Shear-Sensing by C-Reactive Protein: Linking Aortic Stenosis and Inflammation.

Background: CRP (C-reactive protein) is a prototypical acute phase reactant. Upon dissociation of the pentameric isoform (pCRP [pentameric CRP]) into its monomeric subunits (mCRP [monomeric CRP]), it exhibits prothrombotic and proinflammatory activity. Pathophysiological shear rates as observed in aortic valve stenosis (AS) can influence protein conformation and function as observed with vWF (von Willebrand factor).

Platelet-targeted thromboprophylaxis with a human serum albumin fusion drug: Preventing thrombosis and reducing cardiac ischemia/reperfusion injurywithout bleeding complications.

Myocardial infarction (MI) as a consequence of atherosclerosis-associated acute thrombosis is a leading cause of death and disability globally. Antiplatelet and anticoagulant drugs are standard therapies in preventing and treating MI. However, all clinically used drugs are associated with bleeding complications, which ultimately limits their use in patients with a high risk of bleeding.

A novel phosphocholine-mimetic inhibits a pro-inflammatory conformational change in C-reactive protein.

C-reactive protein (CRP) is an early-stage acute phase protein and highly upregulated in response to inflammatory reactions. We recently identified a novel mechanism that leads to a conformational change from the native, functionally relatively inert, pentameric CRP (pCRP) structure to a pentameric CRP intermediate (pCRP*) and ultimately to the monomeric CRP (mCRP) form, both exhibiting highly pro-inflammatory effects. This transition in the inflammatory profile of CRP is mediated by binding of pCRP to activated/damaged cell membranes via exposed phosphocholine lipid head groups.

In vivo fluorescence imaging: success in preclinical imaging paves the way for clinical applications.

Advances in diagnostic imaging have provided unprecedented opportunities to detect diseases at early stages and with high reliability. Diagnostic imaging is also crucial to monitoring the progress or remission of disease and thus is often the central basis of therapeutic decision-making. Currently, several diagnostic imaging modalities (computed tomography, magnetic resonance imaging, and positron emission tomography, among others) are routinely used in clinics and present their own advantages and limitations.

Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder.

Feature extraction algorithms are an important class of unsupervised methods used to reduce data dimensionality. They have been applied extensively for time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging─commonly, matrix factorization (MF) techniques such as principal component analysis have been used. A limitation of MF is the assumption of linearity, which is generally not accurate for ToF-SIMS data.