Xenogenic Implantation of Human Mesenchymal Stromal Cells Using a Novel 3D-Printed Scaffold of PLGA and Graphene Leads to a Significant Increase in Bone Mineralization in a Rat Segmental Femoral Bone Defect.

Tissue-engineering technologies have the potential to provide an effective approach to bone regeneration. Based on the published literature and data from our laboratory, two biomaterial inks containing PLGA and blended with graphene nanoparticles were fabricated. The biomaterial inks consisted of two forms of commercially available PLGA with varying ratios of LA:GA (65:35 and 75:25) and molecular weights of 30,000-107,000. Each of these forms of PLGA was blended with a form containing a 50:50 ratio of LA:GA, resulting in ratios of 50:65 and 50:75, which were subsequently mixed with a 0.05 wt% low-oxygen-functionalized derivative of graphene. Scanning electron microscopy showed interconnected pores in the lattice structures of each scaffold. The cytocompatibility of human ADMSCs transduced with a red fluorescent protein (RFP) was evaluated in vitro. The in vivo biocompatibility and the potential to repair bones were evaluated in a critically sized 5 mm mechanical load-bearing segmental femur defect model in rats. Bone repair was monitored by radiological, histological, and microcomputed tomography methods. The results showed that all of the constructs were biocompatible and did not exhibit any adverse effects. The constructs containing PLGA (50:75)/graphene alone and with hADMSCs demonstrated a significant increase in mineralized tissues within 60 days post-treatment. The percentage of bone volume to total volume from microCT analyses in the rats treated with the PLGA + cells construct showed a 50% new tissue formation, which matched that of a phantom. The microCT results were supported by Von Kossa staining.

Exploring the Intersection between Social Determinants of Health and Unmet Dental Care Needs Using Deep Learning.

The goals of this study were to develop a risk prediction model in unmet dental care needs and to explore the intersection between social determinants of health and unmet dental care needs in the United States. Data from the 2016 Medical Expenditure Panel Survey were used for this study. A chi-squared test was used to examine the difference in social determinants of health between those with and without unmet dental needs. Machine learning was used to determine top predictors of unmet dental care needs and to build a risk prediction model to identify those with unmet dental care needs. Age was the most important predictor of unmet dental care needs. Other important predictors included income, family size, educational level, unmet medical needs, and emergency room visit charges. The risk prediction model of unmet dental care needs attained an accuracy of 82.6%, sensitivity of 77.8%, specificity of 87.4%, precision of 82.9%, and area under the curve of 0.918. Social determinants of health have a strong relationship with unmet dental care needs. The application of deep learning in artificial intelligence represents a significant innovation in dentistry and enables a major advancement in our understanding of unmet dental care needs on an individual level that has never been done before. This study presents promising findings and the results are expected to be useful in risk assessment of unmet dental care needs and can guide targeted intervention in the general population of the United States.

Health and dental care expenditures in the United States from 1996 to 2016.

INTRODUCTION: As total health and dental care expenditures in the United States continue to rise, healthcare disparities for low to middle-income Americans creates an imperative to analyze existing expenditures. This study examined health and dental care expenditures in the United States from 1996 to 2016 and explored trends in spending across various population subgroups. METHODS: Using data collected by the Medical Expenditure Panel Survey, this study examined health and dental care expenditures in the United States from 1996 to 2016. Trends in spending were displayed graphically and spending across subgroups examined. All expenditures were adjusted for inflation or deflation to the 2016 dollar. RESULTS: Both total health and dental expenditures increased between 1996 and 2016 with total healthcare expenditures increasing from $838.33 billion in 1996 to $1.62 trillion in 2016, a 1.9-fold increase. Despite an overall increase, total expenditures slowed between 2004 and 2012 with the exception of the older adult population. Over the study period, expenditures increased across all groups with the greatest increases seen in older adult health and dental care. The per capita geriatric dental care expenditure increased 59% while the per capita geriatric healthcare expenditure increased 50% across the two decades. For the overall US population, the per capita dental care expenditure increased 27% while the per capita healthcare expenditure increased 60% over the two decades. All groups except the uninsured experienced increased dental care expenditure over the study period. CONCLUSIONS: Healthcare spending is not inherently bad since it brings benefits while exacting costs. Our findings indicate that while there were increases in both health and dental care expenditures from 1996 to 2016, these increases were non-uniform both across population subgroups and time. Further research to understand these trends in detail will be helpful to develop strategies to address health and dental care disparities and to maximize resource utilization.