Determining Plasma Protein Variation Parameters as a Prerequisite for Biomarker Studies-A TMT-Based LC-MSMS Proteome Investigation.

Specific plasma proteins serve as valuable markers for various diseases and are in many cases routinely measured in clinical laboratories by fully automated systems. For safe diagnostics and monitoring using these markers, it is important to ensure an analytical quality in line with clinical needs. For this purpose, information on the analytical and the biological variation of the measured plasma protein, also in the context of the discovery and validation of novel, disease protein biomarkers, is important, particularly in relation to for sample size calculations in clinical studies.

Deep learning detects and visualizes bleeding events in electronic health records.

Background: Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.

Objectives: To develop a deep learning model that detects and visualizes bleeding events in electronic health records.

[Intravitreal angiostatic treatment of ocular vascular diseases].

Intraocular vascular diseases like neovascular age-related macular degeneration, diabetic macular oedema and retinal vein occlusion are associated with decline in vision if left untreated. Anti-vascular endothelia growth factor (VEGF) agents originally developed for cancer treatment were the beginning of a new treatment regime for intraocular vascular diseases. Ambiguous results exist regarding adverse effects but are considered limited.