Nonsyndromic orofacial clefts (NSOFCs) represent a large proportion (70%-80%) of all OFCs. They can be broadly categorized into nonsyndromic cleft lip with or without cleft palate (NSCL/P) and nonsyndromic cleft palate only (NSCPO). Although NSCL/P and NSCPO are considered etiologically distinct, recent evidence suggests the presence of shared genetic risks. Thus, we investigated the genetic overlap between NSCL/P and NSCPO using African genome-wide association study (GWAS) data on NSOFCs. These data consist of 814 NSCL/P, 205 NSCPO cases, and 2159 unrelated controls.â¯We generated common single-nucleotide variants (SNVs) association summary statistics separately for each phenotype (NSCL/P and NSCPO) under an additive genetic model. Subsequently, we employed the pleiotropic analysis under the composite null (PLACO) method to test for genetic overlap. Our analysis identified two loci with genome-wide significance (rs181737795 [p = 2.58E-08] and rs2221169 [p = 4.5E-08]) and one locus with marginal significance (rs187523265 [p = 5.22E-08]). Using mouse transcriptomics data and information from genetic phenotype databases, we identified MDN1, MAP3k7, KMT2A, ARCN1, and VADC2 as top candidate genes for the associated SNVs. These findings enhance our understanding of genetic variants associated with NSOFCs and identify potential candidate genes for further exploration.
PURPOSE: This study aimed to investigate the predictors of survival of non-occlusal non-incisal glass-ionomer restorations as a surrogate for root surface restorations among older adults. METHODS: In a retrospective cohort analysis using the University of Iowa College of Dentistry electronic dental records, we included 721 patients aged 65+ who received 2+ surface non-occlusal non-incisal glass ionomer restorations placed from January 2005 - December 2011. Restorations were followed until September 2017 or until they were deemed to have failed. RESULTS: At baseline, participants' mean age was 77.6 ± 8.2 years, and 45.8% were females. Most patients were self-pay (65.2%). Most restorations were placed by residents and dental students (82.7%) and included only two surfaces (95.6%). About half (49.1%) failed during follow-up, with a median survival time of 3.7 years. The time ratio for lower incisors compared to other teeth was 0.6 (p = .006), for three-and-four-surface restorations compared to two was 0.7 (p = .007), for faculty as providers compared to residents and students was 1.4 (p = .039), and for the Geriatric & Special Needs Clinic compared to others was 0.8 (p = .013). Time ratios less than one indicate association with shorter durations for restorations, and time ratios greater than one indicate association with longer durations for restorations. CONCLUSION: Tooth type, number of restored surfaces, provider type, and clinic were all significant factors associated with survival of these restorations.
In order to keep clinical norms aligned with evidence-based practices, dental researchers need to be equipped to read and evaluate meta-analyses. Moreover, clinician scientists in dentistry should be prepared to lead investigations that incorporate meta-analysis. This article provides an overview of the principles and practices that will equip dental researchers to engage in meta-analysis work. Six elements of meta-analysis are addressed: formulating a research question, searching the literature, collecting the data, aggregating the data, analyzing the data, and interpreting results and drawing generalizations. Readers are guided through each of these stages of research so that they can recognize and implement robust, reproducible meta-analysis work.
OBJECTIVE: This study aims to understand the prevalence of cognitive impairment and dentally-related functional (DRF) loss among older adults seeking community-based dental care. METHODS AND RESULTS: A total of 149 adults aged 65 or older who visited the University of Iowa College of Dentistry Clinics and who had no prior documented cognitive impairment were recruited in 2017 and 2018. Participants underwent a brief interview, a cognitive assessment, and an assessment of DRF. Bivariate and multivariate analyses were used to assess associations between demographic variables, DRF, and cognitive function. Close to half (40.7%) of the patients presented with some degree of cognitive impairment, and impaired DRF was observed in 13.8%. Compared to those without cognitive impairment, elderly dental patients with cognitive impairment were 15% more likely to present with impaired DRF (odds ratio = 1.15, 95% CI = (1.05, 1.26). CONCLUSION: Cognitive impairment is likely more prevalent in older adults seeking dental care than is generally understood by providers. Given its impact on DRF, dental providers should be alert to the possible need to evaluate patients' cognitive status and DRF in order to be able to adjust treatment and recommendations accordingly.
