CCL28, IL-8, IL-1β and TNF-α may play key roles in the host response to inflammation in periodontal diseases. As the severity of periodontal diseases increases, destruction of periodontal tissues also increases. Inflammation is one among many factors that trigger periodontal tissue destruction. Identification of the mediators that influence the development and progression of inflammation in periodontal diseases may be very important in understanding the prognoses of periodontal diseases.

As a result of the observed vascular and cell activity changes that occur within patients diagnosed with DM, periodontal diseases become more severe. These changes hinder the migration and the ability of chemotactic factors and leukocytes to protect periodontal tissues from the effects of microorganisms. In order to eliminate microorganisms, the epithelial cells in patients with DM may release more hBD-1 and hBD-3 into the gingival crevicular fluid. Determining the amount of hBD-1 and hBD-3 in the gingival crevicular fluid of patients with and without DM will help to elucidate the relationship among hBD-1, hBD-3, DM and periodontal disease.

Epithelial cells in contact with microorganisms release LL-37 and hBD-2 to eliminate them. The release response of LL-37 and hBD-2 formed against microorganisms can change depending on factors such as smoking, which activates the nicotinic receptors present on epithelial surfaces. This interaction can increase the release of LL-37 and hBD-2. Smoking may also affect the capillary tissues and reduce leukocytic chemotaxis. The increased number of colonized microorganisms may lead to higher levels of LL-37 and hBD-2 release in the tissues of smokers than in non-smokers.

An increased adrenomedullin level was found in individuals with chronic periodontitis and also in individuals with diabetes mellitus. It is thought that the effect of diabetes mellitus on the pathogenesis of chronic periodontitis could have been achieved through antimicrobial peptides such as adrenomedullin, or that increased adrenomedullin was released in individuals with diabetes mellitus in order to ensure no further periodontal tissue loss.

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Background: Beta-2 microglobulin (B2M) and alpha-2 macroglobulin (A2M) play key roles in the immune system. The aim of this study was to compare B2M and A2M levels in patients with different periodontal diseases. Methods: Eighty patients (20 periodontally healthy, 20 with gingivitis, 20 with chronic periodontitis and 20 with generalized aggressive periodontitis) were enrolled in the study. The analysis of B2M and A2M was performed on gingival crevicular fluid (GCF) using an enzyme-linked immunosorbent assay in GCF. Results: The total levels of B2M and A2M were statistically lower in the periodontally healthy group than in the other groups (p < 0.05) and significantly higher in the generalized aggressive periodontitis group compared to the other groups (p < 0.05). Conclusions: B2M and A2M play key roles in the balance between periodontal health and disease. It is proposed that tissues release B2M and A2M to stop inflammation and inhibit the proliferation of microorganisms and this may be the reason for the high levels of B2M and A2M in the generalized aggressive periodontitis and chronic periodontitis groups. B2M and A2M are assumed to be user-friendly and cost-effective markers for periodontal disease to identify asymptomatic diseases.

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In this paper, we propose a new hybrid Local Binary Pattern (LBP) based on Hessian matrix and Attractive Center-Symmetric LBP (ACS-LBP), called Hess-ACS-LBP. The Hessian matrix provides the directional derivative information of different texture regions, while ACS-LBP reveals the local texture features efficiently. To obtain the macro-and micro-structure textural changes, Hessian matrix is calculated in a multiscale schema. Multiscale Hessian matrix presents the intrinsic local geometry of the texture changes. The magnitude information of the Hessian matrix is used in the ACS-LBP method. A cross-scale joint coding strategy is used to construct Hess-ACS-LBP descriptor. Finally, histogram concatenation is carried out. Extensive experiments on eight texture databases of CUReT, USPTex, KTH-TIPS2b, MondialMarmi, OuTeX TC_00013, XU HR, ALOT and STex validate the efficiency of the proposed method. The proposed Hess-ACS-LBP method achieves about 20% improvement over the original LBP method and 1%-11% improvement over the other state-of-the-art hand-crafted LBP methods in terms of classification accuracy. Besides, the experimental results show that the proposed method achieves up to 32% better results than the state-of-the-art deep learning based methods. Especially, the performance of the proposed method on ALOT and STex datasets containing many classes is remarkable. INDEX TERMS Hessian matrix, feature extraction, local binary patterns, texture classification.