Wind and solar power generation will continue to grow in the energy supply of the future, but its inherent variability (intermittency) requires appropriate energy systems for storing and using power. Storage of possibly temporary excess of power as methane from hydrogen gas and carbon dioxide is a promising option. With electrolysis hydrogen gas can be generated from (renewable) power. The combination of such hydrogen with carbon dioxide results in the energy carrier methane that can be handled well and may may serve as carbon feedstock of the future. Biogas from biomass delivers both methane and carbon dioxide. Anaerobic microorganisms can make additional methane from hydrogen and carbon dioxide in a biomethanation process that compares favourably with its chemical counterpart. Biomethanation for renewable power storage and use makes appropriate use of the existing infrastructure and knowledge base for natural gas. Addition of hydrogen to a dedicated biogas reactor after fermentation optimizes the biomethanation conditions and gives maximum flexibility. The low water solubility of hydrogen gas limits the methane production rate. The use of hollow fibers, nano-bubbles or better-tailored methane-forming microorganisms may overcome this bottleneck. Analyses of patent applications on biomethanation suggest a lot of freedom to operate. Assessment of biomethanation for economic feasibility and environmental value is extremely challenging and will require future data and experiences. Currently biomethanation is not yet economically feasible, but this may be different in the energy systems of the near future.
DOCUMENT
In recent years, the effects of the physical environment on the healing process and well-being have proved to be increasingly relevant for patients and their families (PF) as well as for healthcare staff. The discussions focus on traditional and institutionally designed healthcare facilities (HCF) relative to the actual well-being of patients as an indicator of their health and recovery. This review investigates and structures the scientific research on an evidence-based healthcare design for PF and staff outcomes. Evidence-based design has become the theoretical concept for what are called healing environments. The results show the effects on PF and staff from the perspective of various aspects and dimensions of the physical environmental factors of HFC. A total of 798 papers were identified that fitted the inclusion criteria for this study. Of these, 65 articles were selected for review: fewer than 50% of these papers were classified with a high level of evidence, and 86% were included in the group of PF outcomes. This study demonstrates that evidence of staff outcomes is scarce and insufficiently substantiated. With the development of a more customer-oriented management approach to HCF, the implications of this review are relevant to the design and construction of HCF. Some design features to consider in future design and construction of HCF are single-patient rooms, identical rooms, and lighting. For future research, the main challenge will be to explore and specify staff needs and to integrate those needs into the built environment of HCF.
DOCUMENT
Psoriasis (Pso) is a chronic inflammatory skin disease, and up to 30% of Pso patients develop psoriatic arthritis (PsA), which can lead to irreversible joint damage. Early detection of PsA in Pso patients is crucial for timely treatment but difficult for dermatologists to implement. We, therefore, aimed to find disease-specific immune profiles, discriminating Pso from PsA patients, possibly facilitating the correct identification of Pso patients in need of referral to a rheumatology clinic. The phenotypes of peripheral blood immune cells of consecutive Pso and PsA patients were analyzed, and disease-specific immune profiles were identified via a machine learning approach. This approach resulted in a random forest classification model capable of distinguishing PsA from Pso (mean AUC = 0.95). Key PsA-classifying cell subsets selected included increased proportions ofdifferentiated CD4+CD196+CD183-CD194+ and CD4+CD196-CD183-CD194+ T-cells and reduced proportions of CD196+ and CD197+ monocytes, memory CD4+ and CD8+ T-cell subsets and CD4+ regulatory T-cells. Within PsA, joint scores showed an association with memory CD8+CD45RACD197- effector T-cells and CD197+ monocytes. To conclude, through the integration of in-depth flow cytometry and machine learning, we identified an immune cell profile discriminating PsA from Pso. This immune profile may aid in timely diagnosing PsA in Pso.
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