Objective(s): The study aims to assess the early detection of early detection of first degree relatives to type-II
diabetes mellitus throughout the diagnostic tests of Glycated Hemoglobin A1C. (HgbA1C), Oral Glucose Tolerance
Test (OGTT) and to find out the relationship between demographic data and early detection of first degree
relatives to type-II diabetes mellitus.
Methodology: A purposive "non-probability" sample of (200) subjects first degree relatives to type-II diabetes
mellitus was selected from National Center for Diabetes Mellitus/Al-Mustansria University and Specialist Center
for Diabetes Mellitus and Endocrine Diseases/Al-kindy. These related persons have presented the age of (40-70)
years old. A questionnaire was constructed for the purpose of the study, it is composed of (3) major parts, and
overall items, which are included in the questionnaire are (76) items. Reliability and validity of the questionnaire
were determined through a pilot study which is carried out during the period of August, 1
st
, 2008 to February, 30th
2009. The study instrument and structured interview technique were used as means of data collection. The data
were analyzed through the application of the descriptive statistical data analysis approach (Frequency and
Percentage) and the inferential statistical data analysis approach Chi-square, Pearson correlation coefficient.
Results: The results of the study confirmed that the mean of age is (55.7) year, and the majority of the sample are
male, first degree relatives with diabetes mellitus type-II are within positive bio-social aspect and laboratory
screening had an effect on the incidence of diabetes mellitus type-II for first degree relatives to type-II diabetes
mellitus.
Recommendations: The study recommends that the number of diabetes centers should be increased in Baghdad
and Governorates, promote of HbA1c test from general hospitals laboratories, guide notebook about the
predisposing factors of diabetes mellitus in his family, periodic screening for pre-diabetes and diabetes in high risk,
asymptomatic, undiagnosed adults within the health care setting, prevention program to prevent and control on
the predisposing risk factors for nondependent diabetes mellitus type-II and complication
Background: Insulin resistance is associated with metabolic syndrome , type 2 diabetes and representing a risk factor for cardiovascular disease . This relationship may be modulated to some extent by age related changes in sex hormone status.. In particular, reduced total testosterone (TT) levels have been associated with insulin resistance and subsequent risk for developing type 2 diabetes. Aim of study: we examined whether low total testosterone level were associated with insulin resistance in young adult men. Methods: a total of 83 men (young adult men) divided into 2 group : (group1 ) 49 men with a risk factor for insu
... Show MoreAmong the different passive techniques heat pipe heat exchanger (HPHE) seems to be the most effective one for energy saving in heating ventilation and air conditioning system (HVAC). The applications for nanofluids with high conductivity are favorable to increase the thermal performance in HPHE. Even though the nanofluid has the higher heat conduction coefficient that dispels more heat theoretically but the higher concentration will make clustering .Clustering is a problem that must be solved before nanofluids can be considered for long-term practical uses. Results showed that the maximum value of relative power is 0.13 mW at nanofluid compared with other concentrations due to the low density of nanofluid at this concentration. For highe
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... Show MoreBotnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet
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