Cognition Disorders , Aged , Humans , Pilot Projects , Cognition Disorders/epidemiology , Cognition , Dental Care
CONTEXT: The Covid-19 pandemic has added a new chapter to discussions about the professional duty to care. To understand how Covid-19 may have changed medical students' ethical attitudes towards this duty, we analysed policies written before and during the pandemic by first-year students completing a yearly educational exercise focused on work requirement expectations for healthcare professionals during a hypothetical epidemic. METHODS: Within a repeated cross-sectional design, consensus coding was performed on policies written over 5 years (2017-2021) using a codebook based on eight questions from the educational exercise for summative content analysis. Frequencies provided summative results and comparisons across years used Fisher's exact test. RESULTS: We analysed 142 written policies from 2017 to 2021 representing 884 first-year students working in small groups. Students' commitment to the duty to care remained stable during the Covid-19 pandemic, but during the pandemic, students were more likely to support exceptions to the duty to care (e.g. for healthcare professionals with medical conditions or concern for household members' health) and more likely to expect institutions to provide safe working conditions. Ethical values supporting students' policies were largely consistent before and during the pandemic, the most common being beneficence, justice, duty to care, non-maleficence and utility. CONCLUSIONS: Our results suggest that students' support for the duty to care remained strong during the Covid-19 pandemic. We also found that students supported exceptions to this duty to reflect the needs of healthcare professionals and their families and that they expected institutions to provide safe working conditions. These findings can help inform ethics education and future pandemic preparedness.
INTRODUCTION: Minimal sedation (Anxiolysis) is used in dentistry to reduce stress, manage anxiety, and improve patient comfort during treatment. The oral route of minimal sedation is safe and convenient, but there is limited literature assessing the efficacy of this mode of patient care. This paper aims to evaluate the outcomes of oral sedation use for patients treated in a dental school setting using a retrospective cohort analysis of electronic health record data. METHODS: A total of 6872 patient records were selected after screening through the selection criteria. Demographic and treatment variables were obtained and analyzed. The appointment status was identified as a success or failure depending on the treatment codes assigned for that appointment. A multivariate logistic regression was used to evaluate relationships between appointment status and the obtained variables. RESULTS: Less than 3% patients had a 'failure outcome' when this data set was evaluated. Being treated in multiple clinics and being seen by multiple providers were both factors that increased the odds of success. CONCLUSION: Oral anxiolytics should be considered as a noteworthy option for patient management based on the outcomes reflected in this study. There is some evidence that seeing multiple providers improves the success rate of completing dental procedures carried out under oral sedation.
Three years into the coronavirus disease 2019 (COVID-19) pandemic, there are still growing concerns with the emergence of different variants, unknown long- and short-term effects of the virus, and potential biological mechanisms underlying etiopathogenesis and increased risk for morbidity and mortality. The role of the microbiome in human physiology and the initiation and progression of several oral and systemic diseases have been actively studied in the past decade. With the proof of viral transmission, carriage, and a potential role in etiopathogenesis, saliva and the oral environment have been a focus of COVID-19 research beyond diagnostic purposes. The oral environment hosts diverse microbial communities and contributes to human oral and systemic health. Several investigations have identified disruptions in the oral microbiome in COVID-19 patients. However, all these studies are cross-sectional in nature and present heterogeneity in study design, techniques, and analysis. Therefore, in this undertaking, we (a) systematically reviewed the current literature associating COVID-19 with changes in the microbiome; (b) performed a re-analysis of publicly available data as a means to standardize the analysis, and (c) reported alterations in the microbial characteristics in COVID-19 patients compared to negative controls. Overall, we identified that COVID-19 is associated with oral microbial dysbiosis with significant reduction in diversity. However, alterations in specific bacterial members differed across the study. Re-analysis from our pipeline shed light on Neisseria as the potential key microbial member associated with COVID-19.
Time-to-event (survival) analysis is an integral tool in the wheelhouse of the dental researcher. While there are many references available for the study of time-to-event analysis, they tend to be written for audiences trained in statistical methodology. Moreover, the canonical examples offered by most time-to-event analysis references are focused on outcomes that do not translate directly to dentistry. This article provides a tutorial of time-to-event analysis for the specific context of dental research. Our tutorial assumes no statistical training or computing experience. Using real data from a dental study as our extended example, we explain foundational concepts, including median survival, Nth-year survival, the log-rank test, and the Cox model.
Dental Research , Proportional Hazards Models , Survival Analysis
AIM: This study was primarily aimed at assessing the effect that specific periodontal phenotypical characteristics have on alveolar ridge remodelling after tooth extraction. MATERIALS AND METHODS: Patients in need of extraction of a non-molar maxillary tooth were enrolled. Baseline phenotypical characteristics (i.e., mid-facial and mid-palatal soft tissue and bone thickness, and supracrestal soft tissue height [STH]) were recorded upon extraction. A set of clinical, digital imaging (linear and volumetric), and patient-reported outcomes were assessed over a 14-week healing period. RESULTS: A total of 78 subjects were screened. Forty-two subjects completed the study. Linear and volumetric bone changes, as well as vertical linear soft tissue and alveolar ridge volume (soft tissue contour) variations, were indicative of a marked dimensional reduction of the alveolar ridge over time. Horizontal facial and palatal soft tissue thickness gain was observed. Thin facial bone (≤1 mm) upon extraction, compared with thick facial bone (>1 mm), was associated with greater linear horizontal (-4.57 ± 2.31 mm vs. -2.17 ± 1.65 mm, p = .003) and vertical mid-facial (-0.95 ± 0.67 mm vs. -4.08 ± 3.52 mm, p < 0.001) and mid-palatal (-2.03 ± 2.08 mm vs. -1.12 ± 0.99 mm, p = 0.027) bone loss, as well as greater total (-34% ± 10% vs. 15% ± 6%, p < 0.001), facial (-51% ± 19% vs. 28% ± 18%, p = 0.040), and palatal bone volume reduction (-26% ± 14% vs. -8% ± 10%, p < 0.001). Aside from alveolar bone thickness, STH was also found to be a predictor of alveolar ridge resorption since this variable was directly correlated with bone volume reduction. Patient-reported discomfort scores progressively decreased over time, and the mean satisfaction upon study completion was 94.5 ± 0.83 out of 100. CONCLUSIONS: Alveolar ridge remodelling is a physiological phenomenon that occurs after tooth extraction. Post-extraction alveolar ridge atrophy is more marked on the facio-coronal aspect. These dimensional changes are more pronounced in sites exhibiting a thin facial bone phenotype (Clinicaltrials.gov NCT02668289).
Alveolar Bone Loss , Alveolar Ridge Augmentation , Humans , Tooth Socket/surgery , Alveolar Process/diagnostic imaging , Alveolar Process/surgery , Alveolar Bone Loss/diagnostic imaging , Alveolar Bone Loss/etiology , Dental Care , Tooth Extraction , Phenotype , Alveolar Ridge Augmentation/methods
The intersection between the human oral microbiome and oral health is an emerging area of study which has gained momentum over the last decade. This momentum has motivated a search for associations between the oral microbiome and oral cancer, in hopes of identifying possible biomarkers that facilitate earlier diagnosis and improved prognosis for patients with that disease. The present study examined the relationship between the microbiome in the human oral cavity and oral squamous cell carcinoma (OSCC). We searched the literature for case-control studies which focused on the relationship between the human oral microbiome and OSCC. We aggregated three types of data from these studies: bacteriome data at the genus level, predicted functional pathway data, and gene abundance data. From these data, we noted several microbial genera which may be associated with oral cancer status, including Fusobacterium. We also identified functional pathways which merit further investigation, including RNA degradation (ko03018) and primary immunodeficiency (ko05340). In addition, our analysis of gene abundance data identified the gene K06147 (ATP-binding cassette, subfamily B, bacterial) as being over abundant in OSCC samples. Our results are generalizations which identified some currents that we believe could guide further research. Our work faced several limitations related to the heterogeneity of the available data. Wide variation in methods for sample collection, methods for controlling for known behavioral risk factors, computing platform choice, and methods for case-control design all posed confounding factors in this work. We examined the current methods of data collection, data processing, and data reporting in order to offer suggestions toward the establishment of best practices within this field. We propose that these limitations should be addressed through the implementation of standardized data analytic practices that will conform to the rigor and reproducibility standards required of publicly funded research.
