ABSTRACT
BACKGROUND: Targeting interleukin (IL)-6 has become a major therapeutic strategy in the treatment of immune-mediated inflammatory disease. Interference with the IL-6 pathway can be directed at the specific receptor using anti-IL-6Rα antibodies or by directly inhibiting the IL-6 cytokine. This paper is an update of a previous consensus document, based on most recent evidence and expert opinion, that aims to inform on the medical use of interfering with the IL-6 pathway. METHODS: A systematic literature research was performed that focused on IL-6-pathway inhibitors in inflammatory diseases. Evidence was put in context by a large group of international experts and patients in a subsequent consensus process. All were involved in formulating the consensus statements, and in the preparation of this document. RESULTS: The consensus process covered relevant aspects of dosing and populations for different indications of IL-6 pathway inhibitors that are approved across the world, including rheumatoid arthritis, polyarticular-course and systemic juvenile idiopathic arthritis, giant cell arteritis, Takayasu arteritis, adult-onset Still's disease, Castleman's disease, chimeric antigen receptor-T-cell-induced cytokine release syndrome, neuromyelitis optica spectrum disorder and severe COVID-19. Also addressed were other clinical aspects of the use of IL-6 pathway inhibitors, including pretreatment screening, safety, contraindications and monitoring. CONCLUSIONS: The document provides a comprehensive consensus on the use of IL-6 inhibition to treat inflammatory disorders to inform healthcare professionals (including researchers), patients, administrators and payers.
ABSTRACT
The recent COVID-19 pandemic affected various aspects of life. Several studies established the consequences of pandemic lockdown on air quality using satellite remote sensing. However, such studies have limitations, including low spatial resolution or incomplete spatial coverage. Therefore, in this paper, we propose a machine learning-based scheme to solve the pre-mentioned limitations by training an optimized space-time extra trees model for each year of the study period. The results have shown that our trained models reach a prediction accuracy up to 95% when predicting the missing values in the MODIS MCD19A2 Aerosol Optical Depth (AOD) product. The outcome of the mentioned scheme was a geo-harmonized atmospheric dataset for aerosol optical depth at 550 nm with 1 km spatial resolution and full coverage over Europe. As an application, we used the proposed machine learning based prediction approach in AOD levels analysis. We compared the mean AOD levels between the lockdown period from March to June in 2020 and the mean AOD values of the same period for the past 5 years. We found that AOD levels dropped over most European countries in 2020 but increased in several eastern and western countries. The Netherlands had the most significant average decrease in AOD levels (19%), while Spain had the highest average increase (10%). Moreover, we analyzed the relationship between the relative percentage difference of AOD and four meteorological variables. We found a positive correlation between AOD and relative humidity and a negative correlation between AOD and wind speed. The value of the proposed prediction scheme is further emphasized by taking into consideration that the reconstructed dataset can be used for future air quality studies concerning Europe.