFS as a reference for FI detection and diagnosis, agreement ranged from 43.3 to 63% for OPG, 38.7 to 83.1% for IO and 82.4 to 84% for CBCT. The validity of the measurements was mainly influenced by the location of the furcation entrance. For radiological diagnosis, CBCT displayed the closest agreement with OFS while the accuracy of IO and OPG showed modest agreement and were influenced by the examiner's experience.

CONCLUSION

Altogether, it appears that the use of IO, OPG or CBCT allows detection of FI but could not be considered as gold-standard techniques.

Compromised Teeth Preserve or Extract: A Review of the Literature

Multiple systems and associated factors have been described in the literature to assess the prognosis of teeth with periodontal disease. Nowadays there is a tendency among clinicians to consider implants as the best solution after tooth extraction, in cases of teeth with a questionable prognosis. However, the value of the natural tooth must be considered, as the proprioception of the periodontal ligament is preserved, and it adapts to stress during functional loads. We first review the literature focusing on analyzing the factors that should guide decision-making to maintain or extract a tooth with a compromised periodontium. Then, we propose a schematic diagram of prognostic indicators to reflect the main factors to consider and the survival rate that each one represents when preserving or extracting a tooth.

Informed Consent from a Historical, Societal, Ethical, Legal, and Practical Perspective

Informed consent is often perceived as a regulatory obligation without recognizing its educational potential in the dynamic provider/patient relationship. This article discusses the complex interaction of ethics, society, and law through a historical and practical perspective. The purpose is to provide general dentists and specialists with a comprehensive understanding of the complexity and practical dimensions of informed consent.

Effect of night dentures on tooth mobility in denture wearers with sleep bruxism: a pilot randomized controlled trial

PURPOSE

The purpose of this study was to investigate the inhibitory effect of night denture (ND) placement on abutment tooth mobility in removable partial denture (RPD) wearers with sleep bruxism (SB).

METHODS

The participants were selected from RPD wearers diagnosed with SB using BiteStrip® and randomly allocated to one of the following groups: with ND placement or without ND placement (intervention or control group, respectively). The mobility of the RPD abutment tooth was measured using Periotest® at baseline (T0) and at 1 (T1), 3 (T3), and 6 months (T6). BiteStrip® scores were recorded at T0 and T6. Intragroup comparisons of the Periotest® value (PTV) were performed using analysis of covariance. Intergroup comparisons of PTV and BiteStrip® scores were performed using the linear mixed model and Wilcoxon signed-rank test, respectively. Intragroup comparisons of BiteStrip® scores were performed using the Mann-Whitney U test.

RESULTS

At T6, the PTV in the intervention group was significantly lower than that in the control group (p=0.004). PTV was significantly lower at T6 than that at T0 in the intervention group (p=0.030), while PTV was significantly higher at T6 than that at T0 in the control group (p=0.007). There was no significant difference in the BiteStrip® score between the two groups. The BiteStrip® score at T6 was significantly lower than that at T0 only in the intervention group (p=0.011).

CONCLUSION

ND placement might inhibit the increase in mobility of RPD abutment teeth and be effective in protecting them in RPD wearers with SB.

External validation and comparison of the predictive performance of 10 different tooth-level prognostic systems

AIM

Tooth-level prognostic systems can be used for treatment planning and risk assessment. This retrospective longitudinal study aimed to evaluate the prognostic performance of 10 different tooth-level risk assessment systems in terms of their ability to predict periodontal-related tooth loss (TLP).

MATERIALS AND METHODS

Data were retrieved retrospectively from patients who received surgical and non-surgical periodontal treatment. Data on medical history and smoking status at baseline and the last maintenance visit were collected. Ten tooth-level prognostic systems were compared using both univariate and multivariate Cox proportional hazard regression models to analyse the prognostic capability of each system for predicting TLP risk.

RESULTS

One-hundred and forty-eight patients with 3787 teeth, followed-up for a mean period of 26.5 ± 7.4 years, were evaluated according to 10 different tooth-level prognostic systems, making up a total of 37,870 individual measurements. All compared prognostic systems were able to stratify the risk of TLP at baseline when different classes of association were compared. After controlling for maintenance, age, and gender, all systems exhibited excellent predictive capacity for TLP with no system scoring a Harrell's C-index less than 0.925.

CONCLUSIONS

All tooth-level prognostic systems displayed excellent predictive capability for TLP. Overall, the Miller and McEntire system may have shown the best discrimination and model fit, followed by the Nunn et al. system.

Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Cervical cancer as a common gynecological malignancy threatens the health and lives of women. Resistance to radiotherapy is the primary cause of treatment failure and is mainly related to difference in the inherent vulnerability of tumors after radiotherapy. Here, we investigated signature genes associated with poor response to radiotherapy by analyzing an independent cervical cancer dataset from the Gene Expression Omnibus, including pre-irradiation and mid-irradiation information. A total of 316 differentially expressed genes were significantly identified. The correlations between these genes were investigated through the Pearson correlation analysis. Subsequently, random forest model was used in determining cancer-related genes, and all genes were ranked by random forest scoring. The top 30 candidate genes were selected for uncovering their biological functions. Functional enrichment analysis revealed that the biological functions chiefly enriched in tumor immune responses, such as cellular defense response, negative regulation of immune system process, T cell activation, neutrophil activation involved in immune response, regulation of antigen processing and presentation, and peptidyl-tyrosine autophosphorylation. Finally, the top 30 genes were screened and analyzed through literature verification. After validation, 10 genes (KLRK1, LCK, KIF20A, CD247, FASLG, CD163, ZAP70, CD8B, ZNF683, and F10) were to our objective. Overall, the present research confirmed that integrated bioinformatics methods can contribute to the understanding of the molecular mechanisms and potential therapeutic targets underlying radiotherapy resistance in cervical cancer.

Early Tooth Loss after Periodontal Diagnosis: Development and Validation of a Clinical Decision Model

The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at the university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% confidence interval (95% CI): 0.629–0.989) for the validation sample and 0.920 (95% CI: 0.891–0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.

Clinical decision support model for tooth extraction therapy derived from electronic dental records

STATEMENT OF PROBLEM

Tooth extraction therapy serves as a key initial step in many prosthodontic treatment plans. Dentists must make an appropriate decision on the tooth extraction therapy considering multiple determinants and whether a clinical decision support (CDS) model might help.

PURPOSE

The purpose of this retrospective records study was to construct a CDS model to predict tooth extraction therapy in clinical situations by using electronic dental records (EDRs).

MATERIAL AND METHODS

The cohort involved 4135 deidentified EDRs of 3559 patients from the database of a prosthodontics department. Knowledge-based algorithms were first proposed to convert raw data from EDRs into structured data for feature extraction. Redundant features were filtered by a recursive feature-elimination method. The tooth extraction problem was then modeled alternatively as a binary or triple classification problem to be solved by 5 machine learning algorithms. Five machine learning algorithms within each model were compared, as well as the efficiency between 2 models. In addition, the proposed CDS was verified by 2 prosthodontists.

RESULTS

The triple classification model outperformed the binary model with the F1 score of the Extreme Gradient Boost (XGBoost) algorithm as 0.856 and 0.847, respectively. The XGBoost outperformed the other 4 algorithms. The accuracy, precision, and recall of the XGBoost algorithm were 0.962, 0.865, and 0.830 in the binary classification and 0.924, 0.879, and 0.836 in the triple classification, respectively. The performance of the 2 prosthodontists was inferior to the models.

CONCLUSIONS

The CDS model for tooth extraction therapy achieved high performance in terms of decision-making derived from EDRs.

The efficacy of pocket elimination/reduction compared to access flap surgery: A systematic review and meta-analysis

AIM

To assess the efficacy and adverse effects of resective surgery compared to access flap in patients with periodontitis.

METHODS

Randomized controlled trials with a follow-up ≥6 months were identified in ten databases. Screening, data extraction, and quality assessment were conducted by two reviewers. The primary outcome was probing pocket depth, and the main secondary outcome was clinical attachment level. Data on adverse events were collected. Meta-analysis was used to synthesize the findings of trials.

RESULTS

A total of 880 publications were identified. Fourteen publications from nine clinical trials met the inclusion criteria and were included for analysis. Meta-analysis was carried out using all available results. The results indicated superior pocket depth reduction following resective surgery compared to access flap after 6-12 months of follow-up (weighted mean difference 0.47 mm; confidence interval 0.7-0.24; p = .010). After 36-60 months of follow-up, no differences were found between the two treatments in pocket depth and attachment level. The prevalence of adverse effects was not different between the groups. Post-operative recession tended to be more severe for the resective approaches.

CONCLUSION

Resective surgical approach was superior to access flap in reducing pocket depth 6-12 months post-surgery, while no differences between the two modalities were found at 36-60 months of follow-up.

The correspondence of 3D supporting bone loss and crown-to-root ratio to periodontitis classification

AIM

Assessing the application of three-dimensional clinical attachment loss (3D-CAL), 3D supporting bone loss (3D-SBL), supracrestal tissue attachment (STA), and crown-to-root ratio (CRR) in evaluating the 2017 periodontitis classification.

MATERIALS AND METHODS

We analysed ninety single-rooted human premolars with micro-computed tomography. The amount of 3D-SBL, linear radiographic bone loss (RBL), and CRR corresponding to various periodontitis stages as well as the statistical significance was investigated.

