6608963Recent advances in computational materials science present novel opportunities for structure discovery and optimization, including uncovering of unsuspected compounds and metastable structures, electronic structure, surface and nano-particle properties. The practical realization of these opportunities requires systematic generation and classification of the relevant computational data by high-throughput methods. In this paper we present Aflow (Automatic Flow), a software framework for high-throughput calculation of crystal structure properties of alloys, intermetallics and inorganic compounds. The Aflow software is available for the scientific community on the website of the materials research consortium, aflowlib.org. Its geometric and electronic structure analysis and manipulation tools are additionally available for online operation at the same website. The combination of automatic methods and user online interfaces provide a powerful tool for efficient quantum computational materials discovery and characterization.

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The lattice thermal conductivity (κ ω ) is a key property for many potential applications of compounds. Discovery of materials with very low or high κ ω remains an experimental challenge due to high costs and time-consuming synthesis procedures. High-throughput computational prescreening is a valuable approach for significantly reducing the set of candidate compounds. In this article, we introduce efficient methods for reliably estimating the bulk κ ω for a large number of compounds. The algorithms are based on a combination of machine-learning algorithms, physical insights, and automatic ab initio calculations. We scanned approximately 79,000 half-Heusler entries in the AFLOWLIB.org database. Among the 450 mechanically stable ordered semiconductors identified, we find that κ ω spans more than 2 orders of magnitude-a much larger range than that previously thought. κ ω is lowest for compounds whose elements in equivalent positions have large atomic radii. We then perform a thorough screening of thermodynamical stability that allows us to reduce the list to 75 systems. We then provide a quantitative estimate of κ ω for this selected range of systems. Three semiconductors having κ ω < 5 Wm −1 K −1 are proposed for further experimental study.

Topological insulators (TI) are becoming one of the most studied classes of novel materials because of their great potential for applications ranging from spintronics to quantum computers. To fully integrate TI materials in electronic devices, high-quality epitaxial single-crystalline phases with sufficiently large bulk bandgaps are necessary. Current efforts have relied mostly on costly and time-consuming trial-and-error procedures. Here we show that by defining a reliable and accessible descriptor , which represents the topological robustness or feasibility of the candidate, and by searching the quantum materials repository aflowlib.org, we have automatically discovered 28 TIs (some of them already known) in five different symmetry families. These include peculiar ternary halides, Cs{Sn,Pb,Ge}{Cl,Br,I}(3), which could have been hardly anticipated without high-throughput means. Our search model, by relying on the significance of repositories in materials development, opens new avenues for the discovery of more TIs in different and unexplored classes of systems.

Several thousand compounds from the Inorganic Crystal Structure Database have been considered as nanograined, sintered-powder thermoelectrics with the high-throughput ab-initio AFLOW framework. Regression analysis unveils that the power factor is positively correlated with both the electronic band gap and the carrier effective mass, and that the probability of having large thermoelectric power factors increases with the increasing number of atoms per primitive cell. Avenues for further investigation are revealed by this work. These avenues include the role of experimental and theoretical databases in the development of novel materials.

The cure rate of osteosarcoma has not improved in the past 30 years. The search for new treatments and drugs is urgently needed. Apatinib is a high selectivity inhibitor of vascular endothelial growth factor receptor-2 (VEGFR2) tyrosine kinase, exerting promising antitumoral effect in various tumors. The antitumor effect of Apatinib in human osteosarcoma has never been reported. We investigated the effects of Apatinib in osteosarcoma in vitro and in vivo. Osteosarcoma patients with high levels of VEGFR2 have poor prognosis. Apatinib can inhibit cell growth of osteosarcoma cells. In addition to cycle arrest and apoptosis, Apatinib induces autophagy. Interestingly, inhibition of autophagy increased Apatinib-induced apoptosis in osteosarcoma cells. Immunoprecipitation confirmed direct binding between VEGFR2 and signal transducer and activator of transcription 3 (STAT3). Downregulation of VEGFR2 by siRNA resulted in STAT3 inhibition in KHOS cells. VEGFR2 and STAT3 are inhibited by Apatinib in KHOS cells, and STAT3 act downstream of VEGFR2. STAT3 and BCL-2 were downregulated by Apatinib. STAT3 knockdown by siRNA reinforced autophagy and apoptosis induced by Apatinib. BCL-2 inhibits autophagy and was apoptosis restrained by Apatinib too. Overexpression of BCL-2 decreased Apatinib-induced apoptosis and autophagy. Apatinib repressed the expression of STAT3 and BCL-2 and suppressed the growth of osteosarcoma in vivo. To sum up, deactivation of VEGFR2/STAT3/BCL-2 signal pathway leads to Apatinib-induced growth inhibition of osteosarcoma.

BackgroundImmune checkpoint inhibitors have led to a breakthrough in solid tumor immunotherapy, but related studies on musculoskeletal tumors are few, especially for PD-L2.MethodsWe examined expression of three molecular effectors of the PD-1 axis in 234 patients with musculoskeletal tumors, including osteosarcoma, chondrosarcoma, synovial sarcoma, and giant cell tumor. Survival analyses and potential mechanisms were investigated in osteosarcoma per the Gene Expression Omnibus (GEO) and immunohistochemistry analyses. In vivo, humanized mice were used to evaluate the effect of nivolumab on osteosarcoma.ResultsPD-L1, PD-L2, and PD-1 expression levels were significantly different between the histologic types of the musculoskeletal tumors. For osteosarcoma, PD-L1 was negatively correlated with prognosis, while PD-1 had a negative correlation tendency with overall survival (OS). Meanwhile, PD-L2 had a positive correlation trend with OS. Nivolumab inhibited osteosarcoma metastasis in humanized mice by increasing CD4+ and CD8+ lymphocytes and the cytolytic activity of CD8 lymphocytes in the lung but did not affect primary osteosarcoma growth.ConclusionWe systematically detected the expression patterns of PD-L1, PD-L2, and PD-1 in musculoskeletal tumors for the first time and demonstrated the prognostic roles and underlying mechanisms of PD-1 axis in osteosarcoma. Furthermore, PD-1 blockade could effectively control osteosarcoma pulmonary metastasis in vivo. Therefore, the PD-1 axis may be a potential immunotherapeutic target for metastatic osteosarcoma.Electronic supplementary materialThe online version of this article (10.1186/s13045-018-0560-1) contains supplementary material, which is available to authorized users.

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