Background Hepatocellular carcinoma (HCC) is the third leading cause of cancer mortality worldwide. Currently, there is limited knowledge of dysregulation of cellular proliferation and apoptosis that contribute to the malignant phenotype in HCC. Copper metabolism gene MURR1 domain 10 (COMMD10) is initially identified as a suppressor gene in the pathogenesis of HCC in our observations. Here we aimed to explore its function and prognostic value in the progression of HCC. Methods Functional experiments were performed to explore the role of COMMD10 in HCC. The molecular mechanisms of COMMD10 were determined by luciferase assay, immunofluorescence, and immunoprecipitation. The nomogram was based on a retrospective and multicenter study of 516 patients who were pathologically diagnosed with HCC from three Chinese hospitals. The predictive accuracy and discriminative ability of the nomogram were determined by a C‐index and calibration curve and were compared with COMMD10 and the Barcelona Clinic Liver Cancer (BCLC) staging system. The primary endpoint was overall survival (OS). Results COMMD10 expression was significantly lower in HCC than that in normal liver tissues. In vitro and in vivo experiments revealed that COMMD10 suppressed cell proliferation and induced apoptosis in HCC. Mechanistically, COMMD10 inhibits TNFα mediated ubiquitination of IκBα and p65 nuclear translocation through the combination of COMMD10‐N terminal to the Rel homology domain of p65, which inhibited NF‐κB activity and increased expression of cleaved caspase9/3 in HCC. Clinically, COMMD10 stratifies early‐stage HCC patients into two risk groups with significantly different OS. Additionally, the nomogram based on COMMD10 and BCLC stage yielded more accuracy than BCLC stage alone for predicting OS of HCC patients in three cohorts. Conclusions COMMD10 suppresses proliferation and promotes apoptosis by inhibiting NF‐κB signaling and values up BCLC staging in predicting OS, which provides evidence for the identification of potential therapeutic targets and the accurate prediction of prognosis for patients with HCC.

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications. Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N). However, prohibitive labor costs cause most institutions to delineate a substantially smaller subset of OARs, neglecting the dose distributions of other OARs. Here, we present an automated and highly effective stratified OAR segmentation (SOARS) system using deep learning that precisely delineates a comprehensive set of 42 H&N OARs. We train SOARS using 176 patients from an internal institution and independently evaluate it on 1327 external patients across six different institutions. It consistently outperforms other state-of-the-art methods by at least 3–5% in Dice score for each institutional evaluation (up to 36% relative distance error reduction). Crucially, multi-user studies demonstrate that 98% of SOARS predictions need only minor or no revisions to achieve clinical acceptance (reducing workloads by 90%). Moreover, segmentation and dosimetric accuracy are within or smaller than the inter-user variation.

This paper conducts an analytical study on the urban-rural coordinated development (URCD) in the Yangtze River Delta urban agglomeration (YRDUA), and uses data from 2000–2015 of 27 central cities to study the spatial and temporal evolution patterns of URCD and to discover the influencing factors and driving forces behind it through PCA, ESDA and spatial regression models. It reveals that URCD of the YRDUA shows an obvious club convergence phenomenon during the research duration. The regions with high-level URCD gather mainly in the central part of the urban agglomeration, while the remaining regions mostly have low-level URCD, reflecting the regional aggregation phenomenon of spatial divergence. At the same time, we split URCD into efficiency and equity: urban-rural efficient development (URED) also exhibits similar spatiotemporal evolution patterns, but the patterns of urban-rural balanced development (URBD) show some variability. Finally, by analyzing the driving forces in major years during 2000–2015, it can be concluded that: (i) In recent years, influencing factors such as government financial input and consumption no longer play the main driving role. (ii) Influencing factors such as industrialization degree, fixed asset investment and foreign investment even limit URCD in some years. The above results also show that the government should redesign at the system level to give full play to the contributing factors depending on the actual state of development in different regions and promote the coordinated development of urban and rural areas. The results of this study show that the idea of measuring URCD from two dimensions of efficiency and equity is practical and feasible, and the spatial econometric model can reveal the spatial distribution heterogeneity and time evolution characteristics of regional development, which can provide useful insights for urban-rural integration development of other countries and regions.