RESULTS

From a 3D perspective, the premolars with a 21% of 3D-SBL at 2.0 mm coronal root length (RL) and 15% RBL corresponded to the periodontitis stage I. Premolars with a 44% of 3D-SBL at coronal 4.2-4.4 mm RL and 33% RBL accorded with the periodontitis stage II. Excluding the consideration of STA, CRR = 5:6 and 4:3 were associated with the levels at 15% and 33% RBL, respectively.

CONCLUSIONS

A greater percentage of 3D-CAL than that of 2D-CAL is significant at evaluated levels. It is feasible to correlate the 3D-SBL, 3D-CAL, and STA parameters to evaluate the stages of periodontitis severity. However, the current use of RBL and CAL as applied for staging in the 2017 classification might be inconsistent with the evaluated premolar roots length, when STA dimensions are considered.

Influence of retainer and major connector designs of removable partial dentures on the stabilization of mobile teeth: A preliminary study

The purpose of this study was to investigate the influence of retainers and major connector designs on the stabilization of remaining mobile teeth using removable partial dentures (RPDs). We prepared experimental RPDs with several retainer designs and major connectors for lower Kennedy class I models. The simulated RPD insertion and removal test was conducted and retentive force and mobility of mobile remaining teeth with and without RPD placement were measured throughout a simulation test. Regardless of reduction of retentive force, the placement of RPDs using cast clasps and/or lingual plates resulted in reduced mobility of the remaining teeth than use of wrought wire clasps and/or lingual bars. The results suggested that cast clasps and lingual plates are effective for the stabilization of mobile, remaining teeth. Additionally, the stabilizing effect of RPD on abutment teeth was not diminished, despite decreases in retentive force.

The Effect of Crown Lengthening on the Outcome of Endodontically Treated Posterior Teeth: 10-year Survival Analysis

INTRODUCTION

The purpose of this study was to investigate the effect of a crown lengthening (CL) procedure and the crown-root ratio after CL on the long-term survival of endodontically treated teeth (ETT).

METHODS

Permanent posterior teeth with opposing dentition that had received adequate nonsurgical root canal treatment (NSRCT) and a full-coverage crown between January 1, 2006, and January 1, 2016 were included in this retrospective study. The data collected included dates of the NSRCT, time of extraction if extracted, age, sex, location, the crown-root ratio after CL, and the presence of a lesion. All included ETT were divided into 2 groups: RESULTS: 5-year survival rates of ETT in the control and CL groups were 88.6% and 82.2%, respectively (P > .05). The 10-year survival rates of ETT in the control and CL groups were 74.5% and 51%, respectively (P < .05). ETT that received the CL procedure after NSRCT were almost 2.3 times more likely to get extracted compared with ETT that did not need the CL procedure at the 10-year follow-up (hazard ratio = 2.29, P < .05). Also, ETT with an inadequate crown-root ratio (1:1) after CL showed the lowest survival rate (40%) compared with ETT with an adequate crown-root ratio (<1:1).

CONCLUSIONS

A crown-root ratio of 1:1 after osseous CL may affect the long-term survival of ETT. Despite the promising survival rate of ETT with an adequate crown-root ratio after CL, the long-term survival of NSRCT with an inadequate crown-root ratio (1:1) should be considered in the treatment planning phase. Also, it is worth mentioning that the results of the present study should be evaluated in future prospective studies.

Furcation Involvement Classification: A Comprehensive Review and a New System Proposal

Various classification systems have been proposed to describe furcation lesions and Glickman's classification for many years seems to have been the most widely utilized in the sole clinical diagnosis with no reference to the prognostic value of the lesion itself. This article reviews the previous classification systems and proposes a new method to classify furcation lesions based on the position of the gingival margin and its relationship with the furcation area (clinically exposed/non-exposed furcation area) providing significant aid for a better understanding of furcation involvements and increases the prognostic value of treatments in the long term.

Synthesis of a Novel Electrospun Polycaprolactone Scaffold Functionalized with Ibuprofen for Periodontal Regeneration: An In Vitro andIn Vivo Study

Ibuprofen (IBU) has been shown to improve periodontal treatment outcomes. The aim of this study was to develop a new anti-inflammatory scaffold by functionalizing an electrospun nanofibrous poly-ε-caprolactone membrane with IBU (IBU-PCL) and to evaluate its impact on periodontal inflammation, wound healing and regeneration in vitro and in vivo. IBU-PCL was synthesized through electrospinning. The effects of IBU-PCL on the proliferation and migration of epithelial cells (EC) and fibroblasts (FB) exposed to Porphyromonas gingivlais lipopolysaccharide (Pg-LPS) were evaluated through the AlamarBlue test and scratch assay, respectively. Anti-inflammatory and remodeling properties were investigated through Real time qPCR. Finally, the in vivo efficacy of the IBU-PCL membrane was assessed in an experimental periodontitis mouse model through histomorphometric analysis. The results showed that the anti-inflammatory effects of IBU on gingival cells were effectively amplified using the functionalized membrane. IBU-PCL reduced the proliferation and migration of cells challenged by Pg-LPS, as well as the expression of fibronectin-1, collagen-IV, integrin α3β1 and laminin-5. In vivo, the membranes significantly improved the clinical attachment and IBU-PCL also reduced inflammation-induced bone destruction. These data showed that the IBU-PCL membrane could efficiently and differentially control inflammatory and migratory gingival cell responses and potentially promote periodontal regeneration.