BACKGROUND: Repairing crowns with defective margins is minimally invasive and cost-effective compared with replacement. The authors' objectives were to examine the survival trajectory of crown margin repairs and to determine the factors associated with survival. METHODS: Records of adult patients from January 2008 through August 2019 were reviewed for crown margin repairs completed at University of Iowa College of Dentistry. A total of 1,002 crown margin repairs were found. Each repair was followed through the end of study in 2019 or until an event (for example, additional repair, endodontic treatment, crown replacement, or extraction). A Cox proportional hazards model was used to study the relationship between selected covariates and time to event. RESULTS: During the follow-up period, 32.8% of the repairs needed reintervention. In the final model, repair material was the only significant covariate. No difference was found between the survival of repairs done with resin-modified glass ionomer and amalgam. However, the repairs done with resin-based composite and conventional glass ionomer were more likely (1.5 times: 95% CI, 1.02 to 2.10 times; and 2 times: 95% CI, 1.40 to 2.73 times, respectively) to need reintervention than were those done with amalgam. CONCLUSIONS: Median survival time of crown margin repairs was 5.1 years (95% CI, 4.48 to 5.72 years). Median survival times for amalgam, resin-modified glass ionomer, resin-based composite, and glass ionomer repair materials were 5.7 years (95% CI, 4.80 to 6.25 years), 5.3 years (95% CI, 4.73 to 6.34 years), 3.2 years (95% CI, 2.51 to 6.19 years), and 3.0 years (95% CI, 2.53 to 3.62 years), respectively. PRACTICAL IMPLICATIONS: When considering crown margin repairs, resin-modified glass ionomer or amalgam is preferable to resin-based composite or glass ionomer.
Dental Caries , Dental Restoration, Permanent , Composite Resins , Crowns , Dental Amalgam , Dental Caries/therapy , Dental Materials , Glass Ionomer Cements , Humans , Retrospective Studies , Schools, Dental , Survival Analysis
[This corrects the article DOI: 10.3389/fonc.2019.01393.].
As awareness of the habits and risks associated with lung cancer has increased, so has the interest in promoting and improving upon lung cancer screening procedures. Recent research demonstrates the benefits of lung cancer screening; the National Lung Screening Trial (NLST) found as its primary result that preventative screening significantly decreases the death rate for patients battling lung cancer. However, it was also noted that the false positive rate was very high (>94%).In this work, we investigated the ability of various machine learning classifiers to accurately predict lung cancer nodule status while also considering the associated false positive rate. We utilized 416 quantitative imaging biomarkers taken from CT scans of lung nodules from 200 patients, where the nodules had been verified as cancerous or benign. These imaging biomarkers were created from both nodule and parenchymal tissue. A variety of linear, nonlinear, and ensemble predictive classifying models, along with several feature selection methods, were used to classify the binary outcome of malignant or benign status. Elastic net and support vector machine, combined with either a linear combination or correlation feature selection method, were some of the best-performing classifiers (average cross-validation AUC near 0.72 for these models), while random forest and bagged trees were the worst performing classifiers (AUC near 0.60). For the best performing models, the false positive rate was near 30%, notably lower than that reported in the NLST.The use of radiomic biomarkers with machine learning methods are a promising diagnostic tool for tumor classification. The have the potential to provide good classification and simultaneously reduce the false positive rate.
