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Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by analyzing nutritional biomarkers, key leukemia indicators, and granulocytes in their blood. Using a machine learning algorithm and ten indices, the blood samples of 826 children with ALL and 255 children with AML were compared to a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an area under the curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.
Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary manifestations. Timely treatment has significantly enhanced survival rates for children with acute leukemia. This study aimed to develop an early and comprehensive predictor for hematologic malignancies in children by analyzing nutritional biomarkers, key leukemia indicators, and granulocytes in their blood. Using a machine learning algorithm and ten indices, the blood samples of 826 children with ALL and 255 children with AML were compared to a control group of 200 healthy children. The study revealed notable differences, including higher indicators in boys compared to girls and significant variations in most biochemical indicators between leukemia patients and healthy children. Employing a random forest model resulted in an area under the curve (AUC) of 0.950 for predicting leukemia subtypes and an AUC of 0.909 for forecasting AML. This research introduces an efficient diagnostic tool for early screening of childhood blood cancers and underscores the potential of artificial intelligence in modern healthcare.
Extracellular vesicles (EVs) are heterogeneous, phospholipid membrane enclosed particles that are secreted by healthy and cancerous cells. EVs are present in diverse biological fluids and have been associated with the severity of diseases, which indicates their potential as biomarkers for diagnosis, prognosis and as therapeutic targets. This study investigated the phenotypic characteristics of EVs derived from peripheral blood (PB) and bone marrow (BM) in pediatric patients with B-cell acute lymphoblastic leukemia (B-ALL) during different treatment stages. PB and BM plasma were collected from 20 B-ALL patients at three time points during induction therapy, referred to as: diagnosis baseline (D0), day 15 of induction therapy (D15) and the end of the induction therapy (D35). In addition, PB samples were collected from 10 healthy children at a single time point. The EVs were measured using CytoFLEX S flow cytometer. Calibration beads were employed to ensure accurate size analysis. The following, fluorescent-labeled specific cellular markers were used to label the EVs: Annexin V (phosphatidylserine), CD235a (erythrocyte), CD41a (platelet), CD51 (endothelial cell), CD45 (leukocyte), CD66b (neutrophil), CD14 (monocyte), CD3 (T lymphocyte), CD19, CD34 and CD10 (B lymphoblast/leukemic blast). Our results demonstrate that B-ALL patients had a marked production of EV-CD51/61+, EV-CD10+, EV-CD19+ and EV-CD10+CD19+ (double-positive) with a decrease in EV-CD41a+ on D0. However, the kinetics and signature of production during induction therapy revealed a clear decline in EV-CD10+ and EV-CD19+, with an increase of EV-CD41a+ on D35. Furthermore, B-ALL patients showed a complex biological network, exhibiting distinct profiles on D0 and D35. Interestingly, fold change and ROC curve analysis demonstrated that EV-CD10+CD19+ were associated with B-ALL patients, exhibited excellent clinical performance and standing out as a potential diagnostic biomarker. In conclusion, our data indicate that EVs represent a promising field of investigation in B-ALL, offering the possibility of identifying potential biomarkers and therapeutic targets.
Background: Despite the promising of introduction of tyrosine kinase inhibitors (TKIs), chronic myeloid leukemia (CML) remains a significant cause of annual mortality. Red blood cell distribution width (RDW), neutrophil/lymphocyte ratio (NLR), and platelet/lymphocyte ratio (PLR) are parameters derived from a complete blood count (CBC) commonly used to diagnose anemia, autoimmune diseases, and inflammation. These parameters have been reported to have a strong association with various diseases, including hematologic malignancies. Objectives: The study aims to identify whether RDW, NLR, and PLR can act as predictors of survival in newly diagnosed and treated CML patients. Materials and Methods: The study involved 60 Iraqi patients (37 males, 23 females, aged 17–69 years) with CML at chronic phase, who were referred to the National Center of Hematology/Mustansiriyah University, Baghdad, from February 2022 to December 2022. Twenty were newly diagnosed (T0), and 40 were under TKI treatment (T+), with 20 on imatinib and 20 on nilotinib. Additionally, a control group of 20 age- and gender-matched healthy subjects was included. CBC assessed red blood cell (RBC) indices across all groups. Results: There was no significant difference in the age of CML patients at the onset of disease between males (34.5 ± 11.7 years) and females (34 ± 11.9 years). Likewise, there was no significant difference in the treatment of CML patients with imatinib or nilotinib between males (48% and 52%) and females (53.3% and 47.7%), respectively. Most RBC indices for patients and controls were within normal ranges without significant differences. However, RDW% in T0 was markedly elevated (20.4%), with about 80% showing anisocytosis, surpassing both T+ and controls, and exceeding the upper limit of normal. The total and differential white blood cell (WBC) counts were significantly higher in T0 compared to T+, exceeding their normal ranges. Additionally, the NLR was significantly higher in T0 (8.13) compared with T+ and controls (1.80 and 1.87, respectively). Platelet count, mean platelet volume, and platelet distribution width (PDW%) differed significantly among the three groups but remained within the normal range. However, PLR in T0 (31 ± 24) was significantly lower than those in T+ and controls (130 ± 43 and 102 ± 27, respectively). Conclusion: It can be concluded that the monitoring of some parameters in peripheral blood in CBC test (as a simple and inexpensive test) such as RDW%, NLR%, and PLR% during the therapy course of CML patients may act as predictive markers to evaluate the prognosis of disease in CML patients and the degree of response to certain TKI treatment.
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