Background: Pregnancy is considered a major risk factor for development and progression of periodontal disease. There are hormonal changes in both estrogen and progesterone hormones in addition to bacterial effect and poor oral hygiene that will enhance development of periodontal disease in pregnant women. Materials and methods: Seventy subjects were enrolled in the study, the subjects with an age range (20-35) years old without any history of systemic disease. The subjects were divided into 20 non-pregnant women they represent the control group (G I), 30 pregnant women with gingivitis (GII) and 20 pregnant women with periodontitis (GIII).All periodontal parameters (plaque index, gingival index, bleeding on probing, probing pocket depth and clinical attachment level) were recorded and 5ml of unstimulated saliva was collected for each subject. The collected saliva was centrifuged and clear supernatant was collected and kept frozen until biochemical analysis of salivary enzymes which included ALP, LDH and salivary Calcium. Results: No significant difference in the mean value of salivary ALP between GI and G II, while there is high significant difference between GI and GIII. There was significant difference in the salivary LDH and Ca levels between control group and group II, while there is highly significant difference of salivary LDH and Ca between group I and group III.There was significant difference in the number of bleeding sites, and probing pocket depth (PPD) among all groups. There was increase in the total number of all scores of PPD (score 1,2 and 3) GII and GIII compared to GI. Conclusions: Thepregnant women revealed more periodontal disease conditions (gingivitis and periodontitis) due to hormonal changes superimposed with microbial infection. Salivary enzymes (ALP, LDH) and salivary calcium are considered as good biochemical markers of periodontal tissue destruction and can be used to evaluate the effect of pregnancy on periodontal health status.
KE Sharquie, MM Al-Waiz, AA Al-Nuaimy, Saudi medical journal, 2005 - Cited by 8
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Digital tampering identification, which detects picture modification, is a significant area of image analysis studies. This area has grown with time with exceptional precision employing machine learning and deep learning-based strategies during the last five years. Synthesis and reinforcement-based learning techniques must now evolve to keep with the research. However, before doing any experimentation, a scientist must first comprehend the current state of the art in that domain. Diverse paths, associated outcomes, and analysis lay the groundwork for successful experimentation and superior results. Before starting with experiments, universal image forensics approaches must be thoroughly researched. As a result, this review of variou
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