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Diagnostic potential of salivary interleukin‐17, RANKL, and OPG to differentiate between periodontal health and disease and discriminate stable and unstable periodontitis: A case‐control study
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Abstract<sec><title>Background and Aims

Limitations of the conventional diagnostic techniques urged researchers to seek novel methods to predict, diagnose, and monitor periodontal disease. Use of the biomarkers available in oral fluids could be a revolutionary surrogate for the manual probing/diagnostic radiograph. Several salivary biomarkers have the potential to accurately discriminate periodontal health and disease. This study aimed to determine the diagnostic sensitivity and specificity of salivary interleukin (IL)‐17, receptor activator of nuclear factor‐κB ligand (RANKL), osteoprotegerin (OPG), RANKL/OPG for differentiating (1) periodontal health from disease and (2) stable and unstable periodontitis.

Methods

Participants with periodontitis (n = 50) and gingivitis (n = 25), both diseases represented the cases, and subjects with healthy periodontium (n = 15) as a control were recruited for this study. Periodontitis cases were further equally subdivided into stable and unstable. Whole unstimulated salivary sample were collected from all participants. Periodontal parameters including bleeding on probing, probing pocket depth, clinical attachment loss, and number of missing teeth were recorded. The protein levels of salivary IL‐17, RANKL, and OPG were determined by using enzyme‐linked immunosorbent assays technique.

Results

Salivary IL‐17, OPG, RANKL, and RANKL/OPG showed high sensitivity and specificity to differentiate periodontal health from gingivitis and periodontitis. Similar pattern was observed in discriminating stable and unstable periodontitis. Salivary IL‐17 and RANKL showed a good accuracy to differentiate gingivitis from periodontitis. However, OPG and RANKL/OPG did not exhibit enough sensitivity and specificity to differentiate the latter conditions.

Conclusion

Salivary IL‐17, RANKL, OPG, and RANKL/OPG system are potential candidates for differentiating periodontal health and disease and discriminate stable and unstable periodontitis.

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Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

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Efficacy of MMP-8 Level in Gingival Crevicular Fluid to Predict the Outcome of Nonsurgical Periodontal Treatment: A Systematic Review
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Thu Dec 31 2015
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Publication Date
Mon Oct 05 2026
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Publication Date
Wed May 29 2019
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Iraqi Journal Of Physics
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Fe, Co and Sb nanopowders were fruitfully prepared by electrical wire explosion method in Double distilled and de-ionized water (DDDW) media. The formation of iron, cobalt and antimony (FeCoSb) alloy nanopowder was monitored by X-ray diffraction. The x-ray diffraction pattern indicates that there are iron, cobalt and antimony peaks. Optical properties of this alloy nanoparticles were characterized by UV-Visible absorption spectra. The absorption peak position is shifted to the lower wavelengths when the current increases. That means the mean size of the nanoparticles controlled by changing the magnitude of the current. The surface morphological analysis is carried out by employing Scanning Electron Microscope (SEM). Particles with varies

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