Middle-throughput LC-MS-based platelet proteomics with minute sample amounts using semi-automated positive pressure FASP in 384-well format (PF384).

Comprehensive characterization of platelets requires various functional assays and analysis techniques, including omics-disciplines, each requiring an individual aliquot of a given sample. Consequently, the sample material per assay is often highly limited rendering downscaling a prerequisite for effective sample exploitation. Here we present a transfer of our recently introduced 96-well-based proteomics workflow (PF96) into the 384-well format (PF384) allowing for a significant increase in sensitivity when processing minute platelet protein amounts.

Functionally distinct anticoagulant mechanisms of endothelial cells.

Antithrombin and tissue factor pathway inhibitor (TFPI) provide different anticoagulant mechanisms. Having established a potent anticoagulant role of cultured human umbilical vein endothelial cells in vessel-on-a-chip microfluidic models, we now investigated how these cells modulated thrombin generation under stasis through antithrombin and TFPI pathways. We observed that endothelial monolayers in 96 well-plates strongly delayed and suppressed the thrombin generation process induced by tissue factor, regardless of the presence of whole blood, platelet-rich plasma or platelet-free plasma.

Suppressed ORAI1-STIM1-dependent Ca entry by protein kinase C isoforms regulating platelet procoagulant activity.

Agonist-induced rises in cytosolic Ca control most platelet responses in thrombosis and hemostasis. In human platelets, we earlier demonstrated that the ORAI1-STIM1 pathway is a major component of extracellular Ca entry, in particular when induced via the ITAM-linked collagen receptor, glycoprotein VI (GPVI). In the present article, using functionally defective platelets from patients with a loss-of-function mutation in ORAI1 or STIM1, we show that Ca entry induced by the endoplasmic reticulum ATPase inhibitor, thapsigargin, fully relies on this pathway.

Advancements in supervised deep learning for metal artifact reduction in computed tomography: A systematic review.

Background: Metallic artefacts caused by metal implants, are a common problem in computed tomography (CT) imaging, degrading image quality and diagnostic accuracy. With advancements in artificial intelligence, novel deep learning (DL)-based metal artefact reduction (MAR) algorithms are entering clinical practice.

Objective: This systematic review provides an overview of the performance of the current supervised DL-based MAR algorithms for CT, focusing on three different domains: sinogram, image, and dual domain.