Introducing dental students to dental public health: An analysis of US predoctoral course syllabi.

Dental public health competencies in predoctoral dental education ensure that students have the skills to succeed in an increasingly complex professional environment. This study examined existing public health curricula in US dental education and their alignment with national recommendations from the American Association of Public Health Dentistry (AAPHD) and guidance from the Healthy People Curriculum Task Force for health professions education programs. We contacted all US dental schools (N = 66) in November 2020-January 202 and requested syllabi for schools' first course with dental public health content. We received 34 syllabi, which provided textual data for content analysis. The authors used an initial content analysis tool to extract descriptive course characteristics. Then, direct and emergent coding was performed to summarize course content. Direct codes included the 23 dental public health topics specified by AAPHD recommendations. Uncategorized content was coded using an inductive approach to identify emergent course themes. Frequently covered topics included principles of dental public health (79% of syllabi) and access to care (79%). "Health disparities" was the most common emergent theme, with 50% of courses including related content. There was little consistency in how courses approached each topic. For example, the topic "access to care" covered healthcare delivery systems, determinants of health, legislative reform, and advocacy. Dental public health was often taught alongside unrelated content. Recommendations for dental public health competencies should be updated to include new educational priorities, align with current national recommendations, and align with Commission on Dental Accreditation Standards more clearly.

Dataset-chemokines, cytokines, and biomarkers in the saliva of children with Sjögren's syndrome.

Sjögren's syndrome is an autoimmune disease that can also occur in children. The disease is not well defined and there is limited information on the presence of chemokines, cytokines, and biomarkers (CCBMs) in the saliva of children that could improve their disease diagnosis. In a recent study [1], we reported a large dataset of 105 CCBMs that were associated with both lymphocyte and mononuclear cell functions [2] in the saliva of 11 children formally diagnosed with Sjögren's syndrome and 16 normal healthy children. Here, we extend those findings and use the Mendeley dataset [2] to identify CCBMs that have predictive power for Sjögren's syndrome in female children. Datasets of CCBMs from all saliva samples and female children saliva samples were standardized. We used machine learning methods to select Sjögren's syndrome associated CCBMs and assessed the predictive power of selected CCBMs in these two datasets using receiver operating characteristic (ROC) curves and associated areas under curve (AUC) as metrics. We used eight classifiers to identify 16 datasets that contained from 2 to 34 CCBMs with AUC values ranging from 0.91 to 0.94.

A distinguishing profile of chemokines, cytokines and biomarkers in the saliva of children with Sjögren's syndrome.

OBJECTIVE: SS is an autoimmune disease most commonly diagnosed in adults but can occur in children. Our objective was to assess the presence of chemokines, cytokines and biomarkers (CCBMs) in saliva from these children that were associated with lymphocyte and mononuclear cell functions. METHODS: Saliva was collected from 11 children diagnosed with SS prior to age 18 years and 16 normal healthy children. A total of 105 CCBMs were detected in multiplex microparticle-based immunoassays. ANOVA and t test (0.05 level) were used to detect differences. Ingenuity Pathway Analysis (IPA) was used to assess whether elevated CCBMs were in annotations associated with immune system diseases and select leukocyte activities and functions. Machine learning methods were used to evaluate the predictive power of these CCBMs for SS and were measured by receiver operating characteristic (ROC) curve and area under curve (AUC). RESULTS: Of the 105 CCBMs detected, 43 (40.9%) differed in children with SS from those in healthy study controls (P < 0.05) and could differentiate the two groups (P < 0.05). Elevated CCBMs in IPA annotations were associated with autoimmune diseases and with leukocyte chemotaxis, migration, proliferation, and regulation of T cell activation. The best AUC value in ROC analysis was 0.93, indicating that there are small numbers of CCBMs that may be useful for diagnosis of SS. CONCLUSION: While 35 of these 43 CCBMs have been previously reported in SS, 8 CCBMs had not. Additional studies focusing on these CCBMs may provide further insight into disease pathogenesis and may contribute to diagnosis of SS in children.

HBD3 Induces PD-L1 Expression on Head and Neck Squamous Cell Carcinoma Cell Lines.

Human ß-defensin 3 (HBD3) is an antimicrobial peptide up-regulated in the oral tissues of individuals with head and neck squamous cell carcinomas (HNSCC) and oral squamous cell carcinomas (SCC) and present in high concentrations in their saliva. In this study, we determined if HBD3 contributes to HNSCC pathogenesis by inducing programmed death-ligand 1 (PD-L1) expression on HNSCC cell lines. For this, SCC cell lines SCC4, SCC15, SCC19, SCC25, and SCC99 (5.0 × 104 viable cells) were used. Cells were incubated with IFNγ (0.6 µM) and HBD3 (0.2, 2.0, or 20.0 µM) for 24 h. Cells alone served as controls. Cells were then treated with anti-human APC-CD274 (PD-L1) and Live/Dead Fixable Green Dead Cell Stain. Cells treated with an isotype antibody and cells alone served as controls. All cell suspensions were analyzed in a LSR II Violet Flow Cytometer. Cytometric data was analyzed using FlowJo software. Treatment with IFNγ (0.6 µM) increased the number of cells expressing PD-L1 (p < 0.05) with respect to controls. Treatment with HBD3 (20.0 µM) also increased the number of cells expressing PD-L1 (p < 0.05) with respect to controls. However, treatment with IFNγ (0.6 µM) was not significantly different from treatment with HBD3 (20.0 µM) and the numbers of cells expressing PD-L1 were similar (p = 1). Thus, HBD3 increases the number of cells expressing PD-L1. This is a novel concept, but the role HBD3 contributes to HNSCC pathogenesis by inducing PD-L1 expression in tumors will have to be determined.

Predicting PD-L1 expression on human cancer cells using next-generation sequencing information in computational simulation models.

PURPOSE: Interaction of the programmed death-1 (PD-1) co-receptor on T cells with the programmed death-ligand 1 (PD-L1) on tumor cells can lead to immunosuppression, a key event in the pathogenesis of many tumors. Thus, determining the amount of PD-L1 in tumors by immunohistochemistry (IHC) is important as both a diagnostic aid and a clinical predictor of immunotherapy treatment success. Because IHC reactivity can vary, we developed computational simulation models to accurately predict PD-L1 expression as a complementary assay to affirm IHC reactivity. METHODS: Multiple myeloma (MM) and oral squamous cell carcinoma (SCC) cell lines were modeled as examples of our approach. Non-transformed cell models were first simulated to establish non-tumorigenic control baselines. Cell line genomic aberration profiles, from next-generation sequencing (NGS) information for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines, were introduced into the workflow to create cancer cell line-specific simulation models. Percentage changes of PD-L1 expression with respect to control baselines were determined and verified against observed PD-L1 expression by ELISA, IHC, and flow cytometry on the same cells grown in culture. RESULT: The observed PD-L1 expression matched the predicted PD-L1 expression for MM.1S, U266B1, SCC4, SCC15, and SCC25 cell lines and clearly demonstrated that cell genomics play an integral role by influencing cell signaling and downstream effects on PD-L1 expression. CONCLUSION: This concept can easily be extended to cancer patient cells where an accurate method to predict PD-L1 expression would affirm IHC results and improve its potential as a biomarker and a clinical predictor of treatment success.