RESUMO
The 4th Davos Declaration was developed during the Global Allergy Forum in Davos which aimed to elevate the care of patients with atopic dermatitis (AD) by uniting experts and stakeholders. The forum addressed the high prevalence of AD, with a strategic focus on advancing research, treatment, and management to meet the evolving challenges in the field. This multidisciplinary forum brought together top leaders from research, clinical practice, policy, and patient advocacy to discuss the critical aspects of AD, including neuroimmunology, environmental factors, comorbidities, and breakthroughs in prevention, diagnosis, and treatment. The discussions were geared towards fostering a collaborative approach to integrate these advancements into practical, patient-centric care. The forum underlined the mounting burden of AD, attributing it to significant environmental and lifestyle changes. It acknowledged the progress in understanding AD and in developing targeted therapies but recognized a gap in translating these innovations into clinical practice. Emphasis was placed on the need for enhanced awareness, education, and stakeholder engagement to address this gap effectively and to consider environmental and lifestyle factors in a comprehensive disease management strategy. The 4th Davos Declaration marks a significant milestone in the journey to improve care for people with AD. By promoting a holistic approach that combines research, education, and clinical application, the Forum sets a roadmap for stakeholders to collaborate to improve patient outcomes in AD, reflecting a commitment to adapt and respond to the dynamic challenges of AD in a changing world.
RESUMO
Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.