Background: Implant stability is considered one of the most important factors affecting healing and successful osseointegration of dental implants. The aims of the study were to measure the implant stability quotient (ISQ) values during the healing period and to determine the factors that affect implant stability. Materials and methods: Thirty patients enrolled in the study (17 female, 13 male). They received 44 Implantium® Dental Implants located as the following: 22 implants in maxillary jaw, 22 implants in mandibular jaw from them 17 implants in anterior segment and 27 in posterior segment. The bone density determined using interactive CT scan and classified according to the Misch bone density classification (29 implants in (D3), 15 implants in (D4)). Resonance frequency analysis was used for direct measurement of implant stability on the day of implant placement and 8, 16 and 24 weeks after implant placement. Results: The lowest mean of average ISQ was at the 8th week (69.5) and then the mean increased to reach at the 24th week (76.8). Mandibular implants showed significantly higher ISQ values than maxillary implants. Implants placed in the posterior segment of the jaw had significantly higher ISQ values than implants in the anterior segment. A significant, positive linear correlation was observed between the implant diameter and the implant stability (r=0.343 p<0.001). Conclusion: Resonance frequency analysis was non-invasive diagnostic tool for detecting changes in implant stability during the healing period. The factors that affect implant stability were implant diameter and implant location (maxilla\ mandible, anterior\ posterior).
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Anemia in pregnancy can considerably elevate the maternal mortality risk and can negatively distress the development of fetus.
To assess the association between neonatal outcomes and maternal anemia (MA) among pregnant women (PW).
The present work is a clinical prospective one performed at Al-Elwiya Maternity
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Churning of employees from organizations is a serious problem. Turnover or churn of employees within an organization needs to be solved since it has negative impact on the organization. Manual detection of employee churn is quite difficult, so machine learning (ML) algorithms have been frequently used for employee churn detection as well as employee categorization according to turnover. Using Machine learning, only one study looks into the categorization of employees up to date. A novel multi-criterion decision-making approach (MCDM) coupled with DE-PARETO principle has been proposed to categorize employees. This is referred to as SNEC scheme. An AHP-TOPSIS DE-PARETO PRINCIPLE model (AHPTOPDE) has been designed that uses 2-stage MCDM s
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Objective: The aims of present study to detect the effectiveness of instruction program of non-pharmacological guideline on blood pressure and laboratory test.
Methodology: A pre-experimental study was conducted in Al-Sader Teaching Hospital from 8th of September 2019 to 25th of May 2020, in order to find out the effectiveness of instruction program concerning non-pharmacological guideline on controlling essential hypertension among patients. A non- probability (purposive sample) of 50 patients with essential hypertension is selected. Those patients are already diagnosed with Essential Hypertension
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