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The tumor suppressor Lkb1 is known to regulate the expression of forkhead box P3 (Foxp3), thereby maintaining the levels of Foxp3+ regulatory T cells (Treg) that play a crucial role in self‐tolerance. However, the effect of Lkb1 in Treg on hematopoietic stem cells (HSCs) in the bone marrow (BM) remains obscure. Here, we demonstrated that conditional deletion of Lkb1 in Treg causes loss of Treg in the BM, which leads to failure of HSC homeostasis and the abnormal expansion. Moreover, the loss of BM Treg results in dysregulation of other developing progenitors/stem cell populations, leading to the defective differentiation of T cells and B cells. In addition, HSC from the BM with Treg loss exhibited poor engraftment efficiency, indicating that loss of Treg leads to irreversible impairment of HSC. Collectively, these results demonstrated the essential role of Lkb1 in Treg for maintaining HSC homeostasis and differentiation in mice. These findings provide insight into the mechanisms of HSC regulation and guidance for a strategy to improve the outcomes and reduce complications of HSC transplantation. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID‐19) spreads rapidly across the globe, seriously threatening the health of people all over the world. To reduce the diagnostic pressure of front‐line doctors, an accurate and automatic lesion segmentation method is highly desirable in clinic practice. Purpose: Many proposed two‐dimensional (2D) methods for sliced‐based lesion segmentation cannot take full advantage of spatial information in the three‐dimensional (3D) volume data, resulting in limited segmentation performance. Three‐dimensional methods can utilize the spatial information but suffer from long training time and slow convergence speed. To solve these problems, we propose an end‐to‐end hybrid‐feature cross fusion network (HFCF‐Net) to fuse the 2D and 3D features at three scales for the accurate segmentation of COVID‐19 lesions. Methods: The proposed HFCF‐Net incorporates 2D and 3D subnets to extract features within and between slices effectively. Then the cross fusion module is designed to bridge 2D and 3D decoders at the same scale to fuse both types of features. The module consists of three cross fusion blocks, each of which contains a prior fusion path and a context fusion path to jointly learn better lesion representations. The former aims to explicitly provide the 3D subnet with lesion‐related prior knowledge, and the latter utilizes the 3D context information as the attention guidance of the 2D subnet, which promotes the precise segmentation of the lesion regions. Furthermore, we explore an imbalance‐robust adaptive learning loss function that includes image‐level loss and pixel‐level loss to tackle the problems caused by the apparent imbalance between the proportions of the lesion and non‐lesion voxels, providing a learning strategy to dynamically adjust the learning focus between 2D and 3D branches during the training process for effective supervision. Result: Extensive experiments conducted on a publicly available dataset demonstrate that the proposed segmentation network significantly outperforms some state‐of‐the‐art methods for the COVID‐19 lesion segmentation, yielding a Dice similarity coefficient of 74.85%. The visual comparison of segmentation performance also proves the superiority of the proposed network in segmenting different‐sized lesions. Conclusions: In this paper, we propose a novel HFCF‐Net for rapid and accurate COVID‐19 lesion segmentation from chest computed tomography volume data. It innovatively fuses hybrid features in a cross manner for lesion segmentation, aiming to utilize the advantages of 2D and 3D subnets to complement each other for enhancing the segmentation performance. Benefitting from the cross fusion mechanism, the proposed HFCF‐Net can segment the lesions more accurately with the knowledge acquired from both subnets. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is the most common type of leukemia in adults. AML cells secrete angiogenic factors to remodel vasculature and acquire chemoresistance; however, antiangiogenic drugs are often ineffective in AML treatment. Cancer cell‐derived exosomes can induce angiogenesis, but their role in vascular remodeling during AML is unclear. Here, we found that exosomes secreted by AML cells promoted proliferation and migration and tube‐forming activity of human umbilical vein endothelial cells (HUVECs), whereas HUVECs conferred chemoresistance to AML cells. AML cell‐derived exosomes contained vascular endothelial growth factor (VEGF) and VEGF receptor (VEGFR) messenger RNA and induced VEGFR expression in HUVECs. Furthermore, they enhanced glycolysis, which correlated with HUVEC proliferation, tube formation, and resistance to apoptosis. Thus, AML cells secrete VEGF/VEGFR‐containing exosomes that induce glycolysis in HUVECs leading to vascular remodeling and acquisition of chemoresistance. These findings may contribute to the development of novel therapeutic strategies targeting exosomes in AML. [ABSTRACT FROM AUTHOR]

Functional connectivity network provides novel insights on how distributed brain regions are functionally integrated, and its deviations from healthy brain have recently been employed to identify biomarkers for neuropsychiatric disorders. However, most of brain network analysis methods utilized features extracted only from one functional connectivity network for brain disease detection and cannot provide a comprehensive representation on the subtle disruptions of brain functional organization induced by neuropsychiatric disorders. Inspired by the principles of multi‐view learning which utilizes information from multiple views to enhance object representation, we propose a novel multiple network based framework to enhance the representation of functional connectivity networks by fusing the common and complementary information conveyed in multiple networks. Specifically, four functional connectivity networks corresponding to the four adjacent values of regularization parameter are generated via a sparse regression model with group constraint (l2,1‐norm), to enhance the common intrinsic topological structure and limit the error rate caused by different views. To obtain a set of more meaningful and discriminative features, we propose using a modified version of weighted clustering coefficients to quantify the subtle differences of each group‐sparse network at local level. We then linearly fuse the selected features from each individual network via a multi‐kernel support vector machine for autism spectrum disorder (ASD) diagnosis. The proposed framework achieves an accuracy of 79.35%, outperforming all the compared single network methods for at least 7% improvement. Moreover, compared with other multiple network methods, our method also achieves the best performance, that is, with at least 11% improvement in accuracy. [ABSTRACT FROM AUTHOR]