Background: The immunogenetic predisposition
may be considered as an important factor for the
development of Type 1 Diabetes Mellitus (T1DM)
in association with the HLA antigens.
Objective:This study was designed to investigate
the role of HLA-class II antigens in the etiology of
type T1DM and in prediction of this disease in
siblings, and its effect on expression of glutamic
acid decarboxylase autoantibodies (GADA).
methods:Sixty children who were newly diagnosed
type 1 diabetes (diagnosed less than five months)
were selected. Their age ranged from 3-17 years.
Another 50 healthy siblings were available for this
study, their ages range from 3-16 years. Eighty
apparently healthy control subjects, matched with
age (4-17) years, sex and ethnic backgrounds
(Iraqi Arabs) underwent the HLA-typing
examination. Finally 50 healthy individuals were
selected randomly to undergo GADA test.
Results:At HLA-class II region, DR3 and DR4
were significantly increased in patients (53.33
vs.26.25% and 50.0 vs. 12.5% respectively) as
compared to controls. In
addition to that, T1DM was significantly associated
with DQ2 (33.33 vs.15%) and DQ3 (40.0 vs.20%)
antigens as compared to controls, suggesting that
these antigens had a role in disease susceptibility,
while the frequency of DR2 and DQ1 antigens were
significantly lowered in patients compared to
controls (6.66 vs.25% and 6.66 vs.22.5%
respectively). These molecules might have
protective effect. In siblings a significant increase
frequency of DR4 antigen (34.0 vs.12.5%) was
observed in comparison to controls, suggesting that
it might be much useful for predicting T1DM in
affected families.Anti-GAD autoantibodies were
present in 50% of Type 1Diabetic children, and in
16% of their siblings. High proportion of GADA
was found in the patients carrying HLA-DR3/DR4
heterozygous.
conclusion:Both the T1DM patients and their
siblings shared the HLA- DQ1 as protective
antigens, while DR3 and DR4 were susceptible one,
and high proportion of GADA was found in the
T1DM patients and siblings carrying HLADR3/DR4 heterozygous
In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne
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