Background: Indeterminate colitis (IC), a term
originated by pathologists to characterize confounding
histopathlogic appearance of resected mucosa, has
become catch phrase for cases in which diagnostic
criteria at all levels elude classification as Crohn's
disease (CD) or ulcerative colitis (UC).
OBJECTIVES: evaluate the prevalence of pANCA
expression in the sera and its isotypes.
Patients and methods: PATIENTS GROUP
consisted of 60 patients (40 males and 20 females)
with indeterminate colitis and their age range was (19-
84 years). CONTROL GROUP consisted of 30 (15
males and 15 females) healthy volunteers and their
age range was (20- 66 years).
Antineutrophil cytoplasmic ( pANCA and cANCA)
testing was performed by an IIF technique on ethanol
fixed human EOH granulocytes as substrate
(EUROIMMUNE- Germany). Sigmoidoscope and
colonoscope examination were done for the patients
group and biopsies were taken from the patients for
histopathological examination.
Results:
Serological results of ANCA showed a significant
increased frequency of pANCA (63.3%) in
indeterminate colitis patients as compared to controls
(p=0.000). The highest percentage of this pANCA
titer was 1:10 (p=0.000) then 1:100 (p=0.008) and
most of them was IgG (53.3%) (p=0.000). Sensitivity
of pANCA was 60%, specificity of pANCA was 40%,
positive predictive value of pANCA was 61.1% and
negative predictive value of pANCA was 66.6%.
cANCA did not demonstrated in both groups.
Conclusions : pANCA was more prevalent in
indeterminate colitis and could be used as a predictive
serological marker for the outcome of disease.
The increasing availability of computing power in the past two decades has been use to develop new techniques for optimizing solution of estimation problem. Today's computational capacity and the widespread availability of computers have enabled development of new generation of intelligent computing techniques, such as our interest algorithm, this paper presents one of new class of stochastic search algorithm (known as Canonical Genetic' Algorithm ‘CGA’) for optimizing the maximum likelihood function strategy is composed of three main steps: recombination, mutation, and selection. The experimental design is based on simulating the CGA with different values of are compared with those of moment method. Based on MSE value obtained from bot
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Transmission lines are generally subjected to faults, so it is advantageous to determine these faults as quickly as possible. This study uses an Artificial Neural Network technique to locate a fault as soon as it happens on the Doukan-Erbil of 132kv double Transmission lines network. CYME 7.1-Programming/Simulink utilized simulation to model the suggested network. A multilayer perceptron feed-forward artificial neural network with a back propagation learning algorithm is used for the intelligence locator's training, testing, assessment, and validation. Voltages and currents were applied as inputs during the neural network's training. The pre-fault and post-fault values determined the scaled values. The neural network's p
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