Background/Objectives: Nonsurgical periodontal treatment (NSPT) is the gold-standard technique for treating periodontitis. However, an individual’s susceptibility or the inadequate removal of subgingival biofilms could lead to unfavorable responses to NSPT. This study aimed to assess the potential of salivary and microbiological biomarkers in predicting the site-specific and whole-mouth outcomes of NSPT. Methods: A total of 68 periodontitis patients exhibiting 1111 periodontal pockets 4 to 6 mm in depth completed the active phase of periodontal treatment. Clinical periodontal parameters, saliva, and subgingival biofilm samples were collected from each patient at baseline and three months after NSPT. A quantitative PCR assay was used to detect the presence of Fusobaterium nucleatum and Porphyromonas gingivalis in the biofilm samples. Salivary biomarkers including matrix metalloproteinase (MMP)-9, glutathione S-transferase (GST), and Annexin-1 were assayed both qualitatively (Western blot analysis) and quantitively (ELISA). Results: NSPT yielded significant improvements in all clinical parameters, including a reduction in bacterial load and decreased levels of MMP-9 together with increased concentrations of GST and Annexin-1. The binary logistic regression suggested that the overall accuracy of P. gingivalis identification, probing pocket depth, and interproximal sites was 71.1% in predicting successful site-specific outcomes. The salivary biomarker model yielded an overall accuracy of 79.4% in predicting whole-mouth outcomes following NSPT. Conclusions: At baseline, the presence of shallow periodontal pockets at interdental locations with a lower abundance of P. gingivalis is predictive of a favorable response to NSPT at the site level. Decreased salivary MMP-9 associated with increased GST and Annexin-1 levels can predict successful whole-mouth outcomes following NSPT.
Most of drinking water consuming all over the world has been treated at the water treatment plant (WTP) where raw water is abstracted from reservoirs and rivers. The turbidity removal efficiency is very important to supply safe drinking water. This study is focusing on the use of multiple linear regression (MLR) and artificial neural network (ANN) models to predict the turbidity removal efficiency of Al-Wahda WTP in Baghdad city. The measured physico-chemical parameters were used to determine their effect on turbidity removal efficiency in various processes. The suitable formulation of the ANN model is examined throughout many preparations, trials, and steps of evaluation. The predict
Two different polyvinyl alcohol/polyvinyl chloride (PVA/PVC) hollow fiber composite nanofiltration membranes were prepared after PVC hollow fiber membranes were coated using dip-coating method with PVA aqueous solution, which was composed of PVA, fatty alcohol polyoxyethylene ether (AEO9), and water [PVA/AEO9/water (4:0.5:95.5) wt%]. Effect of two different PVC hollow fiber immersion times in coating solution were studied. Cross-section, internal and external surfaces of the PVC hollow fibers and PVA/PVC composite nanofiltration membranes structures were characterized by scanning electron microscopy (SEM), pure water permeation flux and solutes rejection. It was found that, the coating layer thickness on the outer surface of the 19 wt% P
... Show MoreIn this study, field results data were conducted, implemented in 64 biofilm reactors to analyses extract organic matter nutrients from wastewater through a laboratory level nutrient removal process, biofilm layer moving process using anaerobic aerobic units. The kinetic layer biofilm reactors were continuously operating in Turbo 4BIO for BOD COD with nitrogen phosphorous. The Barakia plant is designed to serve 200,000 resident works on biological treatment through merge two process (activated sludge process, moving bed bio reactio MBBR) with an average wastewater flow of 50,000 m3/day the data were collected annually from 2017-2020. The water samples were analysis in the central labor
Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their performance is evaluated usin
... Show MoreExposure assays to magnetized water have so far revealed striking results. The present study was conducted to determine the effects of magnetized water treatment with in different intensities 500 , 1000 and 1500 Gauss on some biological aspects for species of freshwater Gastropod Lymnaea lagotis (Schrank, 1803) which important species in faun of aquatic habitats of Iraq. This species are considered a component of the food chain. The obtained results compared with these species which lived in the river(control). Result of these experiments showed increased significance the shell size (shell high, shell aperture length, shell aperture width and shell width) for L. lagotis with increased intensity magnetized water such as treated water with 1
... Show MoreBreast cancer (BC) is first of the top 10 malignancies in Iraq. Dose‐volume histograms (DVHs) are most commonly used as a plan evaluation tool. This study aimed to assess DVH statistics using three‐dimensional conformal radiotherapies in BC in an adjuvant setting.
A retrospective study of 70 histologically confirmed women diagnosed with BC was reviewed. The study was conducted between November 2020 and May 2021, planning for treatment with adjuvant three‐dimensional conformal radiotherapies. The treatment plan used for each woman was based on an analysis of the volumetric dose, inclu
Water quality planning relies on Biochemical Oxygen Demand BOD. BOD testing takes five days. The Particle Swarm Optimization (PSO) is increasingly used for water resource forecasting. This work designed a PSO technique for estimating everyday BOD at Al-Rustumiya wastewater treatment facility inlet. Al-Rustumiya wastewater treatment plant provided 702 plant-scale data sets during 2012-2022. The PSO model uses the daily data of the water quality parameters, including chemical oxygen demand (COD), chloride (Cl-), suspended solid (SS), total dissolved solids (TDS), and pH, to determine how each variable affects the daily incoming BOD. PSO and multiple linear regression (MLR) findings are compared, and their perfor
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