Correlation between methylation level of the SLC19A1 promoter region and methotrexate metabolism in adult acute lymphoblastic leukemia.

Aims: To analyze the methylation level in the promoter region of SLC19A1 in adult acute lymphoblastic leukemia (ALL) patients, and explore the relationship between methotrexate (MTX) drug metabolism and SLC19A1 methylation. Methods: The methylation levels of the SLC19A1 promoter region in 52 adult ALL patients who received high-dose MTX chemotherapy were retrospectively analyzed in combination with clinical indicators and plasma MTX concentration. Results: Methylation levels of 17 CpG units were differently correlated with clinical parameters of ALL patients including gender, age, immunophenotype and Philadelphia chromosome status. Patients with delayed MTX drug excretion had higher methylation levels in the SLC19A1 promoter region. Conclusion: The methylation may affect the MTX plasma level and adverse reactions, which may predict patients at risk of adverse reactions after high-dose MTX therapy.

The Diagnosis of Chronic Myeloid Leukemia with Deep Adversarial Learning.

Chronic myeloid leukemia (CML) is a clonal proliferative disorder of granulocytic lineage, with morphologic evaluation as the first step for a definite diagnosis. This study developed a conditional generative adversarial network (cGAN)-based model, CMLcGAN, to segment megakaryocytes from myeloid cells in bone marrow biopsies. After segmentation, the statistical characteristics of two types of cells were extracted and compared between patients and controls. At the segmentation phase, the CMLcGAN was evaluated on 517 images (512 × 512) which achieved a mean pixel accuracy of 95.1%, a mean intersection over union of 71.2%, and a mean Dice coefficient of 81.8%. In addition, the CMLcGAN was compared with seven other available deep learning-based segmentation models and achieved a better segmentation performance. At the clinical validation phase, a series of seven-dimensional statistical features from various cells were extracted. Using the t-test, five-dimensional features were selected as the clinical prediction feature set. Finally, the model iterated 100 times using threefold cross-validation on whole slide images (58 CML cases and 31 healthy cases), and the final best AUC was 84.93%. In conclusion, a CMLcGAN model was established for multiclass segmentation of bone marrow cells that performed better than other deep learning-based segmentation models.

A novel dual-channel fluorescent probe for selectively and sensitively imaging endogenous nitric oxide in living cells and zebrafish.

Nitric oxide (NO) plays various physiological and pathological roles in lots of biological processes. It is crucial to detect NO sensitively and selectively in vivo and in vitro as homeostasis of NO is closely related to various diseases. Herein, a novel dual-channel fluorescent dye (ENNH2) based on dicarboxyimide anthracene was developed as a highly sensitive and selective probe to detect NO in living systems using the dual-channel fluorescence. ENNH2 can emit bright red fluorescence due to the intramolecular charge transfer (ICT) from the amino group at the 6-position of 1,2-dicarboxyimide anthracene to the conjugated aromatic ring, and the ICT is effectively inhibited by the reductive deamination of the amino in the presence of NO to obtain the remarkable strong green emission with the excellent sensitivity (5.52 nM). Promisingly, ENNH2 exhibits an excellent performance in endogenous NO dual-channel fluorescence imaging of RAW 264.7 cells and zebrafish.