Purpose: Interleukin-8 (IL8) is a chemokine produced by malignant cells of multiple cancer types. It exerts various functions in shaping protumoral vascularization and inflammation/immunity. We evaluated sequential levels of serum IL8 in preclinical tumor models and in patients to assess its ability to estimate tumor burden.Experimental Design: IL8 levels were monitored by sandwich ELISAs in cultured tumor cells supernatants, tumor-xenografted mice serum, and in samples from 126 patients with cancer. We correlated IL8 serum levels with baseline tumor burden and with treatment-induced changes in tumor burden, as well as with prognosis.Results: IL8 concentrations correlated with the number of IL8-producing tumor cells in culture. In xenografted neoplasms, IL8 serum levels rapidly dropped after surgical excision, indicating an accurate correlation with tumor burden. In patients with melanoma (n ¼ 16), renal cell carcinoma (RCC; n ¼ 23), non-small cell lung cancer (NSCLC; n ¼ 21), or hepatocellular carcinoma (HCC; n ¼ 30), serum IL8 concentrations correlated with tumor burden and stage, survival (melanoma, n ¼ 16; RCC, n ¼ 23; HCC, n ¼ 33), and objective responses to therapy, including those to BRAF inhibitors (melanoma, n ¼ 16) and immunomodulatory monoclonal antibodies (melanoma, n ¼ 8). IL8 concentrations in urine (n ¼ 18) were mainly elevated in tumors with direct contact with the urinary tract.Conclusions: IL8 levels correlate with tumor burden in preclinical models and in patients with cancer.

Abstract--This paper presents the design of a low complexity Fuzzy Logic Controller of only 25-rules to be embedded in an Energy Management System for a residential grid-connected microgrid including Renewable Energy Sources and storage capability. The system assumes that neither the renewable generation nor the load demand is controllable. The main goal of the design is to minimize the grid power profile fluctuations while keeping the Battery State of Charge within secure limits. Instead of using forecasting-based methods, the proposed approach uses both the microgrid energy rate-of-change and the battery SOC to increase, decrease or maintain the power delivered/absorbed by the mains. The controller design parameters (membership functions and rule-base) are adjusted to optimize a pre-defined set of quality criteria of the microgrid behavior. A comparison with other proposals seeking the same goal is presented at simulation level, whereas the features of the proposed design are experimentally tested on a real residential microgrid implemented at the Public University of Navarre.Index Terms--Distributed power generation, energy management, fuzzy control, microgrid, renewable energy sources, smart grid. I. INTRODUCTION HE ENVIRONMENTAL and economic benefits related to the reduction of both carbon dioxide emission andThis work was partially supported by the Secretaría Nacional de Educación Superior, Ciencia, Tecnología e Innovación SENESCYT and the Instituto de Fomento al Talento Humano del Ecuador under the grant No. 2013-AR2Q4081. Government of Navarra and the FEDER funds under project "Microgrids in Navarra: design, development and implementation". Spanish Ministry of Economy and Competitiveness under grant DPI2013-42853-R; European Union under the project FP7-308468, "PVCROPS-Photovoltaic Cost reduction, reliability, operational performance, prediction and simulation"; grant RUE CSD2009-00046, Consolider-Ingenio 2010 Programme, Spanish Ministry of Science and Innovation, and the grants DPI2013-41224-P, DPI2012-31580 from the Spanish Ministry of Economy and Knowledge.D. Arcos-Aviles is with the Departamento de Ingeniería Eléctrica y Electrónica, Propagation, Electronic Control and Networking (PROCONET) research group, Universidad de las Fuerzas Armadas ESPE, 171-5-231B Sangolquí, Ecuador (e-mail: dgarcos@espe.edu.ec).J. Pascual, L. Marroyo and P. Sanchis are with the Department of Electrical and Electronic Engineering, Public University of Navarre, Pamplona, Spain (e-mail: juliomaria.pascual@unavarra.es; luisma@unavarra.es; pablo.sanchis@unavarra.es.).F. Guinjoan is with the Department of Electronics Engineering, Escuela Técnica Superior de Ingenieros de Telecomunicación de Barcelona, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain (e-mail: francesc.guinjoan@upc.edu).transmission losses have made distributed renewable generation systems became a competitive solution for future smart grids [1]. In this context, microgrids are considered as the key building blocks of smart grids [2] and have aroused g...

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This paper presents the design of an energy management strategy based on a low complexity Fuzzy Logic Control (FLC) for grid power profile smoothing of a residential grid-connected microgrid including Renewable Energy Sources (RES) and battery Energy Storage System (ESS). The proposed energy management strategy uses generation and demand forecasting to anticipate the future behavior of the microgrid. Accordingly to the microgrid power forecast error and the Battery State-of-Charge (SOC) the proposed strategy performs the suitable control of the grid power. A simulation comparison with previous energy management strategies highlights the advantages of the proposed work minimizing fluctuations and power peaks in the power profile exchanged with the grid while keeping the energy stored in the battery between secure limits. Finally, the experimental validation in a real residential microgrid implemented at Public University of Navarre (UPNa, Spain) demonstrates the proper operation of the proposed strategy achieving a smooth grid power profile and a battery SOC center close to the 75% of the rated battery capacity.Peer ReviewedPostprint (author's final draft

Sunitinib is one of the most widely used targeted therapeutics for renal cell carcinoma (RCC), but acquired resistance against targeted therapies remains a major clinical challenge. To dissect mechanisms of acquired resistance and unravel reliable predictive biomarkers for sunitinib in RCC, we sequenced the exons of 409 tumor‐suppressor genes and oncogenes in paired tumor samples from an RCC patient, obtained at baseline and after development of acquired resistance to sunitinib. From newly arising mutations, we selected, using in silico prediction models, six predicted to be deleterious, located in G6PD, LRP1B, SETD2, TET2, SYNE1, and DCC. Consistently, immunoblotting analysis of lysates derived from sunitinib‐desensitized RCC cells and their parental counterparts showed marked differences in the levels and expression pattern of the proteins encoded by these genes. Our further analysis demonstrates essential roles for these proteins in mediating sunitinib cytotoxicity and shows that their loss of function renders tumor cells resistant to sunitinib in vitro and in vivo. Finally, sunitinib resistance induced by continuous exposure or by inhibition of the six proteins was overcome by treatment with cabozantinib or a low‐dose combination of lenvatinib and everolimus. Collectively, our results unravel novel markers of acquired resistance to sunitinib and clinically relevant approaches for overcoming this resistance in RCC.