Farhoodi A, Ahangari Gh, Chavoshzadeh Z, Ramyar A, Movahedi M, Ghareghozlou M, Heydarzadeh M, Fazlolahi M, Bemanian M H, Zandieh F, Mansori M,
Volume 65, Issue 7 (4 2007)
Abstract
Background: Mutations of ELA2, the gene encoding neutrophil elastase (NE) are known to be associated with cyclic neutropenia (CN) and severe congenital neutropenia (SCN). However, high variability of these mutations has been reported. This study was designed to describe the analysis of the ELA2 gene, clinical manifestations and demographic characteristics in patients with CN and SCN.
Methods: A series of 21 patients with CN or SCN were selected, based on SCINR criteria, from the immunology ward of the Pediatric Medicine Center, Tehran, Iran, from March 2004 to August 2005. The ELA2 gene, isolated from blood samples, was analyzed using RT-PCR and automated capillary sequencing. Informed consent was obtained under the tenets of the Helsinki Declaration and the Ethical Committee of the Tehran University of Medical Sciences.
Results: Kostmann's syndrome and CN was diagnosed in three and 18 patients respectively. Of all the patients, one or two mutations were found in 18 cases (85.7%), including all three patients with SCN and 15 of the patients with CN. Exons two and four had the most mutations (eight and seven cases, respectively). Seven patients had double mutations in two distinct exons. Overall, 16 different mutations were found. At the time of presentation, the mean age of patients was 13.4 ±17.6 months, ranging from one month to seven years. Overall, 61.9% of patients had consanguineous parents. The mean absolute neutrophil count was 830.5 ±419.4 (150-2000)/mm3. On average, each patient had been admitted to the hospital 2.2 ±1.6 times. The neutrophil counts of the SCN patients were significantly higher than those of the CN patients. However, there was no significant difference in the neutrophil counts between patients with mutations and those without mutations. All patients with SCN had two or more infectious complications, although the prevalence of infectious or non-infectious complications did not correlate with ELA2 mutations or the neutropenic disorders.
Conclusion: Mutations in ELA2 appear to play an important role in the phatogenetic mechanisms of CN and SCN. Patients with CN had significantly higher neutrophil counts than SCN patients with CN. Although it possible for the gene encoding neutrophil elastase to have more than one mutation in distinct exons, we found no association between the mutations in ELA2 and their complications in CN and SCN patients.
Bahman Mansori , Abdol Hamid Pilevar , Babak Azadnia ,
Volume 73, Issue 7 (October 2015)
Abstract
Background: Magnetic resonance imaging (MRI) is widely applied for examination and diagnosis of brain tumors based on its advantages of high resolution in detecting the soft tissues and especially of its harmless radiation damages to human bodies. The goal of the processing of images is automatic segmentation of brain edema and tumors, in different dimensions of the magnetic resonance images. Methods: The proposed method is based on the unsupervised method which discovers the tumor region, if there is any, by analyzing the similarities between two hemispheres and computes the image size of the goal function based on Bhattacharyya coefficient which is used in the next stage to detect the tumor region or some part of it. In this stage, for reducing the color variation, the gray brain image is segmented, then it is turned to gray again. The self-organizing map (SOM) neural network is used the segmented brain image is colored and finally the tumor is detected by matching the detected region and the colored image. This method is proposed to analyze MRI images for discovering brain tumors, and done in Bu Ali Sina University, Hamedan, Iran, in 2014. Results: The results for 30 randomly selected images from data bank of MRI center in Hamedan was compared with manually segmentation of experts. The results showed that, our proposed method had the accuracy of more than 94% at Jaccard similarity index (JSI), 97% at Dice similarity score (DSS), and 98% and 99% at two measures of specificity and sensitivity. Conclusion: The experimental results showed that it was satisfactory and can be used in automatic separation of tumor from normal brain tissues and therefore it can be used in practical applications. The results showed that the use of SOM neural network to classify useful magnetic resonance imaging of the brain and demonstrated a good performance.
