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Cluster of differentiation 36 (CD36) is a multiligand receptor with important roles in lipid metabolism, angiogenesis and innate immunity, and its diverse effects may depend on the binding of specific ligands in different contexts. CD36 is expressed not only on immune cells in the tumor microenvironment (TME) but also on some hematopoietic cells. CD36 is associated with the growth, metastasis and drug resistance in some hematologic tumors, such as leukemia, lymphoma and myelodysplastic syndrome. Currently, some targeted therapeutic agents against CD36 have been developed, such as anti-CD36 antibodies, CD36 antagonists (small molecules) and CD36 expression inhibitors. This paper not only innovatively addresses the role of CD36 in some hematopoietic cells, such as erythrocytes, hematopoietic stem cells and platelets, but also pays special attention to the role of CD36 in the development of hematologic tumors, and suggests that CD36 may be a potential cancer therapeutic target in hematologic tumors. [ABSTRACT FROM AUTHOR]

Diffuse large B-cell lymphoma (DLBCL), the most common non-Hodgkin's lymphoma (NHL), is substantially heterogeneous. Approximately 5–10% of DLBCLs express CD5, which makes CD5+ DLBCL a rare subgroup. Different studies have shown that CD5+ DLBCL patients are often older and female and have higher lactate dehydrogenase levels, an Eastern Cooperative Oncology Group (ECOG) performance status > 1, and higher International Prognostic Index (IPI) scores. Moreover, patients often have advanced stage disease with a high incidence of central nervous system (CNS) relapse and bone marrow involvement. CD5+ DLBCL cells are more likely to express MYC, BCL-2, and MUM-1, less likely to express CD10, and most belong to the activated B-cell-like (ABC) subtype. The potential mechanisms underlying the poor prognosis of CD5+ DLBCL patients may be related to CD5-mediated B-cell receptor (BCR)-dependent and -independent pathways. The efficacy of the traditional rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) regimen is unsatisfactory in CD5+ DLBCL patients. Despite supporting evidence from retrospective studies, it is currently unclear whether dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin plus rituximab (DA-EPOCH-R) can improve outcomes in this population. Several new drugs, such as Bruton tyrosine kinase inhibitors (BTKi), BCL-2 inhibitors, and CXCR4 antagonists, as well as immunotherapy, may help to improve the prognosis of CD5+ DLBCL patients, but additional clinical explorations are needed to determine the optimal therapeutic strategy for this disease. [ABSTRACT FROM AUTHOR]

Due to the constraints of the exploration environment, acquired seismic data are commonly irregular or incomplete, which seriously affects the quality of subsequent seismic data processing and interpretation. Convolutional neural network (CNN)- based seismic data interpolation methods have been receiving increased attention owing to better efficiency and interpolation performance. UNet is a commonly used encoder-decoder network that uses skip connection, which can extract seismic data features using convolution filters. However, convolution filters are local operations that process a local neighbourhood and extract local features, but experience difficulty capturing global representations. Although an increase in network depth can expand the receptive field, the local operator still limits the precision of the recovered result. To address this limitation, we propose an improved UNet with a non-local block (NL-UNet), wherein non-local blocks are added to the classic UNet network. Owing to the addition of non-local blocks, NL-UNet can capture both local and non-local features of seismic data, which is conducive to improving its reconstruction performance. The applicability and effectiveness of NL-UNet were demonstrated through experiments on both synthetic and field datasets. [ABSTRACT FROM AUTHOR]

Objective: Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions. Background: In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork. Method: This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer. Results: When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human–robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human–robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust. Conclusion: Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust. Application: The study can help to better understand the interaction mechanism of the human–robot team and can better serve the management and cooperation of the human–robot team by appropriately adjusting the robots' fairness and social status. [ABSTRACT FROM AUTHOR]