Risk stratification using lean body mass in patients undergoing transcatheter aortic valve replacement

BACKGROUND

The prognostic impact of skeletal muscle mass, assessed using lean body mass (LBM), remain unclear in patients who underwent transcatheter aortic valve replacement (TAVR). The aim of this study to assess prognostic impact of LBM on mortality after TAVR.

METHODS

We assessed 1,613 patients (median age 85 years, 70% female) who underwent TAVI from October 2013 to April 2016 using OCEAN (Optimized transCathEter vAlvular interveNtion)-TAVI registry data. LBM was calculated using the James formula. The primary endpoint was all-cause death after TAVR.

RESULTS

Median follow-up period was 287 days (interquartile range 110-462). The Kaplan-Meier analysis demonstrated that patients with low LBM had significantly higher incidence of all-cause death than those with high LBM in male (32.3% vs. 9.9%, log rank P < 0.001) and female (15.8% vs. 9.2%, log-rank P = 0.011). On contrary, the risk stratification using body mass index (BMI) could not validate into female patients who underwent TAVR. The multivariate analysis showed that the LBM was an independent predictor of all-cause death in male (Hazard ratio [HR] 0.93; 95% confidence interval [CI] 0.89-0.98) and female (HR 0.94; 95% CI 0.89-0.99). Inversely, the assessment using BMI could not identify the high-risk population in a female.

CONCLUSIONS

The patients with low LBM had the higher incidence of all-cause death after TAVR than those with high LBM, regardless of gender. Thus, the risk stratification using LBM might provide further insight to identify the high-risk TAVR population, compared to conventional risk stratification using BMI.

Inconsistency in the Crown-to-Root Ratios of Single-Rooted Premolars Measured by 2D and 3D Examinations

Micro-computed tomography (micro-CT) was applied to elucidate the relationship between the three-dimensional (3D) root surface area (RSA) and two-dimensional (2D) crown-to-root ratio (CRR) of extracted teeth to classify the periodontitis and assign a periodontal/prosthetic prognosis. A total of 31 maxillary and 35 mandibular single-rooted human premolars were examined. The amount of periodontal support on the basis of 3D RSA and 2D root length (RL) at CRRs of 1:1, 5:4, 3:2, and 2:1 were analyzed. Both maxillary and mandibular premolars demonstrated a nonsignificant RSA percentage at the evaluated CRRs. The coronal 21%–22% 2D RL and the 26%–28% 3D RSA bone loss apical to the cemento-enamel junction corresponded to a CRR of 1:1, relating to mild-moderate periodontitis. The coronal 30%–31% 2D RL and the 41%–42% 3D RSA bone loss corresponded to a CRR of 5:4, correlating to severe periodontitis. More severe clinical attachment loss (CAL) was observed in the 3D RSA measurement than in the 2D RL measurement at the evaluated CRRs. The amount of CAL at the CRR of 1:1 was inadequate to assess the severity of periodontitis on the basis of the 2D RL and 3D RSA measurements.

Preferences and flexibility in decision-making among dental clinicians regarding the treatment of multirooted teeth: an interactive communication device-based survey at two academic conferences

Purpose

Decision-making by dental and medical experts can be influenced by their biases, interests, and experiences, and academic arguments about controversial issues may additionally be considered indirect experiences capable of affecting decision-making. This study reports on the use of interactive communication devices to evaluate preferences and flexibility in decision-making among dental care providers who attended two distinct academic conferences.

Methods

Two debates were presented by a team of two lecturers at two academic conferences (focusing on periodontology and implant dentistry, respectively) and the audience members of each session were surveyed. Before each lecture, two case modules about the diagnosis and treatment of multirooted molar lesions were provided, and interactive communication devices were used to collect responses about decision-making preferences in treatment planning immediately before and after a debate about treatment strategies.

Results

In total, 81 and 84 completed answers from both conferences were obtained for the first and second case modules, respectively. The preferred treatment plan differed significantly according to the focus of the conference, and a tendency emerged for the clinicians participating in each conference to express uniform preferences. However, attending the debates resulted in significant changes in decision-making preferences regardless of the conference focus or the characteristics of the participants.

Conclusions

Our findings suggest that providing continuing education via debates on controversial issues may be effective in widening conceptual knowledge and reducing biases among experts in the dental and medical fields.