Objective: To identify preliminary core sets of outcome variables for disease activity and damage assessment in juvenile systemic lupus erythematosus (JSLE) and juvenile dermatomyositis (JDM).
Methods: Two questionnaire surveys were mailed to 267 physicians from 46 different countries asking each member to select and rank the response variables used when assessing clinical response in patients with JSLE or JDM. Next, 40 paediatric rheumatologists from 34 countries met and, using the nominal group technique, selected the domains to be included in the disease activity and damage core sets for JSLE and JDM.
Results: A total of 41 response variables for JSLE and 37 response variables for JDM were selected and ranked through the questionnaire surveys. In the consensus conference, domains selected for both JSLE and JDM activity or damage core sets included the physician and parent/patient subjective assessments and a global score tool. Domains specific for JSLE activity were the immunological tests and the kidney function parameters. Concerning JDM, functional ability and muscle strength assessments were indicated for both activity and damage core sets, whereas serum muscle enzymes were included only in the activity core set. A specific paediatric domain called 'growth and development' was introduced in the disease damage core set for both diseases and the evaluation of health-related quality of life was advised in order to capture the influence of the disease on the patient lifestyle.
Conclusions: We developed preliminary core sets of measures for disease activity and damage assessment in JSLE and JDM. The prospective validation of the core sets is in progress.
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http://dx.doi.org/10.1093/rheumatology/keg403 | DOI Listing |
Acc Chem Res
January 2025
Chongqing Key Laboratory of Natural Product Synthesis and Drug Research, Innovative Drug Research Center, School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
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January 2025
School of Materials and Energy, Lanzhou University (LZU), Lanzhou 730000, China.
Complementary neural network circuits combining multifunctional high-performance p-type with n-type organic artificial synapses satisfy sophisticated applications such as image cognition and prosthesis control. However, implementing the dual-modal memory features that are both volatile and nonvolatile in a synaptic transistor is challenging. Herein, for the first time, we propose a single vertical n-type organic synaptic transistor (VNOST) with a novel polymeric organic mixed ionic-electronic conductor as the core channel material to achieve dual-modal synaptic learning/memory behaviors at different operating current densities via the formation of an electric double layer and the reversible ion doping.
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January 2025
Department of Chemistry, Southern Methodist University, Dallas, Texas 75275, USA.
Reliable computational methodologies and basis sets for modeling x-ray spectra are essential for extracting and interpreting electronic and structural information from experimental x-ray spectra. In particular, the trade-off between numerical accuracy and computational cost due to the size of the basis set is a major challenge, since molecular orbitals undergo extreme relaxation in the core-hole state. To gain clarity on the changes in electronic structure induced by the formation of a core-hole, the use of sufficiently flexible basis for expanding the orbitals, particularly for the core region, has been shown to be essential.
View Article and Find Full Text PDFHealth Res Policy Syst
January 2025
University College London, London, United Kingdom.
Background: The deteriorating mental health of children and young people in the United Kingdom poses a challenge that services and policy makers have found difficult to tackle. Kailo responds to this issue with a community-based participatory and systemically informed strategy, perceiving mental health and well-being as a dynamic state shaped by the interplay of broader health determinants. The initiative works to explore, define and implement locally relevant solutions to challenges shaping the mental health and well-being of young people.
View Article and Find Full Text PDFMayo Clin Proc Digit Health
December 2024
Department Radiology, Stanford University, Stanford, CA.
Artificial intelligence (AI) and machine learning (ML) are driving innovation in biosciences and are already affecting key elements of medical scholarship and clinical care. Many schools of medicine are capitalizing on the promise of these new technologies by establishing academic units to catalyze and grow research and innovation in AI/ML. At Stanford University, we have developed a successful model for an AI/ML research center with support from academic leaders, clinical departments, extramural grants, and industry partners.
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