B7-H4, a member of B7 family, is a transmembrane protein and inhibits T-cells immunity. However, in a variety of tumor cells, B7-H4 was detected predominantly in intracellular compartments with unknown mechanism and functions. In this study, we analyzed B7-H4 expression and subcellular distribution by immunohistochemistry in renal cell carcinoma (RCC) tissues. B7-H4 protein was detected on the membrane, in the cytosol and/or in the nucleus in tumor tissues. The membrane and nuclear expression of B7-H4 was significantly correlated with the tumor stages of RCC. Moreover, the membrane localization of B7-H4 was inversely correlated with the intensity of tumor infiltrates lymphocyte (TILs), whereas no association was observed between nuclear expression of B7-H4 and the density of TILs status. We further identified that B7-H4 is a cytoplasmic-nuclear shuttling protein containing a functional nuclear localization sequence (NLS) motif. A point mutation of B7-H4 NLS motif blocked the leptomycin B -induced nuclear accumulation of B7-H4. HEK293 cells stably expressing B7-H4 NLS mutant exhibited more potent inhibition in T-cell proliferation and cytokine production through increasing its surface expression compared with wild-type B7-H4 transfected cells owing to their increased surface expression. Most importantly, overexpression of wild-type B7-H4 in HEK293 cells enhanced tumor cell proliferation in vitro and tumorigenicity in vivo, promoted G1/S phase transition. The regulation of cell cycle by wild-type B7-H4 was partialy due to upregulation of Cyclin D 1 and Cyclin E. A mutation of B7-H4 NLS motif abolished the B7-H4-mediated cell proliferation and cell cycle regulation. Furthermore, B7-H4 wild-type confers chemoresistance activity to RCC cell lines including Caki-1 and ACHN. Our study provides a new insight into the functional implication of B7-H4 in its subcellular localization.

• Clinical features can predict lung metastasis of colorectal cancer patients. • Radiomics analysis outperformed clinical features in assessing the risk of pulmonary metastasis. • A clinical-radiomics nomogram can help clinicians predict lung metastasis in colorectal cancer patients.

Organic electrode materials have attracted widespread attention as alternative candidates for lithium-ion batteries due to their potential for sustainable production, wide source, low cost, and adjustability. Herein, we develop a dihydrophenazine-based multielectron redox center to promote the energy and power density of organic batteries. The poly­(1,3,5-tris­(10-(4-vinylphenyl)­phenazin-5­(10H)-yl)­benzene) (p-TPZB)-based battery shows a specific discharge capacity of 155 mAh g–1 with a discharge voltage of 3.1–4.2 V (vs Li+/Li) initially. Until the 2000th cycle, the specific discharge capacity is still maintained up to 138 mAh g–1, with an excellent capacity retention rate of ca. 89% at 2C. Meanwhile, the p-TPZB|Li cell delivers outstanding power density and energy density up to 4320 W kg–1 and 522 Wh kg–1, respectively. Moreover, p-TPZB also has shown potential as an active material for sodium-ion batteries (SIBs). Our study provides a structure and strategy to improve the capacity and density of next-generation high-performance lithium/sodium-ion batteries.

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Background:The purpose of this study is to develop a radiomics approach to predict brain metastasis (BM) for stage III/IV ALK-positive non-small cell lung cancer (NSCLC) patients. Methods: Patients with ALK-positive III/IV NSCLC from 2014 to 2017 were enrolled retrospectively.Their pretreatment thoracic CT images were collected, and the gross tumor volume (GTV) was defined by two experienced radiation oncologists. An in-house feature extraction code-set was performed based on MATLAB 2015b (Mathworks, Natick, MA, USA) in patients' CT images to extract features. Patients were randomly divided into training set and test set (4:1) by using createDataPartition function in caret package.A test-retest in RIDER NSCLC dataset was performed to identify stable radiomics features. LASSO Cox regression and a leave-one-out cross-validation were conducted to identify optimal features for the logistic regression model to evaluate the predictive value of radiomics feature(s) for BM. Furthermore, extended validation for the radiomics feature(s) and Cox regression analyses which combined radiomics feature(s) and treatment elements were implemented to predict the risk of BM during follow-up. Results: In total, 132 patients were included, among which 27 patients had pretreatment BM. The median follow-up time was 11.8 (range, 0.1-65.2) months. In the training set, one radiomics feature (W_GLCM_ LH_Correlation) showed discrimination ability of BM (P value =0.014, AUC =0.687, 95% CI: 0.551-0.824, specificity =83.5%, sensitivity =57.1%). It also exhibited reposeful performance in the test set (AUC =0.642, 95% CI: 0.501-0.783, specificity =60.0%, sensitivity =83.3%). Those 105 patients without pretreatment BM were divided into stage III (n=57) and stage IV (n=48) groups. The radiomics feature (W_GLCM_LH_ Correlation) had moderate performance to predict BM during/after treatment in separate groups (stage III: