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Abstract Background Mounting evidence indicates potential associations between poor oral health status (OHS) and increased pneumonia risk. Relative pneumonia risk was assessed in the context of longitudinally documented OHS. Methods Electronic medical/dental patient data captured from 2007 through 2019 were retrieved from the integrated health records of Marshfield Clinic Health Systems. Participant eligibility initiated with an assessment of OHS, stratified into the best, moderate, or worst OHS groups, with the additional criterion of ‘no pneumonia diagnosis in the past 90 days’. Pneumonia incidence was longitudinally monitored for up to 1 year from each qualifying dental visit. Models were assessed, with and without adjustment for prior pneumonia incidence, adjusted for smoking and subjected to confounding mitigation attributable to known pneumonia risk factors by applying propensity score analysis. Time-to-event analysis and proportional hazard modeling were applied to investigate relative pneumonia risk over time among the OHS groups. Results Modeling identified associations between any incident pneumonia subtype and ‘number of missing teeth’ (p

Abstract Objective To conduct systematic review applying “preferred reporting items for systematic reviews and meta‐analyses statement” and “prediction model risk of assessment bias tool” to studies examining the performance of predictive models incorporating oral health‐related variables as candidate predictors for projecting undiagnosed diabetes mellitus (Type 2)/prediabetes risk. Materials and Methods Literature searches undertaken in PubMed, Web of Science, and Gray literature identified eligible studies published between January 1, 1980 and July 31, 2018. Systematically reviewed studies met inclusion criteria if studies applied multivariable regression modeling or informatics approaches to risk prediction for undiagnosed diabetes/prediabetes, and included dental/oral health‐related variables modeled either independently, or in combination with other risk variables. Results Eligibility for systematic review was determined for seven of the 71 studies screened. Nineteen dental/oral health‐related variables were examined across studies. “Periodontal pocket depth” and/or “missing teeth” were oral health variables consistently retained as predictive variables in models across all systematically reviewed studies. Strong performance metrics were reported for derived models by all systematically reviewed studies. The predictive power of independently modeled oral health variables was marginally amplified when modeled with point‐of‐care biological glycemic measures in dental settings. Meta‐analysis was precluded due to high inter‐study variability in study design and population diversity. Conclusions Predictive modeling consistently supported “periodontal measures” and “missing teeth” as candidate variables for predicting undiagnosed diabetes/prediabetes. Validation of predictive risk modeling for undiagnosed diabetes/prediabetes across diverse populations will test the feasibility of translating such models into clinical practice settings as noninvasive screening tools for identifying at‐risk individuals following demonstration of model validity within the defined population.

BackgroundThe objective of this study was to build models that define variables contributing to pneumonia risk by applying supervised Machine Learning (ML) to medical and oral disease data to define key risk variables contributing to pneumonia emergence for any pneumonia/pneumonia subtypes.MethodsRetrospective medical and dental data were retrieved from the Marshfield Clinic Health System's data warehouse and the integrated electronic medical-dental health records (iEHR). Retrieved data were preprocessed prior to conducting analyses and included matching of cases to controls by (a) race/ethnicity and (b) 1:1 Case: Control ratio. Variables with >30% missing data were excluded from analysis. Datasets were divided into four subsets: (1) All Pneumonia (all cases and controls); (2) community (CAP)/healthcare-associated (HCAP) pneumonias; (3) ventilator-associated (VAP)/hospital-acquired (HAP) pneumonias; and (4) aspiration pneumonia (AP). Performance of five algorithms was compared across the four subsets: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Multi Layer Perceptron (MLP), and Random Forests. Feature (input variables) selection and 10-fold cross validation was performed on all the datasets. An evaluation set (10%) was extracted from the subsets for further validation. Model performance was evaluated in terms of total accuracy, sensitivity, specificity, F-measure, Mathews-correlation-coefficient, and area under receiver operating characteristic curve (AUC).ResultsIn total, 6,034 records (cases and controls) met eligibility for inclusion in the main dataset. After feature selection, the variables retained in the subsets were: All Pneumonia (n = 29 variables), CAP-HCAP (n = 26 variables), VAP-HAP (n = 40 variables), and AP (n = 37 variables). Variables retained (n = 22) were common across all four pneumonia subsets. Of these, the number of missing teeth, periodontal status, periodontal pocket depth more than 5 mm, and number of restored teeth contributed to all the subsets and were retained in the model. MLP outperformed other predictive models for All Pneumonia, CAP-HCAP, and AP subsets, while SVM outperformed other models in VAP-HAP subset.ConclusionThis study validates previously described associations between poor oral health and pneumonia. Benefits of an integrated medical-dental record and care delivery environment for modeling pneumonia risk are highlighted. Based on findings, risk score development could inform referrals and follow-up in integrated healthcare delivery environments and coordinated patient management.

The theoretical and computational framework of finite deformation mesoscale field dislocation mechanics (MFDM) is used to understand the salient aspects of kink-band formation in Cu-Nb nano-metallic laminates (NMLs). A conceptually minimal, plane-strain idealization of the three-dimensional geometry, including crystalline orientation, of additively manufactured NML is used to model NMLs. Importantly, the natural jump/interface condition of MFDM imposing continuity of (certain components) of plastic strain rates across interfaces allows theory-driven ‘communication’ of plastic flow across the laminate boundaries in our finite element implementation. Kink bands under layer parallel compression of NMLs in accord with experimental observations arise in our numerical simulations. The possible mechanisms for the formation and orientation of kink bands are discussed, within the scope of our idealized framework. We also report results corresponding to various parametric studies that provide preliminary insights and clear questions for future work on understanding the intricate underlying mechanisms for the formation of kink bands.

Abstract Background A key challenge for improving the quality of health care is to be able to use a common framework to work with patient information acquired in any of the health and life science disciplines. Patient information collected during dental care exposes many of the challenges that confront a wider scale approach. For example, to improve the quality of dental care, we must be able to collect and analyze data about dental procedures from multiple practices. However, a number of challenges make doing so difficult. First, dental electronic health record (EHR) information is often stored in complex relational databases that are poorly documented. Second, there is not a commonly accepted and implemented database schema for dental EHR systems. Third, integrative work that attempts to bridge dentistry and other settings in healthcare is made difficult by the disconnect between representations of medical information within dental and other disciplines’ EHR systems. As dentistry increasingly concerns itself with the general health of a patient, for example in increased efforts to monitor heart health and systemic disease, the impact of this disconnect becomes more and more severe. To demonstrate how to address these problems, we have developed the open-source Oral Health and Disease Ontology (OHD) and our instance-based representation as a framework for dental and medical health care information. We envision a time when medical record systems use a common data back end that would make interoperating trivial and obviate the need for a dedicated messaging framework to move data between systems. The OHD is not yet complete. It includes enough to be useful and to demonstrate how it is constructed. We demonstrate its utility in an analysis of longevity of dental restorations. Our first narrow use case provides a prototype, and is intended demonstrate a prospective design for a principled data backend that can be used consistently and encompass both dental and medical information in a single framework. Results The OHD contains over 1900 classes and 59 relationships. Most of the classes and relationships were imported from existing OBO Foundry ontologies. Using the LSW2 (LISP Semantic Web) software library, we translated data from a dental practice’s EHR system into a corresponding Web Ontology Language (OWL) representation based on the OHD framework. The OWL representation was then loaded into a triple store, and as a proof of concept, we addressed a question of clinical relevance – a survival analysis of the longevity of resin filling restorations. We provide queries using SPARQL and statistical analysis code in R to demonstrate how to perform clinical research using a framework such as the OHD, and we compare our results with previous studies. Conclusions This proof-of-concept project translated data from a single practice. By using dental practice data, we demonstrate that the OHD and the instance-based approach are sufficient to represent data generated in real-world, routine clinical settings. While the OHD is applicable to integration of data from multiple practices with different dental EHR systems, we intend our work to be understood as a prospective design for EHR data storage that would simplify medical informatics. The system has well-understood semantics because of our use of BFO-based realist ontology and its representation in OWL. The data model is a well-defined web standard.

Abstract Growth and other dynamical processes in soft materials can create novel types of mesoscopic defects including discontinuities for the second and higher derivatives of the deformation, and terminating defects for these discontinuities. These higher-order defects move “easily", and can thus confer a great degree of flexibility to the material. We develop a general continuum mechanical framework from which we can derive the dynamics of higher order defects in a thermodynamically consistent manner. We illustrate our framework by obtaining the explicit dynamical equations for the next higher order defects in an elastic body beyond dislocations, phase boundaries, and disclinations, namely, surfaces of inflection and branch lines.

Introduction: To conduct a statewide survey among Wisconsin-based dental providers evaluating current knowledgeability, attitudes and practice behaviors surrounding management of patients with diabetes/prediabetes in the dental setting. The study explored perceptions on feasibility, value, barriers, and current status of integrated care model (ICM) adoption by dental practicesMaterials and Methods: A 32-question paper-based survey was mailed to all licensed dentists and dental hygienists practicing in Wisconsin. The study was conducted over a 4 week period in 2019. The survey instrument was adapted from a previous validated survey and was expanded to include questions on ICM adoption. Content and validity analyses and beta testing were conducted prior to dissemination of the survey. Descriptive statistics and chi-square tests were applied for data analyses. Thematic analyses was performed on open-ended questions.Results: Survey response rate was 12% (N = 854/7,356) representing 41% dentists and 59% dental hygienists. While 68% reported educating patients on oral health-diabetes association, only 18% reported medical consultations to inform dental treatment, and “frequent” (22%) or “occasional” (40%), medical triage. Knowledge-based questions were correctly answered by >70% of participants. While 50% valued chair-side glycemic screening and 85% supported non-invasive chair-side screening to identify at-risk patients,>88% relied on patient-reported diabetic status. Barriers to ICM adoption included time investment (70%), patient activation/cooperation (62%), cost (50%), insurance coverage (50%), infrequent interdisciplinary communication (46%), lack of equipment (33%) and provider (31%).Conclusion: Low rates of ICM adoption, chair-side testing, medical consultation and triage, and need for educational curricula reform were identified.

Objective Health education interventions during pregnancy can influence maternal oral health (OH), maternal OH-behaviors and children’s OH. Interventions that can be delivered at anytime and anywhere, for example mobile-health (mHealth) provides an opportunity to address challenges of health education and support activation of women in underserved and rural communities to modify their health behavior. This pilot study was undertaken as a part of a mHealth initiative to determine knowledge, attitudes, and behaviors related to pregnancy and ECC prevention among women attending obstetrics/gynecology (OB/GYN) practices at a large rurally-based clinic. Methods A cross-sectional survey study was voluntarily engaged by women (n = 191) aged 18 to 59 years attending OB/GYN visits, over a 3-week period from 12/2019 to 1/2020. Survey results were analyzed applying descriptive statistics, X 2 and Fisher’s Exact tests. The significance level was set at P

Introduction: Rates of diabetes/prediabetes continue to increase, with disparity populations disproportionately affected. Previous field trials promoted point-of-care (POC) glycemic screening in dental settings as an additional primary care setting to identify potentially at-risk individuals requiring integrated care intervention. The present study observed outcomes of POC hemoglobin A1c (HbA1c) screening at community health center (CHC) dental clinics (DC) and compliance with longitudinal integrated care management among at-risk patients attending dental appointments.Materials and Methods: POC HbA1c screening utilizing Food and Drug Administration (FDA)-approved instrumentation in DC settings and periodontal evaluation of at-risk dental patients with no prior diagnosis of diabetes/prediabetes and no glycemic testing in the preceding 6 months were undertaken. Screening of patients attending dental appointments from October 24, 2017, through September 24, 2018, was implemented at four Wisconsin CHC-DCs serving populations with a high representation of disparity. Subjects meeting at-risk profiles underwent POC HbA1c screening. Individuals with measures in the diabetic/prediabetic ranges were advised to seek further medical evaluation and were re-contacted after 3 months to document compliance. Longitudinal capture of glycemic measures in electronic health records for up to 2 years was undertaken for a subset (n = 44) of subjects with available clinical, medical, and dental data. Longitudinal glycemic status and frequency of medical and dental access for follow-up care were monitored.Results: Risk assessment identified 224/915 (24.5%) patients who met inclusion criteria following two levels of risk screening, with 127/224 (57%) qualifying for POC HbA1c screening. Among those tested, 62/127 (49%) exhibited hyperglycemic measures: 55 in the prediabetic range and seven in the diabetic range. Moderate-to-severe periodontitis was more prevalent in patients with prediabetes/diabetes than in individuals with measures in the normal range. Participant follow-up compliance at 3 months was 90%. Longitudinal follow-up documented high rates of consistent access (100 and 89%, respectively), to the integrated medical/DC environment over 24 months for individuals with hyperglycemic screening measures.Conclusion: POC glycemic screening revealed elevated HbA1c measures in nearly half of at-risk CHC-DC patients. Strong compliance with integrated medical/dental management over a 24-month interval was observed, documenting good patient receptivity to POC screening in the dental setting and compliance with integrated care follow-up by at-risk patients.

Oral cavity cancer (OCC) is associated with high morbidity and mortality rates when diagnosed at late stages. Early detection of increased risk provides an opportunity for implementing prevention strategies surrounding modifiable risk factors and screening to promote early detection and intervention. Historical evidence identified a gap in the training of primary care providers (PCPs) surrounding the examination of the oral cavity. The absence of clinically applicable analytical tools to identify patients with high-risk OCC phenotypes at point-of-care (POC) causes missed opportunities for implementing patient-specific interventional strategies. This study developed an OCC risk assessment tool prototype by applying machine learning (ML) approaches to a rich retrospectively collected data set abstracted from a clinical enterprise data warehouse. We compared the performance of six ML classifiers by applying the 10-fold cross-validation approach. Accuracy, recall, precision, specificity, area under the receiver operating characteristic curve, and recall–precision curves for the derived voting algorithm were: 78%, 64%, 88%, 92%, 0.83, and 0.81, respectively. The performance of two classifiers, multilayer perceptron and AdaBoost, closely mirrored the voting algorithm. Integration of the OCC risk assessment tool developed by clinical informatics application into an electronic health record as a clinical decision support tool can assist PCPs in targeting at-risk patients for personalized interventional care.

Abstract Background Oral cancer (OC) is associated with multiple risk factors and high mortality rates and substantially contributes to the global cancer burden despite being highly preventable. This cross-sectional study sought to assess current knowledge, awareness, and behaviors of patients in rural communities surrounding OC risk. Methods An anonymous 21-question survey was distributed to patients in waiting rooms of a large integrated medical-dental health system serving north-central Wisconsin. Survey results were summarized via descriptive statistics. Odds ratios surrounding health literacy on OC risk factors were obtained using unconditional univariate logistic regression analysis. Results Of 504 dental and 306 medical patients completing the survey, 62.2% were female, Caucasian/White (92%) with 41% having a ≤ high school diploma/equivalent. Current smoker/smokeless tobacco use was reported by 34%, while 39% reported former tobacco exposure. Alcohol use was reported by 54% of respondents at the following frequencies: < once/week, (35%); 1–2 times/week, (16%); 3–4 times/week, (6%); 5–6 times/week, (2%); and daily, (23%). Knowledge about tobacco and alcohol use and increased OC risk was reported by 94 and 40%, respectively. About 50% reported knowledgeability regarding cancer-associated symptomology. Tobacco cessation was reported by 20% of responders. Receipt of education on OC from healthcare providers and human papilloma virus links to OC causation was reported by 38 and 21%, respectively. Conclusion Patients who smoked > 20+ cigarettes per day were more knowledgeable about tobacco and OC risk compared to non-smokers and those who smoked ≤ 19 cigarettes/day (p = 0.0647). Patients who were alcohol consumers exhibited higher knowledgeability surrounding increased OC risk with alcohol and tobacco exposures compared to alcohol abstainers (p = 0.06). We concluded that patients recognized links between tobacco and OC risk but demonstrated lower knowledge of other causal factors. Strategic patient education by providers could increase awareness of OC risk.

Molybdenum disulfide quantum dots (MoS2 QDs) are a unique class of zero-dimensional (0D) van der Waals nanostructures. MoS2 QDs have attracted significant attention due to their unique optical, electronic, chemical, and biological properties due to the presence of edge states of these van der Waals QDs for various chemical functionalization. Their novel properties have enabled applications in many fields, including advanced electronics, electrocatalysis, and biomedicine. In this review, the various synthesis techniques, the novel properties, and the wide applications of MoS2 quantum dots are discussed in detail.

Retrospective analyses of real-world clinical data face challenges owing to the absence of some data elements. Historically, missing data was addressed by first classifying its presence into one of three categories: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). Imputation techniques continue to be developed and tested to gauge their capacity to mitigate the negative impact of missing data types on analyses and their results. This study undertook a comparison of two techniques of data imputation: probabilistic principal component analysis (PPCA) and multiple imputation using chained equations (MICE).Retrospective data from 41,543 unique patients including both medical and dental variables (n = 116) were mined from the institutional research data warehouse, which captures data through an integrated medical and dental electronic health record (iEHR). A subset with complete data on all variables of interest was sampled. “Missing data” were artificially created by randomly removing data elements to create the missing data problem. Applying PPCA and MICE, the capacity of the two techniques to create an accurate imputed dataset was tested. Comparisons were drawn between imputed dataset and sampled subset, to investigate which technique more closely simulated the true data.PPCA outperformed MICE with an overall correct imputation percentage (accuracy) and root mean square error (RMSE) of approximately 65% and 0.29, respectively, compared to MICE, which yielded approximately 38% accuracy with a RMSE of 0.83.Overall, this study concluded that PPCA demonstrated higher capacity to impute MCAR data than MICE. Keywords: Imputation, Probabilistic principal component analysis, PPCA, Multiple imputations using chained equations, MICE, Medical dental data, Dental informatics

The objective was to develop a predictive model using medical-dental data from an integrated electronic health record (iEHR) to identify individuals with undiagnosed diabetes mellitus (DM) in dental settings. Retrospective data retrieved from Marshfield Clinic Health System's data-warehouse was pre-processed prior to conducting analysis. A subset was extracted from the preprocessed dataset for external evaluation (Nvalidation) of derived predictive models. Further, subsets of 30%–70%, 40%–60% and 50%–50% case-to-control ratios were created for training/testing. Feature selection was performed on all datasets. Four machine learning (ML) classifiers were evaluated: logistic regression (LR), multilayer perceptron (MLP), support vector machines (SVM) and random forests (RF). Model performance was evaluated on Nvalidation. We retrieved a total of 5319 cases and 36,224 controls. From the initial 116 medical and dental features, 107 were used after performing feature selection. RF applied to the 50%–50% case-control ratio outperformed other predictive models over Nvalidation achieving a total accuracy (94.14%), sensitivity (0.941), specificity (0.943), F-measure (0.941), Mathews-correlation-coefficient (0.885) and area under the receiver operating curve (0.972). Future directions include incorporation of this predictive model into iEHR as a clinical decision support tool to screen and detect patients at risk for DM triggering follow-ups and referrals for integrated care delivery between dentists and physicians. Keywords: Dental informatics, Decision-support systems, Electronic health records, Evidence-based practice, Machine leaning, Modeling healthcare services

Boron nitride quantum dots (BNQDs) have gained increasing attention for their versatile fluorescent, optoelectronic, chemical, and biochemical properties. During the past few years, significant progress has been demonstrated, started from theoretical modeling to actual application. Many interesting properties and applications have been reported, such as excitation-dependent emission (and, in some cases, non-excitation dependent), chemical functionalization, bioimaging, phototherapy, photocatalysis, chemical, and biological sensing. An overview of this early-stage research development of BNQDs is presented in this article. We have prepared un-bias assessments on various synthesis methods, property analysis, and applications of BNQDs here, and provided our perspective on the development of these emerging nanomaterials for years to come.

Background: Precision medicine (PM), representing clinically applicable personalized medicine, proactively integrates and interprets multidimensional personal health data, including clinical, ‘omics’, and environmental profiles, into clinical practice. Realization of PM remains in progress. Objective: The focus of this review is to provide a descriptive narrative overview of: 1) the current status of oral personalized medicine; and 2) recent advances in genomics and related ‘omic’ and emerging research domains contributing to advancing oral-systemic PM, with special emphasis on current understanding of oral microbiomes. Design: A scan of peer-reviewed literature describing oral PM or ‘omic’-based research conducted on humans/data published in English within the last 5 years in journals indexed in the PubMed database was conducted using mesh search terms. An evidence-based approach was used to report on recent advances with potential to advance PM in the context of historical critical and systematic reviews to delineate current state-of-the-art technologies. Special focus was placed on oral microbiome research associated with health and disease states, emerging research domains, and technological advances, which are positioning realization of PM. Results: This review summarizes: 1) evolving conceptualization of personalized medicine; 2) emerging insight into roles of oral infectious and inflammatory processes as contributors to both oral and systemic diseases; 3) community shifts in microbiota that may contribute to disease; 4) evidence pointing to new uncharacterized potential oral pathogens; 5) advances in technological approaches to ‘omics’ research that will accelerate PM; 6) emerging research domains that expand insights into host–microbe interaction including inter-kingdom communication, systems and network analysis, and salivaomics; and 7) advances in informatics and big data analysis capabilities to facilitate interpretation of host and microbiome-associated datasets. Furthermore, progress in clinically applicable screening assays and biomarker definition to inform clinical care are briefly explored. Conclusion: Advancement of oral PM currently remains in research and discovery phases. Although substantive progress has been made in advancing the understanding of the role of microbiome dynamics in health and disease and is being leveraged to advance early efforts at clinical translation, further research is required to discern interpretable constituency patterns in the complex interactions of these microbial communities in health and disease. Advances in biotechnology and bioinformatics facilitating novel approaches to rapid analysis and interpretation of large datasets are providing new insights into oral health and disease, potentiating clinical application and advancing realization of PM within the next decade.