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Periodontal disease (PD) is one of the most prevalent dental diseases. Fortunately, it can be prevented if identified early, especially for high-risk patients. Dental electronic health records (EHRs) could help develop a data-driven personalized prediction model using advanced machine learning development of clinical decision support system (CDSS) as in our Phase I, II AMIA-AI showcase. In phase II, we created a CDSS, the Perio-Risk Scoring system (PRSS), to help clinicians generate perio-scores and diagnoses and identify the influential factors. In Phase III (this study), we implemented and compared the patient’s risk factors information in five periodontal risk assessment tools [periodontal risk assessment (PRA), PreViser, Sonicare, Cigna, and Periodontal Risk Scoring System (PRSS)]. We examined 1) agreement between the risk scores provided by each of the five risk assessment tools of 20 patients’ information and 2) compare the risk scores provided by each tool to the original outcomes (five years outcomes). Fleiss Kappa, Cohen’s Kappa, and percentage agreements were performed to determine the agreements between risk scores and original outcomes. We found a -1.24 Kappa value which indicates disagreement between the risk scores provided by five risk assessment tools. Compared to the original outcomes (five-year disease outcomes), PRSS provided the most accurate prediction (70%), followed by Previser (55%), PRA (35%), Phillips (35%), and Cigna (25%). We conclude that using advanced state-of-the-art informatics methods could help us utilize EHR data optimally to represent the current patient populations and their risk factors to provide the most accurate disease risk score. This may promote preventive strategies at the chairside, hoping to reduce PD prevalence, improve quality of life, and reduce healthcare costs.

Guaranteeing the security, integrity, and traceability of scientific data and its workflow in scientific infrastructures is important for any scientific endeavor. Our project provides flexible, and dynamic data access on the JetStream infrastructure forsharing workflowsin a secure, privacy-preserving, and fine-tuned manner for both local and remote users of different types and different access permissions/rights.

To explore early biomarkers for establishing more sensitive safety evaluation assays in preclinical settings that determine the potential risks during the application of microbicide candidates, three representative microbicide candidates (cellulose sulphate, nonoxynol-9 and tenofovir), whose safety profiles have been well established in clinical trials, were included to gauge the sensitivities of different assays. Both mouse models and cell lines were employed to determine the sensitivities. The recruitment of immune cells at topical mucosal sites and the upregulation of HIV receptor/coreceptors in vitro were identified as highly sensitive biomarkers of the impact of microbicide candidates. Our data suggest that different evaluations/assays have their inherent sensitivities, and at least one assay from each sensitivity level should be included in the safety evaluation algorithm.