MENNA AL-ADL^1,*, MAGDY M. YOUSSEF¹, AHMED EL-SEBAIE², SHERIF REFAAT³, AFAF EL-SAID⁴

Oncology Research, Vol.32, No.4, pp. 785-797, 2024, DOI:10.32604/or.2024.047021

Abstract Cytochromes P450 (CYPs) play a prominent role in catalyzing phase I xenobiotic biotransformation and account for about 75% of the total metabolism of commercially available drugs, including chemotherapeutics. The gene expression and enzyme activity of CYPs are variable between individuals, which subsequently leads to different patterns of susceptibility to carcinogenesis by genotoxic xenobiotics, as well as differences in the efficacy and toxicity of clinically used drugs. This research aimed to examine the presence of the CYP2B6*9 polymorphism and its possible association with the incidence of B-CLL in Egyptian patients, as well as the clinical outcome after receiving cyclophosphamide chemotherapy. DNA… More > Graphic Abstract

MONA S. ABDELLATEIF^1,*, NAGLAA M. HASSAN², MAHMOUD M. KAMEL², YOMNA M. EL-MELIGUI²

Oncology Research, Vol.32, No.3, pp. 577-584, 2024, DOI:10.32604/or.2023.043026

Abstract Background: microRNA-34a (miR-34a) had been reported to have a diagnostic role in acute myeloid leukemia (AML). However, its value in the bone marrow (BM) of AML patients, in addition to its role in response to therapy is still unclear. The current study was designed to assess the diagnostic, prognostic, and predictive significance of miR-34a in the BM of AML patients. Methods: The miR-34a was assessed in BM aspirate of 82 AML patients in relation to 12 normal control subjects using qRT-PCR. The data were assessed for correlation with the relevant clinical criteria, response to therapy, disease-free survival (DFS), and overall… More >

Nilkanth Mukund Deshpande^1,2, Shilpa Gite^3,4,*, Biswajeet Pradhan^5,6, Abdullah Alamri⁷, Chang-Wook Lee^8,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 593-631, 2024, DOI:10.32604/cmes.2023.030704

Abstract Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemia-infected cells. In addition, various subtypes of leukemia require different treatments. These sub-classes must also be detected to obtain an accurate diagnosis of the type of leukemia. This entails using multi-class classification to determine the leukemia subtype. This is usually done using a microscopic examination of these blood cells. Due to the requirement… More > Graphic Abstract

Abdel Nasser H. Zaied¹, Ehab Rushdy², Mona Gamal^3,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1349-1364, 2023, DOI:10.32604/csse.2023.033596

Abstract Acute leukemia is an aggressive disease that has high mortality rates worldwide. The error rate can be as high as 40% when classifying acute leukemia into its subtypes. So, there is an urgent need to support hematologists during the classification process. More than two decades ago, researchers used microarray gene expression data to classify cancer and adopted acute leukemia as a test case. The high classification accuracy they achieved confirmed that it is possible to classify cancer subtypes using microarray gene expression data. Ensemble machine learning is an effective method that combines individual classifiers to classify new samples. Ensemble classifiers… More >

Chaudhary Hassan Abbas Gondal^1,*, Muhammad Irfan², Sarmad Shafique³, Muhammad Salman Bashir⁴, Mansoor Ahmed¹, Osama M.Alshehri⁵, Hassan H. Almasoudi⁵, Samar M. Alqhtani⁶, Mohammed M. Jalal⁷, Malik A. Altayar⁷, Khalaf F. Alsharif⁸

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3541-3558, 2023, DOI:10.32604/csse.2023.036476

Abstract Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human’s immune system and significantly affect bone marrow’s production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient’s blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are… More >

Neenavath Veeraiah^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2039-2058, 2023, DOI:10.32604/csse.2023.036985

Abstract Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person’s tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization… More >

Neenavath Veeraiah^1,*, Youseef Alotaibi², Ahmad F. Subahi³

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1879-1900, 2023, DOI:10.32604/csse.2023.034658

Abstract An abnormality that develops in white blood cells is called leukemia. The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery. Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis. This paper proposes a Histogram Threshold Segmentation Classifier (HTsC) for a decision support system. The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images. Arithmetic operations are used to crop the nucleus based on automated approximation. White Blood Cell (WBC) segmentation is calculated using the active contour model… More >

SHAHRAM NEKOEIAN¹, TAHEREH ROSTAMI², AMIR NOROUZY³, SAFIN HUSSEIN^1,4, GHOLAMREZA TAVOOSIDANA¹, BAHRAM CHAHARDOULI², SHAHRBANO ROSTAMI^2,*, YAZDAN ASGARI⁵, ZAHRA AZIZI^1,*

Oncology Research, Vol.30, No.6, pp. 259-268, 2022, DOI:10.32604/or.2022.027904

Abstract Acute lymphoblastic leukemia (ALL) is a malignancy of bone marrow lymphoid precursors. Despite effective treatments, the causes of its progression or recurrence are still unknown. Finding prognostic biomarkers is needed for early diagnosis and more effective treatment. This study was performed to identify long non-coding RNAs (lncRNAs) involved in ALL progression by constructing a competitive endogenous RNA (ceRNA) network. These lncRNAs may serve as potential new biomarkers in the development of ALL. The GSE67684 dataset identified changes in lncRNAs and mRNAs involved in ALL progression. Data from this study were re-analyzed, and probes related to lncRNAs were retrieved. Targetscan, miRTarBase,… More >

ZHANSHU LIU¹, XI HUANG^2,*

BIOCELL, Vol.47, No.3, pp. 593-605, 2023, DOI:10.32604/biocell.2023.024148

Abstract Purpose: Iron metabolism maintains the balance between iron absorption and excretion. Abnormal iron metabolism can cause numerous diseases, including tumor. This study determined the iron metabolism-related genes (IMRGs) signature that can predict the prognosis of acute myeloid leukemia (AML). The roles of these genes in the immune microenvironment were also explored. Methods: A total of 514 IMRGs were downloaded from the Molecular Characteristics Database (MSigDB). IMRGs related to AML prognosis were identified using Cox regression and LASSO analyses and were used to construct the risk score model. AML patients were stratified into high-risk groups (cluster 1) and low-risk groups (cluster… More >

K. Venkatesh^1,*, S. Pasupathy¹, S. P. Raja²

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 543-560, 2023, DOI:10.32604/iasc.2023.030701

Abstract The evolution of bone marrow morphology is necessary in Acute Myeloid Leukemia (AML) prediction. It takes an enormous number of times to analyze with the standardization and inter-observer variability. Here, we proposed a novel AML detection model using a Deep Convolutional Neural Network (D-CNN). The proposed Faster R-CNN (Faster Region-Based CNN) models are trained with Morphological Dataset. The proposed Faster R-CNN model is trained using the augmented dataset. For overcoming the Imbalanced Data problem, data augmentation techniques are imposed. The Faster R-CNN performance was compared with existing transfer learning techniques. The results show that the Faster R-CNN performance was significant… More >