Adrenal Cushing's syndrome is caused by excess production of glucocorticoid from adrenocortical tumors and hyperplasias, which leads to metabolic disorders. We performed whole-exome sequencing of 49 blood-tumor pairs and RNA sequencing of 44 tumors from cortisol-producing adrenocortical adenomas (ACAs), adrenocorticotropic hormone-independent macronodular adrenocortical hyperplasias (AIMAHs), and adrenocortical oncocytomas (ADOs). We identified a hotspot in the PRKACA gene with a L205R mutation in 69.2% (27 out of 39) of ACAs and validated in 65.5% of a total of 87 ACAs. Our data revealed that the activating L205R mutation, which locates in the P+1 loop of the protein kinase A (PKA) catalytic subunit, promoted PKA substrate phosphorylation and target gene expression. Moreover, we discovered the recurrently mutated gene DOT1L in AIMAHs and CLASP2 in ADOs. Collectively, these data highlight potentially functional mutated genes in adrenal Cushing's syndrome.

Although neurons attract the most attention in neurobiology, our current knowledge of neural circuit can only partially explain the neurological and psychiatric conditions of the brain. Thus, it is also important to consider the influence of brain interstitial system (ISS), which refers to the space among neural cells and capillaries. The ISS is the major compartment of the brain microenvironment that provides the immediate accommodation space for neural cells, and it occupies 15% to 20% of the total brain volume. The brain ISS is a dynamic and complex space connecting the vascular system and neural networks and it plays crucial roles in substance transport and signal transmission among neurons. Investigation of the brain ISS can provide new perspectives for understanding brain architecture and function and for exploring new strategies to treat brain disorders. This review discussed the anatomy of the brain ISS under both physiological and pathological conditions, biophysical modeling of the brain ISS and in vivo measurement and imaging techniques, including recent findings on brain ISS divisions. Moreover, the implications of ISS knowledge for basic neuroscience and clinical applications are addressed.

Our recent studies demonstrate that the focal adhesion protein Kindlin-2 is critical for chondrogenesis and early skeletal development. Here, we show that deleting Kindlin-2 from osteoblasts using the 2.3-kb mouse Col1a1-Cre transgene minimally impacts bone mass in mice, but deleting Kindlin-2 using the 10-kb mouse Dmp1-Cre transgene, which targets osteocytes and mature osteoblasts, results in striking osteopenia in mice. Kindlin-2 loss reduces the osteoblastic population but increases the osteoclastic and adipocytic populations in the bone microenvironment. Kindlin-2 loss upregulates sclerostin in osteocytes, downregulates β-catenin in osteoblasts, and inhibits osteoblast formation and differentiation in vitro and in vivo. Upregulation of β-catenin in the mutant cells reverses the osteopenia induced by Kindlin-2 deficiency. Kindlin-2 loss additionally increases the expression of RANKL in osteocytes and increases osteoclast formation and bone resorption. Kindlin-2 deletion in osteocytes promotes osteoclast formation in osteocyte/bone marrow monocyte cocultures, which is significantly blocked by an anti-RANKLneutralizing antibody. Finally, Kindlin-2 loss increases osteocyte apoptosis and impairs osteocyte spreading and dendrite formation. Thus, we demonstrate an important role of Kindlin-2 in the regulation of bone homeostasis and provide a potential target for the treatment of metabolic bone diseases.Bone Research (2020) 8:2; https://doi.

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The problem of counting crowds in varying density scenes or in different density regions of the same scene, named as pan-density crowd counting, is highly challenging. Previous methods are designed for single density scenes or do not fully utilize pan-density information. We propose a novel framework, the Pan-Density Network (PaDNet), for pan-density crowd counting. In order to effectively capture pan-density information, PaDNet has a novel module, the Density-Aware Network (DAN), that contains multiple sub-networks pretrained on scenarios with different densities. Further, a module named the Feature Enhancement Layer (FEL) is proposed to aggregate the feature maps learned by DAN. It learns an enhancement rate or a weight for each feature map to boost these feature maps. Further, we propose two refined metrics, Patch MAE (PMAE) and Patch RMSE (PRMSE), for better evaluating the model performance on pan-density scenarios. Extensive experiments on four crowd counting benchmark datasets indicate that PaDNet achieves stateof-the-art recognition performance and high robustness in pandensity crowd counting.

BackgroundLong non-coding RNAs (lncRNAs) show great potential as diagnostic tools in many diseases. We aimed to develop sensitive and noninvasive biomarkers in saliva for detecting early hepatocellular carcinoma (HCC).MethodsCandidate lncRNA biomarkers identified by Agilent microarray were subjected to validation using qPCR for the quantification of their expression levels in independent tissue, plasma and saliva sample sets, including healthy controls, HBsAg carriers, patients with chronic Hepatitis B, liver cirrhosis, early HCC, and advanced HCC. Levels of candidate biomarkers were also measured in totally 108 saliva samples from patients with any one of other nine leading causes of cancer death in men and women.FindingsLnc-PCDH9-13:1 was significantly elevated in HCC tissues, plasma and saliva of HCC patients compared with healthy controls and groups of several benign liver diseases and other leading cancers. Its level was significantly reduced after curative hepatectomy but significantly elevated again if HCC recurrence occurred. Salivary lnc-PCDH9-13:1 showed reasonable specificities and sensitivities for detecting HCC compared with several control groups. Furthermore, the overexpression of lnc-PCDH9-13:1 promotes cell proliferation and migration in vitro.InterpretationSalivary lnc-PCDH9-13:1 is a desirable biomarker for early HCC. It may help warrant prospective validation with larger sample sizes in multi-centers.

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