Showing 5 results for Bahrami
Dr Hossein Ayatollahi, Dr Mohammad Hadi Sadeghian, Mrs Sepideh Shakeri, Mrs Seyyede Fatemeh Shams, Mrs Neda Motamedi Rad, Mrs Maryam Sheikhi, Mr Mohammad Alidadi, Dr Afsane Bahrami,
Volume 7, Issue 2 (3-2017)
Abstract
Background: Acute myeloid leukemia (AML) is one of myeloid malignancies which the risk increases with age increment. It is categorized based on genetic aberrations. Some of these genetic disorders can determine minimal residual diseases (MRD) and prognosis of AML patients. Wilms tumor (WT1) over expression is found in AML patients. The aim of this study was to determine the frequency of WT1 over expression in AML pediatric cases in North -East of Iran.
Materials and Methods: This retrospective Study was done in Mashhad, Iran during 2016 in 100 pediatric AML cases. WT1 expression was evaluated by quantitative reverses transcription PCR (qRT-PCR) and cloning method. Both WT1 and ABL genes were cloned to create a standard curve and then copy number of WT1 gene in patients was evaluated.
Results: One hundred children under 15 with mean age of 6.50± 4.22 were evaluated in this study. There were no significant differences between age and sex and WT1 expression (P>0.05). Mean expression of cited gene was 200.52±210.62 copies of WT1/ABL 104 in studied samples. WT1 gene over expression was observed in 82% of all patients.
Conclusion: WT1 assessment can be applied as a prognostic and diagnostic marker in AML patients under 15.
Dr Azam Hashemi, Dr Elnaz Sheikhpour, Ms Fatemeh Ghanizadeh, Dr Mahshid Bahrami,
Volume 12, Issue 4 (10-2022)
Abstract
Background: Given that various types of cancer are major causes of death among children and there is no comprehensive study investigating the simultaneous effect of ifosfamide and mesna in the treatment of patients with various types of cancer in our country, this study aimed to assess the effect of ifosfamide and mesna in the treatment of children with various types of cancer.
Materials and Methods: In the retrospective study, 46 patients with cancer were divided into two groups. In the first group, patients were treated with ifosfamide (800 mg-1g/m2/day) with 500 cc of normal saline and mesna (equivalent to the amount of ifosfamide) in the serum. In the second group, the patient received ifosfamide through serum and mesna at 0, 4, 8, and 16 hours after the ifosfamide injection. This injection continued for three days. Then blood count, hemoglobin, and kidney tests in both groups were examined.
Results: White blood cells in both methods decreased significantly (P<0.01). In the second group, there was a significant difference before and after intervention, regarding hemoglobin level (P<0.01). In addition, more people in the second group showed gastrointestinal complications (P<0.01). There was no significant difference before and after intervention in the the two groups, regarding creatinine and urea levels (P>0.05).
Conclusion: In both groups, a decrease in white blood cells was observed, while kidney toxicity was not observed in any group. The decrease in hemoglobin in the second group was more than in the first group.
Dr Sanaz Mehrabani, Dr Morteza Zangeneh Soroush, Dr Negin Kheiri, Dr Razieh Sheikhpour, Dr Mahshid Bahrami,
Volume 13, Issue 1 (1-2023)
Abstract
Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method.
Materials and Methods: In this descriptive study, the microarray gene expression data of 72 patients with acute myeloid leukemia (AML) and lymphoblastic leukemia (ALL) was used. To remove the redundant genes and identify the most important genes in the prediction of AML and ALL, a robust ℓ2,p-norm (0 < p ≤1) sparsity-based gene selection method was applied, in which the parameter p method was implemented from 1/4, 1/2, 3/4 and 1. Then, the most important genes were used by the random forest (RF) and support vector machine (SVM) classifiers for prediction of AML and ALL.
