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This article provides details on the development of a statistical learning algorithm developed for constructing personalized treatment plans for psychotherapy. The algorithm takes data collected via Ecological Momentary Assessment (EMA) as an input. From this, it constructs an idiographic disorder model that reflects the latent dimensions of this patient’s psychopathology and their temporal interrelations. The priority of individual problems is derived from this statistical model. Based on this, treatment modules from cognitive-behavioral therapy are ranked so that the problems with the highest priority are dealt with first. A case study is used to illustrate the different analysis steps of the algorithm from data collection to the treatment plan.
: Platelets are components of the blood that are highly reactive, and they quickly respond
to multiple physiological and pathophysiological processes. In the last decade, it became clear that
platelets are the key components of circulation, linking hemostasis, innate, and acquired immunity.
Protein composition, localization, and activity are crucial for platelet function and regulation. The
current state of mass spectrometry-based proteomics has tremendous potential to identify and quantify thousands of proteins from a minimal amount of material, unravel multiple post-translational
modifications, and monitor platelet activity during drug treatments. This review focuses on the role
of proteomics in understanding the molecular basics of the classical and newly emerging functions
of platelets. including the recently described role of platelets in immunology and the development
of COVID-19.The state-of-the-art proteomic technologies and their application in studying platelet
biogenesis, signaling, and storage are described, and the potential of newly appeared trapped ion
mobility spectrometry (TIMS) is highlighted. Additionally, implementing proteomic methods in
platelet transfusion medicine, and as a diagnostic and prognostic tool, is discussed.