How to find the right balance
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Light therapy is increasingly administered and studied as a non-pharmacologic treatment for a variety of healthrelated problems, including treatment of people with dementia. Light therapy comes in a variety of ways, ranging from being exposed to daylight, to being exposed to light emitted by light boxes and ambient bright light. Light therapy is an area in medicine where medical sciences meet the realms of physics, engineering and technology. Therefore, it is paramount that attention is paid in the methodology of studies to the technical aspects in their full breadth. This paper provides an extensive introduction for non-technical researchers on how to describe and adjust their methodology when involved in lighting therapy research. A specific focus in this manuscript is on ambient bright light, as it is an emerging field within the domain of light therapy. The paper deals with how to (i) describe the lighting equipment, (ii) describe the light measurements, (iii) describe the building and interaction with daylight. Moreover, attention is paid to the uncertainty in standards and guidelines regarding light and lighting for older adults.
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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