Keiso Takahashi

Salivary pathogen and serum antibody to assess the progression of chronic periodontitis: a 24-mo prospective multicenter cohort study

BACKGROUND AND OBJECTIVE A diagnosis of periodontitis progression is presently limited to clinical parameters such as attachment loss and radiographic imaging. The aim of this multicenter study was to monitor disease progression in patients with chronic periodontitis during a 24-mo follow-up program and to evaluate the amount of bacteria in saliva and corresponding IgG titers in serum for determining the diagnostic usefulness of each in indicating disease progression and stability. MATERIAL AND METHODS A total of 163 patients with chronic periodontitis who received trimonthly follow-up care were observed for 24 mo. The clinical parameters and salivary content of Porphyromonas gingivalis, Prevotella intermedia and Aggregatibacter actinomycetemcomitans were assessed using the modified Invader PLUS assay, and the corresponding serum IgG titers were measured using ELISA. The changes through 24 mo were analyzed using cut-off values calculated for each factor. One-way ANOVA or Fishers exact test was used to perform between-group comparison for the data collected. Diagnostic values were calculated using Fishers exact test. RESULTS Of the 124 individuals who completed the 24-mo monitoring phase, 62 exhibited periodontitis progression, whereas 62 demonstrated stable disease. Seven patients withdrew because of acute periodontal abscess. The ratio of P. gingivalis to total bacteria and the combination of P. gingivalis counts and IgG titers against P. gingivalis were significantly related to the progression of periodontitis. The combination of P. gingivalis ratio and P. gingivalis IgG titers was significantly associated with the progression of periodontitis (p = 0.001, sensitivity = 0.339, specificity = 0.790). CONCLUSIONS It is suggested that the combination of P. gingivalis ratio in saliva and serum IgG titers against P. gingivalis may be associated with the progression of periodontitis.

Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters.

There is neither a single clinical, microbiological, histopathological or genetic test, nor combinations of them, to discriminate aggressive periodontitis (AgP) from chronic periodontitis (CP) patients. We aimed to estimate probability density functions of clinical and immunologic datasets derived from periodontitis patients and construct artificial neural networks (ANNs) to correctly classify patients into AgP or CP class. The fit of probability distributions on the datasets was tested by the Akaike information criterion (AIC). ANNs were trained by cross entropy (CE) values estimated between probabilities of showing certain levels of immunologic parameters and a reference mode probability proposed by kernel density estimation (KDE). The weight decay regularization parameter of the ANNs was determined by 10-fold cross-validation. Possible evidence for 2 clusters of patients on cross-sectional and longitudinal bone loss measurements were revealed by KDE. Two to 7 clusters were shown on datasets of CD4/CD8 ratio, CD3, monocyte, eosinophil, neutrophil and lymphocyte counts, IL-1, IL-2, IL-4, INF-γ and TNF-α level from monocytes, antibody levels against A. actinomycetemcomitans (A.a.) and P.gingivalis (P.g.). ANNs gave 90%–98% accuracy in classifying patients into either AgP or CP. The best overall prediction was given by an ANN with CE of monocyte, eosinophil, neutrophil counts and CD4/CD8 ratio as inputs. ANNs can be powerful in classifying periodontitis patients into AgP or CP, when fed by CE values based on KDE. Therefore ANNs can be employed for accurate diagnosis of AgP or CP by using relatively simple and conveniently obtained parameters, like leukocyte counts in peripheral blood. This will allow clinicians to better adapt specific treatment protocols for their AgP and CP patients.