Results: The RF and SVM classifiers correctly classified all AML and ALL samples. The RF classifier obtained the performance of 100% using 10 genes selected by the ℓ2,1/2-norm and ℓ2,1-norm sparsity-based gene selection methods. Moreover, the SVM classifier obtained a performance of 100% using 10 genes selected by the ℓ2,1/2-norm method. Seven common genes were identified by all four values of parameter p in the ℓ2,p-norm method as the most important genes in the classification of AML and ALL, and the gene with the description “PRTN3 Proteinase 3 (serine proteinase, neutrophil, Wegener granulomatosis autoantigen” was identified as the most important gene.
Conclusion: The results obtained in this study indicated that the prediction of blood cancer from leukemia microarray gene expression data can be carried out using the robust ℓ2,p-norm sparsity-based gene selection method and classification algorithms. It can be useful to examine the expression level of the genes identified by this study to predict leukemia.
Dr Mahshid Bahrami, Dr Elnaz Sheikhpour, Dr Mojtaba Karami, Dr Sanaz Mehrabani,
Volume 14, Issue 1 (1-2024)
Abstract
Cancer is as the second leading cause of death among children in the United States. The mortality rate for cancer has witnessed a decline, dropping from 6.5 per 100,000 in 1970 to 2.3 per 100,000 in 2016. Second malignant neoplasms (SMNs) represent novel primary malignancies emerging after the initial cancer diagnosis, particularly prominent as late effects of cancer therapy in children. The incidence of SMNs sees a substantial increase over time, reaching nearly 10% even a decade after the initial diagnosis. A comparative analysis between the general population and child cancer survivors reveals a six-fold higher risk of developing SMNs among the latter. Various factors contribute to the elevated risk of second cancers, with age, lifestyle, environmental influences, primary cancer treatment, and genetic predisposition playing pivotal roles. Noteworthy risk factors for SMNs in children encompass radiation therapy, chemotherapeutic agents, topoisomerase inhibitors, genetic factors, hematopoietic stem cell transplantation, and ionizing radiation, as elucidated in the present study. Despite these findings, further research is imperative to accurately quantify the risks associated with etiological factors, enabling the identification of individuals at a heightened risk for second cancers and facilitating proactive screening and preventive measures.
Dr Amirhossein Omidi, Dr Maryam Sadat Yazdanparast, Dr Seyedeh Elham Shams, Dr Reza Bahrami, Dr Mohammad Golshan-Tafti, Dr Seyed Alireza Dastgheib, Dr Maryam Yeganegi, Dr Mahsa Danaie, Dr Ali Masoudi, Dr Amirmasoud Shiri, Dr Maryam Aghasipour, Dr Kazem Aghili, Dr Mahmood Noorishadkam, Dr Hossein Neamatzadeh,
Volume 15, Issue 2 (3-2025)
Abstract
The Pediatric Buccal Epigenetic (PedBE) and Neonatal Epigenetic Estimator of Age (NEOage) clocks provide a novel method for assessing the biological age of young individuals, enhancing our comprehension of their health and development. By analyzing DNA methylation patterns, these clocks identify risk factors for various health conditions and guide personalized interventions to promote optimal growth in children and infants. With ongoing research and validation, PedBE and NEOage could revolutionize pediatric and neonatal healthcare by facilitating early detection of age-related changes and targeted interventions to improve long-term outcomes. In pediatric oncology, PedBE is particularly promising for evaluating biological age in children with cancer, as it accurately estimates DNA methylation age in buccal cells, revealing the effects of cancer and its treatments on biological aging. Additionally, PedBE can detect DNA methylation changes associated with environmental exposures and childhood adversities, making it a valuable tool for studying the impact of cancer on the epigenetic age of pediatric patients. The NEOage clock, designed to predict gestational age in newborns, complements the PedBE clock, offering a comprehensive assessment of biological age from infancy to adolescence, which is vital for understanding pediatric oncology’s influence on aging. This paper examines the complexities of both clocks, highlighting their potential for accurately determining the age of children and infants through DNA methylation analysis.
