IgD/FcδR is involved in T-cell acute lymphoblastic leukemia and regulated by IgD-Fc-Ig fusion protein

Wu, Yujing; Zhang, Aijun; Chen, Wensheng; Xin, Qianling; Pan, Wenwen; Hu, Xiaoxi; Li, Tao; Chen, Hengshi; Zhang, Jing; Luan, Chengxin; Ge, Junbo; Wei, Wei

doi:10.1016/j.phrs.2023.106686

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…It is the most common type of childhood cancer, accounting for about one-third of all childhood cancers. Wu, Y.,et al [38] has expressed an ALL is a serious and potentially life-threatening disease, but treatment has improved significantly in recent years and the overall prognosis is good. Risk factors for ALL include exposure to radiation, exposure to certain chemicals, genetic predisposition, and weakened immune systems.…”

Section: A Analysis Based On Diagnosismentioning

confidence: 99%

“…Sekar, G.,et al [35] An environmental factors, including exposure to ionizing radiation following or connected to diagnostic or therapeutic procedures Gupta, S.,et al [36] The genetic factors, such as having a family history of leukemia, other blood clots, or certain diseases, including Down's syndrome and Fanconi anemia Khalil, M.,et al [37] An acute lymphoblastic leukemia (ALL) is a type of cancer that begins in the bone marrow and white blood cells. It is the most common type of childhood cancer, accounting for about one-third of all childhood cancers Wu, Y.,et al [38] An ALL is a serious and potentially life-threatening disease, but treatment has improved significantly in recent years and the overall prognosis is good. Exposure to radiation, either through medical treatments or through environmental sources such as nuclear radiation, increases the risk of ALL Logeshwaran, J.,et al [39] A family history of ALL also increases the risk of developing the disease.…”

Section: Authorsmentioning

confidence: 99%

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Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

et al. 2023

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Leukemia is a cancer of blood-producing cells, including the bone marrow. Abnormal white blood cells travel through blood vessels and multiply rapidly. Healthy cells in the body become a minority, and the imbalance increases the chances of infection in the body. Leukemia or blood cancer is the most common cancer in children ages 2 -14. Most leukemia in children is treated. Acute lymphocytic leukemia (ALL) is a type of cancer in the blood and bone marrow. It progresses rapidly when immature white blood cells are formed instead of mature ones. Treatments for acute lymphocytic leukemia include drugs and blood transfusions directly into veins, chemotherapy, and all transplantation, which involve transferring organs or tissues within the body or from one person to another. In this paper, Pattern Recognition of Acute Lymphoblastic Leukemia has been proposed using Computational Deep Learning. Pattern recognition technology uses mathematical algorithms to identify patterns in large datasets of data. Analyzing the data, the algorithms can identify patterns indicative of certain states or conditions. In the case of ALL, the algorithm would look for patterns in white blood cell count data that indicate the presence of ALL. These patterns may include changes in the number of white blood cells over time, changes in the composition of the white blood cells, or changes in the levels of certain proteins or gene expressions associated with ALL. The proposed ALLDM model achieved 81.53% (DDS) and 87.92% (SDS) of chemotherapy management, 79.16% (DDS) and 94.31% (SDS) of Stem Cell Transplantation Management, 63.77% (DDS) and 87.37% (SDS) of Radiation therapy Management and 88.92% (DDS) and 85.86% (SDS) of Targeted therapy drugs management.

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Section: A Analysis Based On Diagnosismentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

et al. 2023

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CD7‐targeting pro‐apoptotic extracellular vesicles: A novel approach for T‐cell haematological malignancy therapy

Zhang,

Chen,

Chen

et al. 2024

J of Extracellular Vesicle

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T‐cell haematological malignancies progress rapidly and have a high mortality rate and effective treatments are still lacking. Here, we developed a drug delivery system utilizing 293T cell‐derived extracellular vesicles (EVs) modified with an anti‐CD7 single‐chain variable fragment (αCD7/EVs). Given the challenges of chemotherapy resistance in patients with T‐cell malignancy, we selected cytochrome C (CytC) and Bcl2 siRNA (siBcl2) as therapeutic agents and loaded them into αCD7/EVs (αCD7/EVs/CytC/siBcl2). We found that αCD7/EVs efficiently targeted and were internalized by human T‐ALL Molt‐4 cells. In addition, the interaction between αCD7 and CD7 switched the EV entry pathway in Molt‐4 cells from macropinocytosis‐dependent endocytosis to clathrin‐mediated endocytosis, thereby reducing EV‐lysosome colocalization, ultimately improving CytC delivery efficiency and increasing the cytotoxicity of nascent EVs from EV‐treated Molt‐4 cells. Notably, αCD7/EVs/CytC/siBcl2 demonstrated similar efficacy against both Molt‐4 and chemotherapy‐resistant Molt‐4 cells (CR‐Molt‐4). Furthermore, αCD7/EVs/CytC/siBcl2 exhibited high safety, low immunogenicity and minimal impact on human T cells. Therefore, αCD7/EVs/CytC/siBcl2 are promising therapeutic approaches for treating CD7+ T‐cell malignancies.

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IgD/FcδR is involved in T-cell acute lymphoblastic leukemia and regulated by IgD-Fc-Ig fusion protein

Cited by 2 publications

References 37 publications

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

Pattern Recognition of Acute Lymphoblastic Leukemia (ALL) Using Computational Deep Learning

CD7‐targeting pro‐apoptotic extracellular vesicles: A novel approach for T‐cell haematological malignancy therapy

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