ProtGraph: a tool for the quick and comprehensive exploration and exploitation of the peptide search space derived from protein sequence databases using graphs.

Due to computational resource limitations, in mass spectrometry based proteomics only a limited set of peptide sequences is used for the matching against measured spectra. We present an approach to represent proteins by graphs and allow not only the canonical sequences but also known isoforms and annotated amino acid variations, e.g.

Employing artificial intelligence for optimising antibiotic dosages in sepsis on intensive care unit: a study protocol for a prospective observational study (KI.SEP).

Introduction: In sepsis treatment, achieving and maintaining effective antibiotic therapy is crucial. However, optimal antibiotic dosing faces challenges due to significant variability among patients with sepsis. Therapeutic drug monitoring (TDM), the current gold standard, lacks initial dosage adjustments and global availability.

A Review of Protein Inference.

Protein inference is an often neglected though crucial step in most proteomic experiments. In the bottom-up proteomic approach, the actual molecules of interest, the proteins, are digested into peptides before measurement on a mass spectrometer. This approach introduces a loss of information: The actual proteins must be inferred based on the identified peptides.

Activation of the MAPK network provides a survival advantage during the course of COVID-19-induced sepsis: a real-world evidence analysis of a multicenter COVID-19 Sepsis Cohort.

Purpose: There is evidence that lower activity of the RAF/MEK/ERK network is associated with positive outcomes in mild and moderate courses of COVID-19. The effect of this cascade in COVID-19 sepsis is still undetermined. Therefore, we tested the hypothesis that activity of the RAF/MEK/ERK network in COVID-19-induced sepsis is associated with an impact on 30-day survival.