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Because of some disadvantages of chemical disinfection in dental practice (especially denture cleaning), we investigated the effects of physical methods on Candida albicans biofilms. For this purpose, the antifungal efficacy of three different low-temperature plasma devices (an atmospheric pressure plasma jet and two different dielectric barrier discharges (DBDs)) on Candida albicans biofilms grown on titanium discs in vitro was investigated. As positive treatment controls, we used 0.1% chlorhexidine digluconate (CHX) and 0.6% sodium hypochlorite (NaOCl). The corresponding gas streams without plasma ignition served as negative treatment controls. The efficacy of the plasma treatment was determined evaluating the number of colony-forming units (CFU) recovered from titanium discs. The plasma treatment reduced the CFU significantly compared to chemical disinfectants. While 10 min CHX or NaOCl exposure led to a CFU log10 reduction factor of 1.5, the log10 reduction factor of DBD plasma was up to 5. In conclusion, the use of low-temperature plasma is a promising physical alternative to chemical antiseptics for dental practice.
Because of its antimicrobial properties, nonthermal plasma could serve as an alternative to chemical antisepsis in wound treatment. Therefore, this study investigated the inactivation of biofilm-embedded Pseudomonas aeruginosa SG81 by a surface barrier-discharged (SBD) plasma for 30, 60, 150 and 300 s. In order to optimize the efficacy of the plasma, different carrier gases (argon, argon admixed with 1% oxygen, and argon with increased humidity up to approx. 80%) were tested and compared against 0.1% chlorhexidine digluconate (CHG) exposure for 600 s. The antimicrobial efficacy was determined by calculating the difference between the numbers of colony-forming units (CFU) of treated and untreated biofilms. Living bacteria were distinguished from dead by fluorescent staining and confocal laser scanning microscopy. Both SBD plasmas and CHG showed significant antimicrobial effects compared to the untreated control. However, plasma treatment led to a higher antimicrobial reduction (argon plasma 4.9 log<sub>10</sub> CFU/cm<sup>2</sup>, argon with admixed oxygen 3 log<sub>10</sub> CFU/cm<sup>2</sup>, and with increased gas humidity 2.7 log<sub>10</sub> CFU/cm<sup>2</sup> after 300 s) compared to CHG. In conclusion, SBD plasma is suitable as an alternative to CHG for inactivation of Pseudomonas aeruginosa embedded in biofilm. Further development of SBD plasma sources and research on the role of carrier gases and humidity may allow their clinical application for wound management in the future.
Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites (n = 284) obtained by mass spectrometry among a subsample (n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites (n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.
The aim of the present study was to construct a biological age score reflecting one’s physiologic capability and aging condition with respect to tooth loss over 10 y. From the follow-up to the population-based Study of Health in Pomerania (i.e., SHIP-2), 2,049 participants were studied for their baseline biomarker measures 10 y before (i.e., in SHIP-0). Metabolic and periodontal data were regressed onto chronological age to construct a score designated as “biological age.” For either sex separately, the impact of this individualized score was used to predict tooth loss in the follow-up cohort in comparison with each participant’s chronological age. Outcome data after 10 y with respect to tooth loss, periodontitis, obesity, and inflammation were shown to be better for biologically younger subjects than as expected by their chronological age, whereas for the older subjects, data were worse. Especially for tooth loss, a striking increase was observed in subjects whose biological age at baseline appeared to be higher than their chronological age. Biological age produced significantly better tooth loss predictions than chronological age (P < 0.001). Areas under receiver operating characteristic curves for tooth loss of ≥3 teeth in men during follow-up were 0.811 and 0.745 for biological and chronological age, respectively. For women, these figures were 0.788 and 0.724. For total tooth loss, areas under the curve were 0.890 and 0.749 in men and 0.872 and 0.752 in women. Biological age combines various measures into a single score and allows identifying individuals at increased risk of tooth loss.
Evidence is limited regarding whether periodontal treatment improves hemoglobin A1c (HbA1c) among people with prediabetes and periodontal disease, and it is unknown whether improvement of metabolic status persists >3 mo. In an exploratory post hoc analysis of the multicenter randomized controlled trial “Antibiotika und Parodontitis” (Antibiotics and Periodontitis)—a prospective, stratified, double-blind study—we assessed whether nonsurgical periodontal treatment with or without an adjunctive systemic antibiotic treatment affects HbA1c and high-sensitivity C-reactive protein (hsCRP) levels among periodontitis patients with normal HbA1c (≤5.7%, n = 218), prediabetes (5.7% < HbA1c < 6.5%, n = 101), or unknown diabetes (HbA1c ≥ 6.5%, n = 8) over a period of 27.5 mo. Nonsurgical periodontal treatment reduced mean pocket probing depth by >1 mm in both groups. In the normal HbA1c group, HbA1c values remained unchanged at 5.0% (95% CI, 4.9% to 6.1%) during the observation period. Among periodontitis patients with prediabetes, HbA1c decreased from 5.9% (95% CI, 5.9% to 6.0%) to 5.4% (95% CI, 5.3% to 5.5%) at 15.5 mo and increased to 5.6% (95% CI, 5.4% to 5.7%) after 27.5 mo. At 27.5 mo, 46% of periodontitis patients with prediabetes had normal HbA1c levels, whereas 47.9% remained unchanged and 6.3% progressed to diabetes. Median hsCRP values were reduced in the normal HbA1c and prediabetes groups from 1.2 and 1.4 mg/L to 0.7 and 0.7 mg/L, respectively. Nonsurgical periodontal treatment may improve blood glucose values among periodontitis patients with prediabetes (ClinicalTrials.gov NCT00707369).
Prediction models learn patterns from available data (training) and are then validated on new data (testing). Prediction modeling is increasingly common in dental research. We aimed to evaluate how different model development and validation steps affect the predictive performance of tooth loss prediction models of patients with periodontitis. Two independent cohorts (627 patients, 11,651 teeth) were followed over a mean ± SD 18.2 ± 5.6 y (Kiel cohort) and 6.6 ± 2.9 y (Greifswald cohort). Tooth loss and 10 patient- and tooth-level predictors were recorded. The impact of different model development and validation steps was evaluated: 1) model complexity (logistic regression, recursive partitioning, random forest, extreme gradient boosting), 2) sample size (full data set or 10%, 25%, or 75% of cases dropped at random), 3) prediction periods (maximum 10, 15, or 20 y or uncensored), and 4) validation schemes (internal or external by centers/time). Tooth loss was generally a rare event (880 teeth were lost). All models showed limited sensitivity but high specificity. Patients’ age and tooth loss at baseline as well as probing pocket depths showed high variable importance. More complex models (random forest, extreme gradient boosting) had no consistent advantages over simpler ones (logistic regression, recursive partitioning). Internal validation (in sample) overestimated the predictive power (area under the curve up to 0.90), while external validation (out of sample) found lower areas under the curve (range 0.62 to 0.82). Reducing the sample size decreased the predictive power, particularly for more complex models. Censoring the prediction period had only limited impact. When the model was trained in one period and tested in another, model outcomes were similar to the base case, indicating temporal validation as a valid option. No model showed higher accuracy than the no-information rate. In conclusion, none of the developed models would be useful in a clinical setting, despite high accuracy. During modeling, rigorous development and external validation should be applied and reported accordingly.
For the goal of individualized medicine, it is critical to have clinical phenotypes at hand which represent the individual pathophysiology. However, for most of the utilized phenotypes, two individuals with the same phenotype assignment may differ strongly in their underlying biological traits. In this paper, we propose a definition for individualization and a corresponding statistical operationalization, delivering thereby a statistical framework in which the usefulness of a variable in the meaningful differentiation of individuals with the same phenotype can be assessed. Based on this framework, we develop a statistical workflow to derive individualized phenotypes, demonstrating that under specific statistical constraints the prediction error of prediction scores contains information about hidden biological traits not represented in the modeled phenotype of interest, allowing thereby internal differentiation of individuals with the same assigned phenotypic manifestation. We applied our procedure to data of the population-based Study of Health in Pomerania to construct a refined definition of obesity, demonstrating the utility of the definition in prospective survival analyses. Summarizing, we propose a framework for the individualization of phenotypes aiding personalized medicine by shifting the focus in the assessment of prediction models from the model fit to the informational content of the prediction error.
Abstract
Aim
The aim of this study was to evaluate the effect of non‐surgical periodontal therapy on circulating levels of the systemic inflammation‐associated biomarkers orosomucoid (ORM), high‐sensitivity C‐reactive protein (hsCRP), chemerin, and retinol‐binding protein 4 (RBP4) in overweight or normal‐weight patients with periodontitis at 27.5 months after therapy.
Materials and methods
This exploratory subanalysis includes patients from the ABPARO‐trial (ClinicalTrials.gov NCT00707369). The per‐protocol collective provided untreated periodontitis patients with high (≥28 kg/m2) or moderate (21–24 kg/m2) BMI. Out of the per‐protocol collective, 80 patients were randomly selected and stratified for BMI group, sex, and treatment group (antibiotics/placebo), resulting in 40 overweight and normal‐weight patients. Patients received non‐surgical periodontal therapy and maintenance at 3‐month intervals. Plasma samples from baseline and 27.5 months following initial treatment were used to measure the concentrations of ORM, hsCRP, chemerin, and RBP4.
Results
At the 27.5‐month examination, ORM and hsCRP decreased noticeably in the overweight group (ORM: p = .001, hsCRP: p = .004) and normal‐weight patients (ORM: p = .007, hsCRP: p < .001). Chemerin decreased in the overweight group (p = .048), and RBP4 concentrations remained stable.
Conclusion
Non‐surgical periodontal therapy reduced systemically elevated inflammation‐associated biomarkers in periodontitis patients. These improvements were more pronounced in overweight patients than in normal‐weight patients.