Background: diabetes is a metabolic disease characterized by hyperglycemia that results in deficiency or absence of insulin production. The dental caries and gingivitis/periodontitis are widespread chronic diseases in diabetes. The aim of the present study was determined the salivary matrix metalloproteinase (MMP-8), Secretory Leukocyte Peptidase Inhibitor (SLPI) and oral health status among uncontrolled diabetic group in comparison with healthy control group. Materials and Methods: The total sample composed of 90 adults aged (18-35) years. Divided into 60 uncontrolled diabetic patients (HbA1c >7%) and 30 healthy control group. Unstimulated saliva was collected from each subject with type-I DM, BMI, duration of diabetes, HbA1c%, DMFT, gingival index (GI) and periodontal disease index were recorded during clinical visit. Level of salivary MMP-8 and SLPI was measured by using ELISA immunoassay analysis. Results: The DMFT was highly significant higher among type-I DM group than control group (p<0.001),GI and pocket depthwas significant difference (p=0.002, p<0.001 respectively) between two groups except the attachment loss with no significant difference (p=0.06). The Salivary MMP-8 was elevated, whereas SLPI was lowered in individuals with type I diabetes mellitus in comparison to the healthy controls, but statistically was non-significant. Analysis among uncontrolled diabetic patients revealed that the HbA1c% correlate positively significant with salivary MMP-8 (r=0.321, p=0.012), SLPI (r=0.276, P=0.033) andattachment loss (r=0.353, p=0.006); however the correlation between MMP-8 & SLPIwas a significant in negative direction (r=-0.395, p=0.002). Conclusion:The DMFT, pocket depth and gingival index was higher in uncontrolled diabetes group. HbA1c% was positively correlation withMMP-8, SLPI and attachment loss in uncontrolled diabetic group. Salivary SLPI associated inversely with significant correlation with salivary MMP-8.
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A stochastic process {Xk, k = 1, 2, ...} is a doubly geometric stochastic process if there exists the ratio (a > 0) and the positive function (h(k) > 0), so that {α 1 h-k }; k ak X k = 1, 2, ... is a generalization of a geometric stochastic process. This process is stochastically monotone and can be used to model a point process with multiple trends. In this paper, we use nonparametric methods to investigate statistical inference for doubly geometric stochastic processes. A graphical technique for determining whether a process is in agreement with a doubly geometric stochastic process is proposed. Further, we can estimate the parameters a, b, μ and σ2 of the doubly geometric stochastic process by using the least squares estimate for Xk a
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