血液科学(英文)最新文献

Methotrexate (MTX) has an antitumor effect when used for the treatment of acute lymphoblastic leukemia (ALL). This study aims at evaluating the associations between 14 polymorphisms of six genes involved in MTX metabolism with serum MTX concentration and toxicity accompanying high-dose MTX. Polymorphisms in 183 Chinese patients with ALL were analyzed using TaqMan single nucleotide polymorphism genotyping assay. The serum MTX concentration was determined using homogeneous enzyme immunoassay. MTX-related toxicities were also evaluated. Renal toxicity was significantly associated with higher serum MTX concentrations at 24, 48, and 72 hours, and MTX elimination delay (P = 0.001, P < 0.001, P < 0.001, and P < 0.001, respectively), whereas SLCO1B1 rs4149056 was associated with serum MTX concentrations at 48 and 72 hours, and MTX elimination delay in candidate polymorphisms (P = 0.014, P = 0.019, and P = 0.007, respectively). SLC19A1 rs2838958 and rs3788200 were associated with serum MTX concentrations at 24 hours (P = 0.016, P = 0.043, respectively). MTRR rs1801394 was associated with serum MTX concentrations at 72 hours (P = 0.045). Neutropenia was related to SLC19A1 rs4149056 (odds ratio [OR]: 3.172, 95% confidence interval [CI]: 1.310-7.681, P = 0.011). Hepatotoxicity was associated with ABCC2 rs2273697 (OR: 3.494, 95% CI: 1.236-9.873, P = 0.018) and MTRR rs1801394 (OR: 0.231, 95% CI: 0.084-0.632, P = 0.004). Polymorphisms of SLCO1B1, SLC19A1, ABCC2, and MTRR genes help predict higher risk of increased MTX levels or MTX-related toxicities in adult ALL patients.

Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.

Several cases such as myeloproliferative neoplasms (MPN) with the coexistence of JAK2 and BCR-ABL have been reported. However, cases of transformation of essential thrombocythemia (ET) into chronic myeloid leukemia (CML) during the disease progression were rarely reported. Here, we report the case of a patient with JAK2 V617F- positive ET who subsequently acquired BCR-ABL1, which transformed the disease into CML after 10 years from the initial diagnosis. In this study, we dynamically monitored JAK2 V617F and BCR-ABL and observed multiple gene mutations, including IDH2, IDH1, ASXL1, KRAS, and RUNX1. It is important to be aware of this potentially clone evolution in disease progression.

Plasmablastic lymphoma (PBL) is an aggressive lymphoma with limited treatment strategies. Tuberculosis (TB) infection poses a high risk for patients with hematologic malignancies, especially those treated with immune agents but were never reported post-daratumumab treatment. Herein, we reported a TB infection in a 57-year-old male diagnosed with HIV-negative PBL receiving daratumumab-based treatment, who showed atypical lung infection and yielded Mycobacterium tuberculosis and cytomegalovirus (CMV) in the bronchoalveolar lavage fluid. Anti-TB therapy was administered, and the following daratumumab treatment was complete with good tolerance. In this case, we demonstrated that TB infection might occur after daratumumab therapy, and adequate attention should be paid to atypical symptoms.

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist's horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.