Objectives: To determine if the sera of patients with acute promyelocytic leukemia (APL) and all-trans retinoic acid (ATRA) inhibit the formation of colony-forming-unit of megakaryocyte (CFU-Meg).
Methods: By cellular culture assay, the sera of 22 APL patients treated with ATRA and 10(-6)' mol/L ATRA were kinetically used to perform the investigation. Meanwhile, serum tumor necrosis factor (TNF) activity and bone marrow CFU-L were also detected.
Results: Before treatment, there was an inhibitory activity on CFU-Meg in the serum of APL patients (29.6% +/- 2.4%). The activity reduced gradually with the treatment and was stimulus when bone marrow remission obtained (-18.7% +/- 4.1%, P < 0.001). It was positively correlated with TNF activity in the serum and the number of CFU-L however negatively correlated with the number of megakaryocyte in bone marrow and PLT. Serum TNF activity and the number of CFU-L before treatment were higher than those after treatment (P < 0.05 and < 0.001, respectively). ATRA could stimulate the growth of CFU-Meg.
Conclusions: There was an inhibitory activity in the serum of APL patients which could be used to monitor the efficacy of the therapy. ATRA stimulates the growth of CFU-Meg.
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The prognosis for patients with acute promyelocytic leukemia (APL) has improved dramatically since the introduction of all-trans retinoic acid (ATRA) and intravenous arsenic trioxide (ATO). However, ATO administration requires daily infusions over several months, representing an onerous burden for hospitals and patients. We evaluated the bioavailability of a novel encapsulated oral ATO formulation in APL patients in first complete remission during standard-of-care consolidation.
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Department of Software Technology, Konkuk University, Chungju 27478, Republic of Korea.
The timely and accurate detection of brain tumors is crucial for effective medical intervention, especially in resource-constrained settings. This study proposes a lightweight and efficient RetinaNet variant tailored for medical edge device deployment. The model reduces computational overhead while maintaining high detection accuracy by replacing the computationally intensive ResNet backbone with MobileNet and leveraging depthwise separable convolutions.
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