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Item Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental Records(MDPI, 2023-03-08) Patel, Jay S.; Kumar, Krishna; Zai, Ahad; Shin, Daniel; Willis, Lisa; Thyvalikakath, Thankam P.Objective: To develop two automated computer algorithms to extract information from clinical notes, and to generate three cohorts of patients (disease improvement, disease progression, and no disease change) to track periodontal disease (PD) change over time using longitudinal electronic dental records (EDR). Methods: We conducted a retrospective study of 28,908 patients who received a comprehensive oral evaluation between 1 January 2009, and 31 December 2014, at Indiana University School of Dentistry (IUSD) clinics. We utilized various Python libraries, such as Pandas, TensorFlow, and PyTorch, and a natural language tool kit to develop and test computer algorithms. We tested the performance through a manual review process by generating a confusion matrix. We calculated precision, recall, sensitivity, specificity, and accuracy to evaluate the performances of the algorithms. Finally, we evaluated the density of longitudinal EDR data for the following follow-up times: (1) None; (2) Up to 5 years; (3) > 5 and ≤ 10 years; and (4) >10 and ≤ 15 years. Results: Thirty-four percent (n = 9954) of the study cohort had up to five years of follow-up visits, with an average of 2.78 visits with periodontal charting information. For clinician-documented diagnoses from clinical notes, 42% of patients (n = 5562) had at least two PD diagnoses to determine their disease change. In this cohort, with clinician-documented diagnoses, 72% percent of patients (n = 3919) did not have a disease status change between their first and last visits, 669 (13%) patients’ disease status progressed, and 589 (11%) patients’ disease improved. Conclusions: This study demonstrated the feasibility of utilizing longitudinal EDR data to track disease changes over 15 years during the observation study period. We provided detailed steps and computer algorithms to clean and preprocess the EDR data and generated three cohorts of patients. This information can now be utilized for studying clinical courses using artificial intelligence and machine learning methods.Item Diagnosis and treatment planning using the 2017 classification of periodontal diseases among three dental schools(Wiley, 2022-05-29) Gandhi, Kaveri K.; Katwal, Diksha; Chang, Jennifer; Blanchard, Steven; Shin, Daniel; Maupome, Gerardo; Eckert, George J.; John, VanchitObjectives: The American Academy of Periodontology and the European Federation of Periodontology developed a new classification system for periodontal diseases in 2017. The next step in its widespread implementation involves training dental students to improve consistency in clinical decisions. This study conducted in 2020–2021 aimed to evaluate knowledge in periodontal diagnosis and treatment planning using the new classification, among first, second, third- and fourth-year dental students at Indiana University School of Dentistry (IUSD), University of Texas School of Dentistry at Houston (UTSD), and University of Louisville School of Dentistry (ULSD). Methods: A minimum of 20 dental students per class year from each of the three schools participated. Ten HIPPA de-identified case records and a questionnaire with a fixed list of answer options, comprising two demographic questions and two questions on diagnosis and treatment planning of each case, were presented to the participants. A group of three board-certified periodontists established the answers for all cases which were used to score the appropriateness of diagnosis and treatment planning among the participants. Results: A total of 263 students participated. Overall, 22.6% of IUSD responses, 25.2% of UTSD, and 27.6% of ULSD responses were correct for diagnosis (no statistically significant differences). For the treatment plan, 64.9% of IUSD responses, 66.2% of UTSD, and 68.9% of ULSD responses were correct (no statistically significant differences). Conclusion: Based on the findings from our study, we suggest that additional training be considered to improve the understanding of the 2017 classification of periodontal and peri-implant diseases among dental students.Item The Effects of Nicotine on the Proteolytic Activity of Periodontal Pathogens(2011) Kaeley, Janice,1976-; Gregory, Richard L.; Blanchard, Steven B.; Kowolik, Michael J.; Windsor, L. Jack; Zunt, Susan L., 1951-Periodontal disease is the leading cause of tooth loss in adults. Bacterial biofilm on tooth surfaces is the primary initiator of periodontal disease. Various factors contribute to the severity of periodontal disease including the different virulence factors of the bacteria within the biofilm. In the progression of periodontal disease, the microflora evolves from a predominantly Gram positive microbial population to a mainly Gram negative population. Specific gram negative bacteria with pronounced virulence factors have been implicated in the etiology and pathogenesis of periodontal disease, namely Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola which form the red complex of bacteria. The orange complex bacteria become more dominant in the maturation process of dental plaque and act to bridge the early colonizers of plaque with the later more dominant red complex bacterial and consists of such bacteria as Campylobacter showae, Campylobacter rectus, Fusobacterium nucleatum and Prevotella intermedia. Perhaps the most investigated contributing factor is the relationship between smoking and periodontal disease. When examining the association between cigarette smoking and interproximal bone loss, greater bone loss is associated with higher cigarette consumption, longer duration (i.e., pack year history) and higher lifetime exposure. The presence of various virulence factors such as the production of a capsular material, as well as the proteolytic