Mathematical Modeling Suggests That Periodontitis Behaves as a Non-Linear Chaotic Dynamical Process

BACKGROUND This study aims to expand on a previously presented cellular automata model and further explore the non-linear dynamics of periodontitis. Additionally the authors investigated whether their mathematical model could predict the two known types of periodontitis, aggressive (AgP) and chronic periodontitis (CP). METHODS The time evolution of periodontitis was modeled by an iterative function, based on the hypothesis that the host immune response level determines the rate of periodontitis progression. The chaotic properties of this function were investigated by direct iteration, and the model was validated by immunologic and clinical parameters derived from two clinical study populations. RESULTS Periodontitis can be described as chaos with the level of the host immune response determining its progression rate; the dynamics of the proposed model suggest that by increasing the host immune response level, periodontitis progression rate decreases. Renormalization transformations show the presence of two overlapping zones of disease activity corresponding to AgP and CP. By k-means cluster analysis, immunologic parameters corroborated the findings of the renormalization transformations. Periodontitis progression rates are modeled to scale with a power law of 1.3, and the mean exponential speed of the system is found to be 1.85 (metric entropy); clinical datasets confirmed the mathematical estimates. CONCLUSIONS This study introduces a mathematical model that identifies periodontitis as a non-linear chaotic process. It offers a quantitative assessment of the disease progression rate and identifies two zones of disease activity that correspond to the existing classification of periodontitis in the AgP and CP types.

Detection of IgA Subclasses and J Chain mRNA Bearing Plasma Cells in Human Dental Periapical Lesions by In Situ Hybridization

Humoral immune responses are implicated in the pathogenesis of human dental periapical lesions. To elucidate whether IgA-associated immune systems play a role in the lesions, the in situ hybridization technique was used to detect J chain mRNA expression, which is correlated with the secretion of dimeric IgA. In addition, IgA subclass mRNA-expressing cells were also investigated by double target in situ hybridization (ISH) methodology using digoxigenin- and biotin-labeled IgA subclass specific oligonucleotide probes. This double target ISH technique involved immunochemical detection with an alkaline phosphatase-conjugated antibody and a peroxidase conjugated avidin-biotin complex system to detect IgA subclass mRNA in the formalin-fixed, paraffin wax embedded periapical tissue sections. Twenty-four biopsy samples (16 periapical granulomas and 8 radicular cysts) were examined. IgA subclass mRNA positive plasma cells were detected in all samples. IgA1 mRNA-expressing cells were predominant both in granulomas and cysts (mean = 75.3 +/- 11.2%, 64.8 +/- 21.3%, respectively), and the IgA1 proportion was higher in granulomas than in cysts, although no significant difference was seen between the two lesions (p = 0.132). J chain mRNA positive cells were very sparsely detected in 21/24 cases. The median percentages of J chain mRNA positive cells/IgA mRNA positive plasma cells (4.7%, range 0.3-13.6%) in cysts were significantly higher than in granulomas (1.3%, range 0-7.7%; p = 0.03). This result supports the hypothesis that dimeric IgA may be more actively produced in radicular cysts than in granulomas. These features are thought to reflect the local activation of the periapical immune system in response to environmental factors and indicate that secretory IgA mediated immune defense systems appear to play little role in these lesions. Our results indicate that the IgA-associated immune response in periapical lesions is more similar to serum or systemic IgA responses than to mucosa-associated immune responses where dimeric IgA predominates.

Assessing the progression of chronic periodontitis using subgingival pathogen levels: a 24-month prospective multicenter cohort study

BackgroundThe diagnosis of the progression of periodontitis presently depends on the use of clinical symptoms (such as attachment loss) and radiographic imaging. The aim of the multicenter study described here was to evaluate the diagnostic use of the bacterial content of subgingival plaque recovered from the deepest pockets in assessing disease progression in chronic periodontitis patients.MethodsThis study consisted of a 24-month investigation of a total of 163 patients with chronic periodontitis who received trimonthly follow-up care. Subgingival plaque from the deepest pockets was recovered and assessed for bacterial content of Porphyromonas gingivalis, Prevotella intermedia, and Aggregatibacter actinomycetemcomitans using the modified Invader PLUS assay. The corresponding serum IgG titers were measured using ELISA. Changes in clinical parameters were evaluated over the course of 24 months. The sensitivity, specificity, and prediction values were calculated and used to determine cutoff points for prediction of the progression of chronic periodontitis.ResultsOf the 124 individuals who completed the 24-month monitoring phase, 62 exhibited progression of periodontitis, whereas 62 demonstrated stable disease. The P. gingivalis counts of subgingival plaque from the deepest pockets was significantly associated with the progression of periodontitis (p < 0.001, positive predictive value = 0.708).ConclusionsThe P. gingivalis counts of subgingival plaque from the deepest pockets may be associated with the progression of periodontitis.

