Towards near zero energy dwellings by heat pump implementation in HVAC plants

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Targeting of immunosuppressive myeloid cells from glioblastoma patients by modulation of size and surface charge of lipid nanocapsules

Background: Myeloid derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs) are two of the major players involved in the inhibition of anti-tumor immune response in cancer patients, leading to poor prognosis. Selective targeting of myeloid cells has therefore become an attractive therapeutic strategy to relieve immunosuppression and, in this frame, we previously demonstrated that lipid nanocapsules (LNCs) loaded with lauroyl-modified gemcitabine efficiently target monocytic MDSCs in melanoma patients. In this study, we investigated the impact of the physico-chemical characteristics of LNCs, namely size and surface potential, towards immunosuppressive cell targeting. We exploited myeloid cells isolated from glioblastoma patients, which play a relevant role in the immunosuppression, to demonstrate that tailored nanosystems can target not only tumor cells but also tumor-promoting cells, thus constituting an efficient system that could be used to inhibit their function. Results: The incorporation of different LNC formulations with a size of 100 nm, carrying overall positive, neutral or negative charge, was evaluated on leukocytes and tumor-infiltrating cells freshly isolated from glioblastoma patients. We observed that the maximum LNC uptake was obtained in monocytes with neutral 100 nm LNCs, while positively charged 100 nm LNCs were more effective on macrophages and tumor cells, maintaining at low level the incorporation by T cells. The mechanism of uptake was elucidated, demonstrating that LNCs are incorporated mainly by caveolae-mediated endocytosis. Conclusions: We demonstrated that LNCs can be directed towards immunosuppressive cells by simply modulating their size and charge thus providing a novel approach to exploit nanosystems for anticancer treatment in the frame of immunotherapy.[Figure not available: see fulltext.

A Guide for the Design of Benchmark Environments for Building Energy Optimization

The need for algorithms that optimize building energy consumption is usually motivated with the high energy consumption of buildings on a global scale. However, the current practice for evaluating the performance of such algorithms does not reflect this goal, as in most cases the performance is reported for one specific simulated building only, which provides no indication about the generalization of the score on other buildings. One approach to overcome this severe issue is to establish a shared collection of environments, each representing one simulated building setup, that would enable researchers to systematically compare and contrast the efficacy of their building optimization algorithms at scale. However, this requires that the individual environments are well designed for this goal. This paper is thus targeting the design of suitable environments for such a collection based on a detailed analysis of related publications that allows the identification of relevant characteristics for suitable environments. Based on this analysis a guide is developed that distills these characteristics into questions, intended to support a discussion of relevant topics during the design of such environments. Additional explanations and examples are provided for each question to make the guide more comprehensible. Finally, it is demonstrated how the guide can be applied, by utilizing it for the design of a novel environment, which represents an office building in tropical climate. This environment is released open source alongside this publication. We also indicate how test scenarios from existing publications could be enhanced to comply with the required characteristics according to our guide, underlining its importance for the future development and evaluation of building energy optimization algorithms, and thus for the sustainability of buildings in general