activity of the various periopathodontic bacteria has been associated with the pathogenesis of periodontitis. Even though many different enzymes are produced in large quantities by these periodontal bacteria, trypsin-like enzymes, chymotrypsin-like enzymes and elastase-like enzymes, as well as dipeptidyl peptidase-like enzymes, have been thought to increase the destructive potential of the bacterium and mediate destruction of the periodontal apparatus. More specifically, it is hypothesized that the proteolytic activity of other clinically important periodontal pathogens, such as Fusobacterium nucleatum, Prevotella intermedia and Porphyromonas assacharolyticus, is increased in the presence of nicotine. The purpose of this study was to determine the effects of nicotine on F. nucleatum, P. intermedia and P. assacharolyticus proteolytic activity. Cultures were maintained on anaerobic blood agar plates containing 3% sheep blood. Bacterial cells were harvested from the plates and washed. Washed F. nucleatum, P. intermedia and P. assacharolyticus cells were incubated with 1 mg/ml of nicotine. Bacterial cells not incubated with nicotine were used as positive controls. Secreted enzymatic activity was measured using the synthetic chromogenic substrates glycyl-L-proline-p-nitroanilide (GPPNA), N-succinyl-L-alanyl-L-alanyl-L-alanyl-p-nitroanilide (SAAAPNA), N-succinyl-alanine-alanine-proline-phenylalanine-p-nitroanilide (SAAPPPNA) and N-α-benzoyl-L-arginine-p-nitroanilide (L-BAPNA) (Sigma-Aldrich Products, St. Louis, MO, USA). Appropriate means and standard deviations were determined for each of the enzymatic activities measured and analysis of variance (ANOVA) was used to compare the groups utilizing a 5% significance level for all comparisons. Results demonstrated that after 60 minutes of incubation of F. nucleatum, P. intermedia and P. assacharolyticus cells with 1 mg/ml of nicotine and the various synthetic substrates, had the following proteolytic activity for GPPNA: 0.83 ± 0.14, 0.72 ± 0.03 and 0.67 ± 0.10, respectively; SAAAPNA: 0.82 ± 0.06, 0.76 ± 0.05 and 0.68 ± 0.08, respectively; SAAPPPNA: 0.90 ± 0.13, 0.85 ± 0.17 and 0.72 ± 0.03, respectively; and BAPNA: 0.81 ± 0.15, 0.74 ± 0.13 and 0.74 ± 0.16, respectively. In conclusion, the results indicate that in the presence of 1 mg/ml of nicotine, the proteolytic activity of F. nucleatum and P. assacharolyticus was increased with all of the synthetic substrates (with statistical significance seen only in the increases with F. nucleatum and GPPNA, SAAAPNA and BAPNA). The proteolytic activity exhibited an increasing trend in activity for P. intermedia with SAAPPPNA and BAPNA but a decreasing trend in activity with GPPNA and SAAAPNA when incubated with 1 mg/ml of nicotine, once again demonstrating no statistical significance for any of the substrates. Therefore, it could be concluded that based on these results nicotine at a concentration of 1 mg/ml may increase the proteolytic activity of periodontal pathogens and thus may increase periodontal disease activity and subsequent periodontal breakdown. Further studies are needed to validate these results utilizing different concentrations of nicotine.Item Peptidoglycan Recognition Proteins in the Pathogenesis of Preeclampsia and Periodontal Disease(2015) Dukka, Himabindu; John, Vanchit; Reiter, Jill; Blanchard, Steven B.; Zunt, Susan; Kowolik, MichaelBackground: Pre-eclampsia a potentially life threatening hypertensive disorder occurring in 3-14% of pregnancies. Its etiology is multifactorial involving the placenta. The only “cure” that currently exists is the delivery of the baby, which is often pre-term. There is no early pregnancy screening test to recognize those at risk. Recently, an altered immune-inflammatory responses at the placental level in response to infectious agents (eg., periodontal pathogens) have been proposed to be etiological for this pregnancy complication. A new class of Pattern Recognition Receptors called Peptidoglycan Recognition Proteins (PGRPs) constituting 4 distinct molecules PGRP 1-4 is emerging as a key player in modulating host responses to peptidoglycan and its breakdown products. A critical knowledge gap exists on the role of PGRPs in the innate immune responses that occur at the maternal-fetal interface in response to pathogens and their components that may be present in maternal circulation secondary to chronic infections. Aim: The aim of this pilot study is to investigate the expression PGRPs in the placenta of pre-eclamptic women. The overall goal is to better understand the association of periodontal disease and adverse pregnancy outcomes. Methods and Materials: This case control study consisted of subjects with: (1) normal term pregnancies (n=7) (2) pre-eclampsia (n=7). Preeclampsia was defined as hypertension (systolic blood pressure of ≥ 140 mm Hg or diastolic blood pressure of ≥ 90 mm Hg on at least 2 occasions, 4 hours to 1 week apart) and proteinuria (≥ 300 mg in a 24-hour urine collection or one dipstick measurement of ≥ 2+). A real time quantitative PCR array was used to analyze the relative mRNA expression of TLR2, TLR4, NOD1, NOD2, PGRP1, PGRP2, PGRP3, and PGRP4. Immunohistochemistry was performed to determine the cell type(s) expressing the PGRP proteins in the placental tissue. Summary statistics (mean, standard deviation, range, 95% confidence interval for the mean) were calculated for PGRP 1-4 expression for each group. Results and conclusions: The PCR data showed the expression of PGRPs 1, 3 and 4 in the placental samples. There was an up-regulation of PGRP-1 (1.4 fold) and down regulation of PGRP-3 (1.3 fold) and PGRP-4 (1.6 fold). TLR2, TLR4 and NOD2 mRNA were also elevated in the placental samples. Immunohistochemistry demonstrated positive staining for PGRPs 3 and 4 in the trophoblasts. The results from this novel research could lead to development of salivary and/or plasmatic biomarkers for early detection of PE and warrants further investigation.