Glutathione Can Efficiently Prevent Direct Current–induced Cytotoxicity

We have reported that direct current (DC) with antibacterial agents used in iontophoresis for root canal disinfection induced host cell necrotic cytotoxicity, and this DC-induced cytotoxicity may be because of generated free radicals and metal ions eluted from metal electrodes. Iontophoresis is still used in some cases, and thus it is necessary to consider how we may prevent DC-induced cytotoxicity of host cells of periapical lesions. Thus, we compared the protective effects of various antioxidants on the DC-induced cytotoxicity against host cells. N-acetyl-L-cysteine and glutathione (GSH) efficiently prevented DC-induced cytotoxicity against human polymorphonuclear cells (PMNs) (p < 0.01). The DC-induced cytotoxicity against PMNs was significantly enhanced by buthionine sulfoximine (p < 0.05), an inhibitor of GSH synthesis, and its effect was rescued by adding the exogenous GSH (p < 0.01). In addition, DC treatment reduced the intracellular GSH levels in a time-dependent manner (p < 0.05). Transmission electron microscopy showed that the DC induced the intense vacuolization and accumulation of cellular debris in autophagolysosomes, and these morphological changes were blocked by adding exogenous GSH. These results suggest that GSH, a thiol antioxidant, effectively prevents the DC-induced cytotoxicity.

Aggressive periodontitis defined by recursive partitioning analysis of immunologic factors.

BACKGROUND The present study aims to extend recent findings of a non-linear model of the progression of periodontitis supporting the notion that aggressive periodontitis (AgP) and chronic periodontitis (CP) are distinct clinical entities. This approach is based on the implementation of recursive partitioning analysis (RPA) to evaluate a series of immunologic parameters acting as predictors of AgP and CP. METHODS RPA was applied to three population samples, that were retrieved from previous studies, using 17 immunologic parameters. The mean values of the parameters in control subjects were used as the cut-off points. Leave-one-out cross-validation (LOOCV) prediction errors were estimated in the proposed models, as well as the Kullback-Leibler divergence (DKL) of the distribution of positive results in AgP compared to CP and negative results in CP compared to AgP. RESULTS Seven classification trees were derived showing that the relationship of interleukin (IL)-4, IL-1, IL-2 has the highest potential to rule out or rule in AgP. On the other hand, immunoglobulin (Ig)A, IgM used to rule out AgP and cluster of differentiation 4 (CD4)/CD8, CD20 used to rule in AgP showed the least LOOCV cost. Penalizing DKL with LOOCV cost promotes the IL-4, IL-1, IL-2 model for ruling out AgP, whereas the single CD4/CD8 ratio with a lowered discrimination cut-off point was used to rule in AgP. CONCLUSIONS Although a test is unlikely to have both high sensitivity and high specificity, the use of immunologic parameters in the right model can efficiently complement a clinical examination for ruling out or ruling in AgP.

Three-Dimensional Slit Width Measurement for Long Precision Slot Dies

This paper describes a measurement method for three-dimensional (3D) slit width deviations of long precision slot dies, which are essential for process control in manufacturing. A sensor unit consisting of two laser probes with their measurement axes aligned along the same Z-directional line but with opposite measurement directions, is placed between the two parts of the slot die to scan the two opposing surfaces of the parts along the X- and Y-axes. The variation of the sum of the laser probe outputs, which shows the deviation of the distance between the two surfaces, corresponds to the deviation of the slit width in the Z-direction. The 3D slit width deviations can be obtained accurately through scanning the entire surface in the X Y plane. In addition, the surface flatness of the parts can also be measured accurately by adding one more probe. Measurement experiments have been conducted on a precision grinding machine. The measurement results have indicated that the 3D slit width deviations and flatness can be measured with a repeatability error of less than 1 micron, which meets the requirement for quality control of slot dies.